Daily Papers

Understanding the co-evolution of super-massive black holes (SMBHs) and their host galaxies remains a key challenge of extragalactic astrophysics, particularly the earliest stages at high-redshift. However, studying SMBHs at high-redshift with cosmological simulations, is challenging due to the large volumes and high-resolution required. Through its innovative simulation strategy, the First Light And Reionisation Epoch Simulations (FLARES) suite of cosmological hydrodynamical zoom simulations allows us to simulate a much wider range of environments which contain SMBHs with masses extending to M_{bullet}>10^{9} M_{odot} at z=5. In this paper, we use FLARES to study the physical properties of SMBHs and their hosts in the early Universe (5le, z le10). FLARES predicts a sharply declining density with increasing redshift, decreasing by a factor of 100 over the range z=5to 10. Comparison between our predicted bolometric luminosity function and pre-JWST observations yield a good match. However, recent JWST observations appear to suggest a larger contribution of SMBHs than previously observed, or predicted by FLARES. Finally, by using a re-simulation with AGN feedback disabled, we explore the impact of AGN feedback on their host galaxies. This reveals that AGN feedback results in a reduction of star formation activity, even at z>5, but only in the most massive galaxies. A deeper analysis reveals that AGN are also the cause of suppressed star formation in passive galaxies but that the presence of an AGN doesn't necessarily result in the suppression of star formation.

One of the most remarkable results from the James Webb Space Telescope has been the discovery of a large population of compact sources exhibiting strong broad Halpha emission, typically interpreted to be low-luminosity broad-line (Type 1) active galactic nuclei (BLAGN). An important question is whether these observations are in tension with galaxy formation models, and if so how? While comparisons have been made using physical properties (i.e.~black hole mass and accretion rate) inferred from observations, these require the use of SED modelling assumptions, or locally inferred scaling relations, which may be unjustified, at least in the distant high-redshift Universe. In this work we take an alternative approach and forward model predictions from the First Light And Reionisation Epoch Simulations (FLARES) suite of cosmological hydrodynamical zoom simulations to predict the observable properties of BLAGN. We achieve this by first coupling \flares\ with the \qsosed\ model to predict the ionising photon luminosities of high-redshift (z>5) AGN. To model the observed broad Halpha emission we then assume a constant conversion factor and covering fraction, and the fraction of AGN that have observable broad-lines. With a reasonable choice of these parameters, \flares\ is able to reproduce observational constraints on the Halpha luminosity function and equivalent width distribution at z=5.

The upcoming space-based gravitational wave (GW) observatory, LISA, is expected to detect GW signals from supermassive black hole (SMBH) mergers occurring at high redshifts. However, understanding the origin and growth of SMBHs in the early Universe remains an open problem in astrophysics. In this work, we utilize the First Light And Reionization Epoch Simulations (FLARES), a suite of cosmological hydrodynamical zoom-in simulations, to study SMBH mergers at 5 lesssim z lesssim 10 across a wide range of environments. Most mergers in FLARES involve secondary SMBHs near the seed mass (m_{seed} approx 1.5 times 10^{5} M_{odot}) while primary SMBHs span up to 10^{9} M_{odot}, resulting in mass ratios from q sim 10^{-4} to 1, with a peak at q sim 1. The number of mergers increases rapidly towards lower redshifts, and the comoving total number density scales with overdensity as n_{merger} = 10^{-3.80} (1 + delta)^{4.56}. Denser regions host more massive mergers, with higher merger redshifts and lower mass ratios. Within the FLARES redshift range, LISA is expected to detect mergers with 10^{5} lesssim M_{tot} / M_{odot} lesssim 10^{8} and q gtrsim 10^{-2}, corresponding to a detection rate of 0.030 yr^{-1} for events with signal-to-noise ratio SNR geq 10. Our study demonstrates the sensitivity of GW predictions at high redshifts to SMBH seed models and merger time delays, highlighting the need for improved modeling in future cosmological simulations to maximize LISA's scientific return.

The radial gradient of gas-phase metallicity is a powerful probe of the chemical and structural evolution of star-forming galaxies, closely tied to disk formation and gas kinematics in the early universe. We present spatially resolved chemical and dynamical properties for a sample of 25 galaxies at 0.5 lesssim z lesssim 1.7 from the \msasd survey. These innovative observations provide 3D spectroscopy of galaxies at a spatial resolution approaching JWST's diffraction limit and a high spectral resolution of Rsimeq2700. The metallicity gradients measured in our galaxy sample range from -0.03 to 0.02 dex~kpc^{-1}. Most galaxies exhibit negative or flat radial gradients, indicating lower metallicity in the outskirts or uniform metallicity throughout the entire galaxy. We confirm a tight relationship between stellar mass and metallicity gradient at zsim1 with small intrinsic scatter of 0.02 dex~kpc^{-1}. Our results indicate that metallicity gradients become increasingly negative as stellar mass increases, likely because the more massive galaxies tend to be more ``disky". This relationship is consistent with the predictions from cosmological hydrodynamic zoom-in simulations with strong stellar feedback. This work presents the effort to harness the multiplexing capability of JWST NIRSpec/MSA in slit-stepping mode to map the chemical and kinematic profiles of high-redshift galaxies in large samples and at high spatial and spectral resolution.

Understanding the galaxy-halo relationship is not only key for elucidating the interplay between baryonic and dark matter, it is essential for creating large mock galaxy catalogues from N-body simulations. High-resolution hydrodynamical simulations are limited to small volumes by their large computational demands, hindering their use for comparisons with wide-field observational surveys. We overcome this limitation by using the First Light and Reionisation Epoch Simulations (FLARES), a suite of high-resolution (M_gas = 1.8 x 10^6 M_Sun) zoom simulations drawn from a large, (3.2 cGpc)^3 box. We use an extremely randomised trees machine learning approach to model the relationship between galaxies and their subhaloes in a wide range of environments. This allows us to build mock catalogues with dynamic ranges that surpass those obtainable through periodic simulations. The low cost of the zoom simulations facilitates multiple runs of the same regions, differing only in the random number seed of the subgrid models; changing this seed introduces a butterfly effect, leading to random differences in the properties of matching galaxies. This randomness cannot be learnt by a deterministic machine learning model, but by sampling the noise and adding it post-facto to our predictions, we are able to recover the distributions of the galaxy properties we predict (stellar mass, star formation rate, metallicity, and size) remarkably well. We also explore the resolution-dependence of our models' performances and find minimal depreciation down to particle resolutions of order M_DM ~ 10^8 M_Sun, enabling the future application of our models to large dark matter-only boxes.

We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of (25~h^{-1}{rm Mpc})^3 volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying Omega_m, sigma_8, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of (400~h^{-1}{rm Mpc})^3. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.

Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness; this trend is expected to continue into the future as new ground- and space-based surveys come online. In order to maximally extract cosmological information from these observations, matching theoretical predictions are needed. At low redshifts, the surveys probe the nonlinear regime of structure formation where cosmological simulations are the primary means of obtaining the required information. The computational cost of sufficiently resolved large-volume simulations makes it prohibitive to run very large ensembles. Nevertheless, precision emulators built on a tractable number of high-quality simulations can be used to build very fast prediction schemes to enable a variety of cosmological inference studies. We have recently introduced the Mira-Titan Universe simulation suite designed to construct emulators for a range of cosmological probes. The suite covers the standard six cosmological parameters {omega_m,omega_b, sigma_8, h, n_s, w_0} and, in addition, includes massive neutrinos and a dynamical dark energy equation of state, {omega_{nu}, w_a}. In this paper we present the final emulator for the matter power spectrum based on 111 cosmological simulations, each covering a (2.1Gpc)^3 volume and evolving 3200^3 particles. An additional set of 1776 lower-resolution simulations and TimeRG perturbation theory results for the power spectrum are used to cover scales straddling the linear to mildly nonlinear regimes. The emulator provides predictions at the two to three percent level of accuracy over a wide range of cosmological parameters and is publicly released as part of this paper.

Recent observations with the James Webb Space Telescope (JWST) have uncovered unexpectedly high cosmic star formation activity in the early Universe, mere hundreds of millions of years after the Big Bang. These observations are often understood to reflect an evolutionary shift in star formation efficiency (SFE) caused by changing galactic conditions during these early epochs. We present FIREbox-HR, a high-resolution, cosmological hydrodynamical simulation from the Feedback in Realistic Environments project, which offers insights into the SFE of galaxies during the first billion years of cosmic time. FIREbox-HR re-simulates the cosmic volume (L = 22.1 cMpc) of the original FIREbox run with eight times higher mass resolution (m_b ~ 7800 M_sun), but with identical physics, down to z ~ 6. FIREbox-HR predicts ultraviolet (UV) luminosity functions in good agreement with available observational data. The simulation also successfully reproduces the observed cosmic UV luminosity density at z ~ 6 - 14, demonstrating that relatively high star formation activity in the early Universe is a natural outcome of the baryonic processes encoded in the FIRE-2 model. According to FIREbox-HR, the SFE - halo mass relation for intermediate mass halos (M_halo ~ 10^9 - 10^11 M_sun) does not significantly evolve with redshift and is only weakly mass-dependent. These properties of the SFE - halo mass relation lead to a larger contribution from lower mass halos at higher z, driving the gradual evolution of the observed cosmic UV luminosity density. A theoretical model based on the SFE - halo mass relation inferred from FIREbox-HR allows us to explore implications for galaxy evolution. Future observations of UV faint galaxies at z > 12 will provide an opportunity to further test these predictions and deepen our understanding of star formation during Cosmic Dawn.

Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature and composition of the Universe. In this paper we introduce CosmoBench, a benchmark dataset curated from state-of-the-art cosmological simulations whose runs required more than 41 million core-hours and generated over two petabytes of data. CosmoBench is the largest dataset of its kind: it contains 34 thousand point clouds from simulations of dark matter halos and galaxies at three different length scales, as well as 25 thousand directed trees that record the formation history of halos on two different time scales. The data in CosmoBench can be used for multiple tasks -- to predict cosmological parameters from point clouds and merger trees, to predict the velocities of individual halos and galaxies from their collective positions, and to reconstruct merger trees on finer time scales from those on coarser time scales. We provide several baselines on these tasks, some based on established approaches from cosmological modeling and others rooted in machine learning. For the latter, we study different approaches -- from simple linear models that are minimally constrained by symmetries to much larger and more computationally-demanding models in deep learning, such as graph neural networks. We find that least-squares fits with a handful of invariant features sometimes outperform deep architectures with many more parameters and far longer training times. Still there remains tremendous potential to improve these baselines by combining machine learning and cosmology to fully exploit the data. CosmoBench sets the stage for bridging cosmology and geometric deep learning at scale. We invite the community to push the frontier of scientific discovery by engaging with this dataset, available at https://cosmobench.streamlit.app

Statistics that capture the directional dependence of the baryon distribution in the cosmic web enable unique tests of cosmology and astrophysical feedback. We use constrained oriented stacking of thermal Sunyaev-Zel'dovich (tSZ) maps to measure the anisotropic distribution of hot gas 2.5-40 Mpc away from galaxy clusters embedded in massive filaments and superclusters. The cluster selection and orientation (at a scale of sim15 Mpc) use Dark Energy Survey (DES) Year 3 data, while expanded tSZ maps from the Atacama Cosmology Telescope Data Release 6 enable a sim3times more significant measurement of the extended gas compared to the technique's proof-of-concept. Decomposing stacks into cosine multipoles of order m, we detect a dipole (m=1) and quadrupole (m=2) at 8-10sigma, as well as evidence for m=4 signal at up to 6sigma, indicating sensitivity to late-time non-Gaussianity. We compare to the Cardinal simulations with spherical gas models pasted onto dark matter halos. The fiducial tSZ data can discriminate between two models that deplete pressure differently in low-mass halos (mimicking astrophysical feedback), preferring higher average pressure in extended structures. However, uncertainty in the amount of cosmic infrared background contamination reduces the constraining power. Additionally, we apply the technique to DES galaxy density and weak lensing to study for the first time their oriented relationships with tSZ. In the tSZ-to-lensing relation, averaged on 7.5 Mpc (transverse) scales, we observe dependence on redshift but not shape or radial distance. Thus, on large scales, the superclustering of gas pressure, galaxies, and total matter is coherent in shape and extent.

Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high mass resolution is usually limited by available computing time. Therefore, achieving a simulation volume with the effective statistical errors significantly smaller than those of the observations becomes prohibitively expensive. In this study, we apply the Convergence Acceleration by Regression and Pooling (CARPool) method to mitigate the sample variance of the DESI-like galaxy clustering in the AbacusSummit simulations, with the assistance of the quasi-N-body simulations FastPM. Based on the halo occupation distribution (HOD) models, we construct different FastPM galaxy catalogs, including the luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars, with their number densities and two-point clustering statistics well matched to those of AbacusSummit. We also employ the same initial conditions between AbacusSummit and FastPM to achieve high cross-correlation, as it is useful in effectively suppressing the variance. Our method of reducing noise in clustering is equivalent to performing a simulation with volume larger by a factor of 5 and 4 for LRGs and ELGs, respectively. We also mitigate the standard deviation of the LRG bispectrum with the triangular configurations k_2=2k_1=0.2 h/Mpc by a factor of 1.6. With smaller sample variance on galaxy clustering, we are able to constrain the baryon acoustic oscillations (BAO) scale parameters to higher precision. The CARPool method will be beneficial to better constrain the theoretical systematics of BAO, redshift space distortions (RSD) and primordial non-Gaussianity (NG).

We present here self-consistent zoom-in simulations of massive galaxies forming in a full cosmological setting. The simulations are run with an updated version of the KETJU code, which is able to resolve the gravitational dynamics of their supermassive black holes, while simultaneously modelling the large-scale astrophysical processes in the surrounding galaxies, such as gas cooling, star formation and stellar and AGN feedback. The KETJU code is able to accurately model the complex behaviour of multiple SMBHs, including dynamical friction, stellar scattering and gravitational wave emission, and also to resolve Lidov-Kozai oscillations that naturally occur in hierarchical triplet SMBH systems. In general most of the SMBH binaries form at moderately high eccentricities, with typical values in the range of e =0.6-0.95, meaning that the circular binary models that are commonly used in the literature are insufficient for capturing the typical binary evolution.

We introduce the First Light And Reionisation Epoch Simulations (FLARES), a suite of zoom simulations using the EAGLE model. We resimulate a range of overdensities during the Epoch of Reionisation (EoR) in order to build composite distribution functions, as well as explore the environmental dependence of galaxy formation and evolution during this critical period of galaxy assembly. The regions are selected from a large (3.2 ;cGpc)^{3} parent volume, based on their overdensity within a sphere of radius 14,h^{-1};cMpc. We then resimulate with full hydrodynamics, and employ a novel weighting scheme that allows the construction of composite distribution functions that are representative of the full parent volume. This significantly extends the dynamic range compared to smaller volume periodic simulations. We present an analysis of the galaxy stellar mass function (GSMF), the star formation rate distribution function (SFRF) and the star forming sequence (SFS) predicted by \flares, and compare to a number of observational and model constraints. We also analyse the environmental dependence over an unprecedented range of overdensity. Both the GSMF and the SFRF exhibit a clear double-Schechter form, up to the highest redshifts (z = 10). We also find no environmental dependence of the SFS normalisation. The increased dynamic range probed by FLARES will allow us to make predictions for a number of large area surveys that will probe the EoR in coming years, such as WFIRST and Euclid.

This paper studies the scale and redshift variation of density and velocity distributions in self-gravitating collisionless dark matter flow by a halo-based non-projection approach. All particles are divided into halo and out-of-halo particles for redshift variation of distributions. Without projecting particle fields onto a structured grid, the scale variation is analyzed by identifying all particle pairs on different scales r. We demonstrate that: i) Delaunay tessellation can be used to reconstruct the density field. The density correlation, spectrum, and dispersion functions were obtained, modeled, and compared with the N-body simulation; ii) the velocity distributions are symmetric on both small and large scales and are non-symmetric with a negative skewness on intermediate scales due to the inverse energy cascade at a constant rate varepsilon_u; iii) On small scales, the even order moments of pairwise velocity Delta u_L follow a two-thirds law (-varepsilon_ur)^{2/3}, while the odd order moments follow a linear scaling langle(Delta u_L)^{2n+1}rangle=(2n+1)langle(Delta u_L)^{2n}ranglelangleDelta u_Lrangler; iv) The scale variation of the velocity distributions was studied for longitudinal velocities u_L or u_L^{'}, pairwise velocity (velocity difference) Delta u_L=u_L^{'}-u_L and velocity sum Sigma u_L=u^{'}_L+u_L. Fully developed velocity fields are never Gaussian on any scale, despite that they can initially be Gaussian; v) On small scales, u_L and Sigma u_L can be modeled by a X distribution to maximize the system entropy; vi) On large scales, Delta u_L and Sigma u_L can be modeled by a logistic or a X distribution; vii) the redshift variation of the velocity distributions follows the evolution of the X distribution involving a shape parameter alpha(z) decreasing with time.

Domain walls are topological defects that may have formed in the early Universe through the spontaneous breakdown of discrete symmetries, and can be a strong source of gravitational waves (GWs). We perform 3D lattice field theory simulations with CosmoLattice, considering grid sizes N = 1250, 2048 and 4096, to study the dynamics of the domain wall network and its GW signatures. We first analyze how the network approaches the scaling regime with a constant O(1) number of domain walls per Hubble volume, including setups with a large initial number of domains as expected in realistic scenarios, and find that scaling is always reached in a few Hubble times after the network formation. To better understand the properties of the scaling regime, we then numerically extract the Equal Time Correlator (ETC) of the energy-momentum tensor of the network, thus determining its characteristic shape for the case of domain walls, and verifying explicitly its functional dependence as predicted by scaling arguments. The ETC can be further extended to the Unequal Time Correlator (UTC) controlling the GW emission by making assumptions on the coherence of the source. By comparison with the actual GW spectrum evaluated by CosmoLattice, we are then able to infer the degree of coherence of the domain wall network. Finally, by performing numerical simulations in different background cosmologies, e.g. radiation domination and kination, we find evidence for a universal ETC at subhorizon scales and hence a universal shape of the GW spectrum in the UV, while the expansion history of the Universe may instead be determined by the IR features of the GW spectrum.

Using a set of LambdaCDM simulations of cosmic structure formation, we study the evolving connectivity and changing topological structure of the cosmic web using state-of-the-art tools of multiscale topological data analysis (TDA). We follow the development of the cosmic web topology in terms of the evolution of Betti number curves and feature persistence diagrams of the three (topological) classes of structural features: matter concentrations, filaments and tunnels, and voids. The Betti curves specify the prominence of features as a function of density level, and their evolution with cosmic epoch reflects the changing network connections between these structural features. The persistence diagrams quantify the longevity and stability of topological features. In this study we establish, for the first time, the link between persistence diagrams, the features they show, and the gravitationally driven cosmic structure formation process. By following the diagrams' development over cosmic time, the link between the multiscale topology of the cosmic web and the hierarchical buildup of cosmic structure is established. The sharp apexes in the diagrams are intimately related to key transitions in the structure formation process. The apex in the matter concentration diagrams coincides with the density level at which, typically, they detach from the Hubble expansion and begin to collapse. At that level many individual islands merge to form the network of the cosmic web and a large number of filaments and tunnels emerge to establish its connecting bridges. The location trends of the apex possess a self-similar character that can be related to the cosmic web's hierarchical buildup. We find that persistence diagrams provide a significantly higher and more profound level of information on the structure formation process than more global summary statistics like Euler characteristic or Betti numbers.

The peculiar velocities of biased tracers of the cosmic density field contain important information about the growth of large scale structure and generate anisotropy in the observed clustering of galaxies. Using N-body data, we show that velocity expansions for halo redshift-space power spectra are converged at the percent-level at perturbative scales for most line-of-sight angles mu when the first three pairwise velocity moments are included, and that the third moment is well-approximated by a counterterm-like contribution. We compute these pairwise-velocity statistics in Fourier space using both Eulerian and Lagrangian one-loop perturbation theory using a cubic bias scheme and a complete set of counterterms and stochastic contributions. We compare the models and show that our models fit both real-space velocity statistics and redshift-space power spectra for both halos and a mock sample of galaxies at sub-percent level on perturbative scales using consistent sets of parameters, making them appealing choices for the upcoming era of spectroscopic, peculiar-velocity and kSZ surveys.

Studies show that the model-independent, fully non-perturbative covariant cosmographic approach is suitable for analyzing the local Universe (zlesssim 0.1). However, accurately characterizing large and inhomogeneous mass distributions requires the fourth-order term in the redshift expansion of the covariant luminosity distance d_L(z,n). We calculate the covariant snap parameter S and its spherical harmonic multipole moments using the matter expansion tensor and the evolution equations for lightray bundles. The fourth-order term adds 36 degrees of freedom, since the highest independent multipole of the snap is the 32-pole (dotriacontapole) (ell=5). Including this term helps to de-bias estimations of the covariant deceleration parameter. Given that observations suggest axially symmetric anisotropies in the Hubble diagram for z lesssim 0.1 and theory shows that only a subset of multipoles contributes to the signal, we demonstrate that only 12 degrees of freedom are needed for a model-independent description of the local universe. We use an analytical axisymmetric model of the local Universe, with data that matches the Zwicky Transient Facility survey, in order to provide a numerical example of the amplitude of the snap multipoles and to forecast precision.

The James Webb Space Telescope's (JWST) discovery of an unexpectedly high abundance of UV-bright galaxies at redshifts z > 10 poses a significant challenge to the standard LambdaCDM cosmology. This work tests whether this tension can be resolved solely by modifying the cosmological background, without invoking significant evolution in the astrophysical properties of early galaxies. We investigate an alternative framework featuring the presence of an anti-de Sitter vacuum in the dark energy sector, a model that naturally arises in quantum gravity models like string theory and can enhance early structure formation. Using a self-consistent semi-analytical model that couples galaxy evolution with reionization, we confront this scenario with a wide range of observations. We first show that while a model tailored to fit the high-z UV luminosity functions (UVLFs) shows promise, it is in strong tension with well-established cosmological constraints from the CMB and other low-redshift probes. Conversely, models within this framework that are consistent with these constraints provide only a modest boost to structure formation and fail to reproduce the observed JWST galaxy abundances at z > 10. While these models remain consistent with the cosmic reionization history, our primary result is that this class of cosmological modifications is insufficient on its own to explain the galaxy excess. Our study underscores the critical importance of holistic testing for any beyond-LambdaCDM proposal; apparent success in one observational regime does not guarantee overall viability. By demonstrating the limitations of a purely cosmological solution, our results strengthen the case that evolving astrophysical properties are a necessary ingredient for solving the challenge of early galaxy formation.

We present the first high-precision model for the group-scale strong lensing system CASSOWARY 19 (CSWA19), utilising images from the Hubble Space Telescope (HST). Sixteen member galaxies identified via the red-sequence method, and the main halo, all modelled as the dual Pseudo Isothermal Elliptical profile (dPIE), are incorporated into a parametric lens model alongside an external shear field. To model the system, we adopt the PyAutoLens software package, employing a progressive search chain strategy for realizing the transition of source model from multiple S\'ersic profiles to a brightness-adaptive pixelization, which uses 1000 pixels in the source plane to reconstruct the background source corresponding to 177,144 image pixels in the image plane. Our results indicate that the total mass within the Einstein radius is M_{theta_E} approx 1.41times10^{13}M_{odot} and the average slope of the total mass density rho (r)propto r^{-gamma} is gamma=1.33 within the effective radius. This slope is shallower than those measured in galaxies and groups but is closer to those of galaxy clusters. In addition, our approach successfully resolves the two merging galaxies in the background source and yields a total magnification of mu=103.18^{+0.23}_{-0.19}, which is significantly higher than the outcomes from previous studies of CSWA19. In summary, our research demonstrates the effectiveness of the brightness-adaptive pixelization source reconstruction technique for modelling group-scale strong lensing systems. It can serve as a technical reference for future investigations into pixel-level modelling of the group- and cluster-scale strong lensing systems.

Baryonic effects created by feedback processes associated with galaxy formation are an important, poorly constrained systematic effect for models of large-scale structure as probed by weak gravitational lensing. Upcoming surveys require fast methods to predict and marginalize over the potential impact of baryons on the total matter power spectrum. Here we use the FLAMINGO cosmological hydrodynamical simulations to test a recent proposal to approximate the matter power spectrum as the sum of the linear matter power spectrum and a constant multiple, A_{rm mod}, of the difference between the linear and non-linear gravity-only power spectra. We show that replacing this constant multiple with a one-parameter family of sigmoid functions of the wavenumber k allows to us match the predictions of simulations with different feedback strengths for z leq 1, k < 3~hrm Mpc^{-1}, and the different cosmological models in the FLAMINGO suite. The baryonic response predicted by FLAMINGO models that use jet-like AGN feedback instead of the fiducial thermally-driven AGN feedback can also be reproduced, but at the cost of increasing the number of parameters in the sigmoid function from one to three. The assumption that A_{rm mod} depends only on k breaks down for decaying dark matter models, highlighting the need for more advanced baryon response models when studying cosmological models that deviate strongly from LambdaCDM.

We present a neural network emulator to constrain the thermal parameters of the intergalactic medium (IGM) at 5.4z6.0 using the Lyman-displaystylealpha (Lydisplaystylealpha) forest flux auto-correlation function. Our auto-differentiable JAX-based framework accelerates the surrogate model generation process using approximately 100 sparsely sampled Nyx hydrodynamical simulations with varying combinations of thermal parameters, i.e., the temperature at mean density T_{{0}}, the slope of the temperaturedisplaystyle-density relation displaystylegamma, and the mean transmission flux langle{F}{rangle}. We show that this emulator has a typical accuracy of 1.0% across the specified redshift range. Bayesian inference of the IGM thermal parameters, incorporating emulator uncertainty propagation, is further expedited using NumPyro Hamiltonian Monte Carlo. We compare both the inference results and computational cost of our framework with the traditional nearest-neighbor interpolation approach applied to the same set of mock Lyalpha flux. By examining the credibility contours of the marginalized posteriors for T_{{0}},gamma,and{langle}{F}{rangle} obtained using the emulator, the statistical reliability of measurements is established through inference on 100 realistic mock data sets of the auto-correlation function.

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.

Context. Radiation plays a significant role in solar and astrophysical environments as it may constitute a sizeable fraction of the energy density, momentum flux, and the total pressure. Modelling the dynamic interaction between radiation and magnetized plasmas in such environments is an intricate and computationally costly task. Aims. The goal of this work is to demonstrate the capabilities of the open-source parallel, block-adaptive computational framework MPI-AMRVAC, in solving equations of radiation-magnetohydrodynamics (RMHD), and to present benchmark test cases relevant for radiation-dominated magnetized plasmas. Methods. The existing magnetohydrodynamics (MHD) and flux-limited diffusion (FLD) radiative-hydrodynamics physics modules are combined to solve the equations of radiation-magnetohydrodynamics (RMHD) on block-adaptive finite volume Cartesian meshes in any dimensionality. Results. We introduce and validate several benchmark test cases such as steady radiative MHD shocks, radiation-damped linear MHD waves, radiation-modified Riemann problems and a multi-dimensional radiative magnetoconvection case. We recall the basic governing Rankine-Hugoniot relations for shocks and the dispersion relation for linear MHD waves in the presence of optically thick radiation fields where the diffusion limit is reached. The RMHD system allows for 8 linear wave types, where the classical 7-wave MHD picture (entropy and three wave pairs for slow, Alfven and fast) is augmented with a radiative diffusion mode. Conclusions. The MPI-AMRVAC code now has the capability to perform multidimensional RMHD simulations with mesh adaptation making it well-suited for larger scientific applications to study magnetized matter-radiation interactions in solar and stellar interiors and atmospheres.

An avenue for understanding cosmological galaxy formation is to compare morphometric parameters in observations and simulations of galaxy assembly. In this second paper of the ASymba: Asymmetries of HI in SIMBA Galaxies series, we measure atomic gas HI asymmetries in spatially-resolved detections from the untargetted WALLABY survey, and compare them to realizations of WALLABY-like mock samples from the SIMBA cosmological simulations. We develop a Scanline Tracing method to create mock galaxy HI datacubes which minimizes shot noise along the spectral dimension compared to particle-based methods, and therefore spurious asymmetry contributions. We compute 1D and 3D asymmetries for spatially-resolved WALLABY Pilot Survey detections, and find that the highest 3D asymmetries A3D>0.5 stem from interacting systems or detections with strong bridges or tails. We then construct a series of WALLABY-like mock realizations drawn from the SIMBA 50 Mpc simulation volume, and compare their asymmetry distributions. We find that the incidence of high A3D detections is higher in WALLABY than in the SIMBA mocks, but that difference is not statistically significant (p-value = 0.05). The statistical power of quantitative comparisons of asymmetries such as the one presented here will improve as the WALLABY survey progresses, and as simulation volumes and resolutions increase.

With its exquisite sensitivity, wavelength coverage, and spatial and spectral resolution, the James Webb Space Telescope is poised to revolutionise our view of the distant, high-redshift (z>5) Universe. While Webb's spectroscopic observations will be transformative for the field, photometric observations play a key role in identifying distant objects and providing more comprehensive samples than accessible to spectroscopy alone. In addition to identifying objects, photometric observations can also be used to infer physical properties and thus be used to constrain galaxy formation models. However, inferred physical properties from broadband photometric observations, particularly in the absence of spectroscopic redshifts, often have large uncertainties. With the development of new tools for forward modelling simulations it is now routinely possible to predict observational quantities, enabling a direct comparison with observations. With this in mind, in this work, we make predictions for the colour evolution of galaxies at z=5-15 using the FLARES: First Light And Reionisation Epoch Simulations cosmological hydrodynamical simulation suite. We predict a complex evolution, driven predominantly by strong nebular line emission passing through individual bands. These predictions are in good agreement with existing constraints from Hubble and Spitzer as well as some of the first results from Webb. We also contrast our predictions with other models in the literature: while the general trends are similar we find key differences, particularly in the strength of features associated with strong nebular line emission. This suggests photometric observations alone should provide useful discriminating power between different models.

There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees. The former suffer from mode collapse or memorization issues, limiting their application to scientific data. The latter require restrictive assumptions such as log-concavity to escape the curse of dimensionality. We partially bridge this gap by introducing conditionally strongly log-concave (CSLC) models, which factorize the data distribution into a product of conditional probability distributions that are strongly log-concave. This factorization is obtained with orthogonal projectors adapted to the data distribution. It leads to efficient parameter estimation and sampling algorithms, with theoretical guarantees, although the data distribution is not globally log-concave. We show that several challenging multiscale processes are conditionally log-concave using wavelet packet orthogonal projectors. Numerical results are shown for physical fields such as the varphi^4 model and weak lensing convergence maps with higher resolution than in previous works.

Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star-formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2,000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allow our models to infer the value of Omega_{rm m}, at fixed Omega_{rm b}, with a sim10% precision, while no constraint can be placed on sigma_8. Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, zleq3, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of Omega_{rm m}. We believe that our results can be explained taking into account that changes in the value of Omega_{rm m}, or potentially Omega_{rm b}/Omega_{rm m}, affect the dark matter content of galaxies. That effect leaves a distinct signature in galaxy properties to the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.

We reassess the realistic discovery reach of Solar-System experiments for dark energy (DE) and dark matter (DM), making explicit the bridge from cosmology-level linear responses to local, screened residuals. In scalar-tensor frameworks with a universal conformal coupling A(phi) and chameleon/Vainshtein screening, we map cosmological responses {mu(z,k),Sigma(z,k)} inferred by DESI and Euclid to thin-shell or Vainshtein residuals in deep Solar potentials Phi_N. We emphasize a two-branch strategy. In a detection-first branch, a verified local anomaly -- an Einstein equivalence principle (EEP) violation, a Shapiro-delay signal with |gamma-1|simfewtimes 10^{-6}, an AU-scale Yukawa tail, or a ultralight DM (ULDM) line in clocks/atom interferometers in space (AIS) -- triggers a joint refit of cosmology and Solar-System data under a common microphysical parameterization {V(phi),A(phi)}. In a guardrail branch, Solar-System tests enforce constraints (EEP; PPN parameters gamma,beta; and dot G/G) and close unscreened or weakly screened corners indicated by cosmology. We forecast, per conjunction, |gamma-1|lesssim (2-5)times 10^{-6} (Ka-/X-band or optical Shapiro), eta_{EEP}sim (1--10)times 10^{-17} (drag-free AIS), |dot G/G|sim(3-5)times10^{-15},yr^{-1} (sub-mm-class LLR), a uniform ~2x tightening of AU-scale Yukawa/DM-density bounds, and (3-10)times improved ULDM-coupling reach from clocks. For a conformal benchmark, mu_{ lin,0}=0.10 implies chisimeq mu_{lin,0/2} and a Sun thin shell Delta R/Rlesssim (1/3chi)|gamma-1|/2=2.4times 10^{-3} at |gamma-1|=5times 10^{-6}; Vainshtein screening at 1 AU yields |gamma-1|lesssim 10^{-11}, naturally below near-term reach. We recommend a cost-effective guardrail+discovery portfolio with explicit triggers for escalation to dedicated missions.

Multi-agent systems (MAS) utilizing multiple Large Language Model agents with Retrieval Augmented Generation and that can execute code locally may become beneficial in cosmological data analysis. Here, we illustrate a first small step towards AI-assisted analyses and a glimpse of the potential of MAS to automate and optimize scientific workflows in Cosmology. The system architecture of our example package, that builds upon the autogen/ag2 framework, can be applied to MAS in any area of quantitative scientific research. The particular task we apply our methods to is the cosmological parameter analysis of the Atacama Cosmology Telescope lensing power spectrum likelihood using Monte Carlo Markov Chains. Our work-in-progress code is open source and available at https://github.com/CMBAgents/cmbagent.

We present cosmological analysis of 12 nearby (z<0.06) Type IIP supernovae (SNe IIP) observed with the ROTSE-IIIb telescope. To achieve precise photometry, we present a new image differencing technique that is implemented for the first time on the ROTSE SN photometry pipeline. With this method, we find up to a 20\% increase in the detection efficiency and significant reduction in residual RMS scatter of the SN lightcurves when compared to the previous pipeline performance. We use the published optical spectra and broadband photometry of well studied SNe IIP to establish temporal models for ejecta velocity and photospheric temperature evolution for our SNe IIP population. This study yields measurements that are competitive to other methods even when the data are limited to a single epoch during the photospheric phase of SNe IIP. Using the fully reduced ROTSE photometry and optical spectra, we apply these models to the respective photometric epochs for each SN in the ROTSE IIP sample. This facilitates the use of the Expanding Photosphere Method (EPM) to obtain distance estimates to their respective host galaxies. We then perform cosmological parameter fitting using these EPM distances from which we measure the Hubble constant to be 72.9^{+5.7}_{-4.3}~{rm kms^{-1}~Mpc^{-1}}, which is consistent with the standard Lambda CDM model values derived using other independent techniques.

Context. Cosmic voids are vast underdense regions in the cosmic web that encode crucial information about structure formation, the composition of the Universe, and its expansion history. Due to their lower density, these regions are less affected by non-linear gravitational dynamics, making them suitable candidates for analysis using semi-analytic methods. Aims. We assess the accuracy of the PINOCCHIO code, a fast tool for generating dark matter halo catalogs based on Lagrangian Perturbation Theory, in modeling the statistical properties of cosmic voids. We validate this approach by comparing the resulting void statistics measured from PINOCCHIO to those obtained from N-body simulations. Methods. We generate a set of simulations using PINOCCHIO and OpenGADGET3, assuming a fiducial cosmology and varying the resolution. For a given resolution, the simulations share the same initial conditions between the different simulation codes. Snapshots are saved at multiple redshifts for each simulation and post-processed using the watershed void finder VIDE to identify cosmic voids. For each simulation code, we measure the following statistics: void size function, void ellipticity function, core density function, and the void radial density profile. We use these statistics to quantify the accuracy of PINOCCHIO relative to OpenGADGET3 in the context of cosmic voids. Results. We find agreement for all void statistics at better than 2{\sigma} between PINOCCHIO and OpenGADGET3, with no systematic difference in redshift trends. This demonstrates that the PINOCCHIO code can reliably produce void statistics with high computational efficiency compared to full N-body simulations.

The collisionless Boltzmann equation (CBE) is a fundamental equation that governs the dynamics of a broad range of astrophysical systems from space plasma to star clusters and galaxies. It is computationally expensive to integrate the CBE directly in a multi-dimensional phase space, and thus the applications to realistic astrophysical problems have been limited so far. Recently, Todorova & Steijl (2020) proposed an efficient quantum algorithm to solve the CBE with significantly reduced computational complexity. We extend the algorithm to perform quantum simulations of self-gravitating systems, incorporating the method to calculate gravity with the major Fourier modes of the density distribution extracted from the solution-encoding quantum state. Our method improves the dependency of time and space complexities on Nv , the number of grid points in each velocity coordinate, compared to the classical simulation methods. We then conduct some numerical demonstrations of our method. We first run a 1+1 dimensional test calculation of free streaming motion on 64*64 grids using 13 simulated qubits and validate our method. We then perform simulations of Jeans collapse, and compare the result with analytic and linear theory calculations. It will thus allow us to perform large-scale CBE simulations on future quantum computers.

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range 0.1<z<2.1 divided into six redshift bins over a sim 7,500 square degree footprint, from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). By fitting the full power spectrum, we extend previous DESI DR1 baryon acoustic oscillation (BAO) measurements to include redshift-space distortions and signals from the matter-radiation equality scale. For the first time, this Full-Shape analysis is blinded at the catalogue-level to avoid confirmation bias and the systematic errors are accounted for at the two-point clustering level, which automatically propagates them into any cosmological parameter. When analysing the data in terms of compressed model-agnostic variables, we obtain a combined precision of 4.7\% on the amplitude of the redshift space distortion signal reaching similar precision with just one year of DESI data than with 20 years of observation from previous generation surveys. We analyse the data to directly constrain the cosmological parameters within the LambdaCDM model using perturbation theory and combine this information with the reconstructed DESI DR1 galaxy BAO. Using a Big Bang Nucleosynthesis Gaussian prior on the baryon density parameter, and a Gaussian prior on the spectral index, we constrain the matter density is Omega_m=0.296pm 0.010 and the Hubble constant H_0=(68.63 pm 0.79)[{rm km, s^{-1}Mpc^{-1}}]. Additionally, we measure the amplitude of clustering sigma_8=0.841 pm 0.034. The DESI DR1 results are in agreement with the LambdaCDM model based on general relativity with parameters consistent with those from Planck. The cosmological interpretation of these results in combination with external datasets are presented in a companion paper.

Integral field units (IFU) have extended our knowledge of galactic properties to kpc (or, sometimes, even smaller) patches of galaxies. These scales are where the physics driving galaxy evolution (feedback, chemical enrichment, etc.) take place. Quantifying the spatially-resolved properties of galaxies, both observationally and theoretically, is therefore critical to our understanding of galaxy evolution. To this end, we investigate spatially-resolved scaling relations within central galaxies (M_star>10^{9.0}) at z=0 in IllustrisTNG. We examine both the resolved star-forming main sequence (rSFMS) and the resolved mass-metallicity relation (rMZR) using 1~{rm kpc}times1~{rm kpc} maps of galaxies. We find that the rSFMS in IllustrisTNG is well-described by a power-law, but has some dependence on the host galaxy's mass. Conversely, the rMZR for IllustrisTNG can be described by a single power-law at low stellar mass surface density that flattens at high surface densities and is independent of host galaxy mass. We find quantitative agreement in both the rSFMS and rMZR with recent IFU observational campaigns. Furthermore, we argue that the rSFMS is an indirect result of the Schmidt-Kennicutt (SK) law and local gas fraction relation, which are both independent of host galaxy properties. Finally, we expand upon a localized leaky-box model to study the evolution of idealized spaxels and find that it provides a good description of these resolved relations. The degree of agreement, however, between idealized spaxels and simulated spaxels depends on the `net' outflow rate for the spaxel, and the observed scaling relations indicate a preference for a low net outflow rate.

The main aim of this work is to use a model-independent approach, along with late-time observational probes, to reconstruct the dark energy (DE) equation of state w_{rm DE}(z). Our analysis showed that, for a late time universe, w_{rm DE} deviates from being a constant but in contrast exhibits an oscillatory behavior, hence both quintessence (w_{rm DE}> -1) and phantom (w_{rm DE} < -1) regimes are equally allowed. In order to portray this oscillatory behavior, we explored various parametrizations for the equation of state and identified the closest approximation based on the goodness of fit with the data and the Bayesian evidence analysis. Our findings indicated that while all considered oscillating DE parametrizations provided a better fit to the data, compared to the cosmological constant, they are penalized in the Bayesian evidence analysis due to the additional free parameters. Overall, the present article demonstrates that in the low redshift regime, the equation of state of the DE prefers to be dynamical and oscillating. We anticipate that future cosmological probes will take a stand in this direction.

In this paper, we emphasise the recent observational findings from the Dark Energy Spectroscopic Instrument Data Release 2 (DESI DR2), which provide compelling evidence for a possible deviation from the standard ΛCDM (Cold Dark Matter) cosmology, suggesting the presence of a dynamically evolving effective dark energy component. Motivated by this, we construct a theoretical framework in which a massive cosmological vector field, B^μ, couples non-minimally to the background curvature through marginal interactions, offering a controlled mechanism to realise the deviation from the ΛCDM model. A detailed analysis of the effective Equation of State (EoS) parameter w(tilde H) reveals a narrow region of parameter space consistent with current cosmological observations presented by DESI. The analysis yields a stringent upper bound for the coupling constant λ to be λ<2.98times10^{-11}, a very strong bound on mass 3.1356times10^{-66}~g leq m leq 3.3627times10^{-66}~g, and the admissible range -0.405 leq log_{10}tildeγleq -0.38 for which present-day value w_0 = w(tilde H = 1) corresponding to a deviation δ= w_0 + 1 that lies within the region 0.107 leq δleq 0.217. This interval reproduces the deviation inferred from the combined DESI, Cosmic Microwave Background (CMB), and Pantheon+ data, reflecting a controlled departure from the ΛCDM behaviour. In summary, the results suggest that the proposed framework of a massive vector field can account for the departure from ΛCDM behaviour highlighted by DESI in the current cosmic acceleration. Furthermore, the framework approaches the ΛCDM behaviour in late-time tgtrsim28 Gyr, establishing a direct phenomenological link between the underlying parameters and the observed dynamical nature of dark energy.

Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. Yet modeling the selection function of such samples is complicated by non-cluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). We empirically constrain the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy survey data with the \rdmpr, algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). For matched clusters, we augment the \rdmpr,catalog by the SPT detection significance. For unmatched objects we use the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multi-wavelength data, we explore various physically motivated models to describe the relationship between observed richness and halo mass. Our analysis reveals the limitations of a simple lognormal scatter model in describing the data. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge, requiring a combination of simulation and data-driven approaches.

The wavelength-coverage and sensitivity of JWST now enables us to probe the rest-frame UV - optical spectral energy distributions (SEDs) of galaxies at high-redshift (z>4). From these SEDs it is, in principle, through SED fitting possible to infer key physical properties, including stellar masses, star formation rates, and dust attenuation. These in turn can be compared with the predictions of galaxy formation simulations allowing us to validate and refine the incorporated physics. However, the inference of physical properties, particularly from photometry alone, can lead to large uncertainties and potential biases. Instead, it is now possible, and common, for simulations to be forward-modelled to yield synthetic observations that can be compared directly to real observations. In this work, we measure the JWST broadband fluxes and colours of a robust sample of 5<z<10 galaxies using the Cosmic Evolution Early Release Science (CEERS) Survey. We then analyse predictions from a variety of models using the same methodology and compare the NIRCam/F277W magnitude distribution and NIRCam colours with observations. We find that the predicted and observed magnitude distributions are similar, at least at 5<z<8. At z>8 the distributions differ somewhat, though our observed sample size is small and thus susceptible to statistical fluctuations. Likewise, the predicted and observed colour evolution show broad agreement, at least at 5<z<8. There is however some disagreement between the observed and modelled strength of the strong line contribution. In particular all the models fails to reproduce the F410M-F444W colour at z>8, though, again, the sample size is small here.

Many previous studies have imposed stringent constraints on the particle mass of fuzzy dark matter (FDM) by analyzing observations of Galactic satellite galaxies, which show no significant evidence of the heating effect predicted by FDM. However, these analyses have generally neglected the tidal influence of the Milky Way, which can substantially suppress the FDM-induced heating in satellites. This oversight arises from computational challenges of accurately capturing the tidal effects in FDM simulations. In this study, we present a novel simulation framework that, for the first time, enables the simulation of an FDM-stellar system within an observationally motivated gravitational potential of the Milky Way. This framework incorporates the diverse Galactic components, including the gravitational influence of the Large Magellanic Cloud. Using the Fornax dwarf galaxy as a case study, we demonstrate that tidal effects significantly alleviate the tension between observational data and the predicted heating effect for an FDM particle mass of m_asim 10^{-22} eV.

We use the First Light And Reionisation Epoch Simulations (FLARES) to study the evolution of the rest-frame ultraviolet (UV) and far-infrared (FIR) sizes for a statistical sample of massive (gtrsim10^{9}M_{odot}) high redshift galaxies (z in [5,10]). Galaxies are post-processed using the SKIRT radiative transfer code, to self-consistently obtain the full spectral energy distribution and surface brightness distribution. We create mock observations of the galaxies for the Near Infrared Camera (NIRCam) to study the rest-frame UV 1500 xC5 morphology. We also generate mock rest-frame FIR (50 mum) photometry and mock ALMA (158 mum) (0.01"-0.03" and approx0.3" angular resolution) observations to study the dust-continuum. We find the effect of dust on observed sizes reduces with increasing wavelength from the UV to optical (sim0.6 times the UV at 0.4mum), with no evolution in FIR sizes. Observed sizes vary within 0.4-1.2 times the intrinsic sizes at different signal to noise ratios (SNR = 5-20) across redshifts. The effect of PSF and noise makes bright structures prominent, whereas fainter regions blend with noise, leading to an underestimation (factor of 0.4-0.8) of sizes at SNR=5. At SNR=15-20, the underestimation reduces (factor of 0.6-0.9) at z=5-8 but due to PSF, at z=9-10, bright cores are dominant, resulting in an overestimation (factor of 1.0-1.2). For ALMA, low resolution sizes are effected by noise which acts as extended emission. The size evolution in UV broadly agrees with current observational samples and other simulations. This work is one of the first to analyse the panchromatic sizes of a statistically significant sample of simulated high-redshift galaxies, complementing a growing body of research highlighting the importance of conducting an equivalent comparison between observed galaxies and their simulated counterparts in the early Universe.

Dwarf galaxies serve as powerful laboratories for investigating the underlying physics of galaxy evolution including the impact of baryonic feedback processes and environmental influences. We compare the visual and structural properties of dwarf galaxies in ultra-deep HSC-SSP imaging of the COSMOS field with those measured from realistic HSC-like synthetic observations of dwarfs generated by the Illustris TNG50 and NewHorizon simulations. Using S\'ersic profile fitting and non-parametric morphological metrics (Gini, M_{20}, asymmetry, and concentration), we evaluate the diversity of structural properties in observed and simulated galaxies. Our analysis shows that NewHorizon and TNG50 galaxies lie at opposite extremes of observed structural trends: NewHorizon produces diffuse, extended galaxies with shallow S\'ersic indices, while TNG50 yields compact, concentrated systems with steep indices. Both simulations reproduce observed structural trends more closely at higher stellar masses (M_{star}sim10^{9.5} {rm M_{odot}}) but fail to capture the full diversity of COSMOS dwarfs at lower masses. Non-parametric metrics further show that NewHorizon galaxies exhibit more uneven, clumpy light distributions while TNG50 galaxies have smoother but excessively concentrated profiles. These structural differences reflect underlying differences in their physical prescriptions and are likely driven by differing approaches to ISM physics, supernova feedback and star formation in addition to differences in numerical resolution. Our findings highlight the unique power of low-mass galaxies to constrain differences in simulation physics, especially star formation and feedback. Upcoming surveys from facilities like the Vera C. Rubin Observatory and Euclid will enable more rigorous comparisons with simulations, offering deeper insights into the physical processes shaping galaxy evolution.

Some stars are known to explode at the end of their lives, called supernovae (SNe). The substantial amount of matter and energy that SNe release provides significant feedback to star formation and gas dynamics in a galaxy. SNe release a substantial amount of matter and energy to the interstellar medium, resulting in significant feedback to star formation and gas dynamics in a galaxy. While such feedback has a crucial role in galaxy formation and evolution, in simulations of galaxy formation, it has only been implemented using simple {\it sub-grid models} instead of numerically solving the evolution of gas elements around SNe in detail due to a lack of resolution. We develop a method combining machine learning and Gibbs sampling to predict how a supernova (SN) affects the surrounding gas. The fidelity of our model in the thermal energy and momentum distribution outperforms the low-resolution SN simulations. Our method can replace the SN sub-grid models and help properly simulate un-resolved SN feedback in galaxy formation simulations. We find that employing our new approach reduces the necessary computational cost to sim 1 percent compared to directly resolving SN feedback.

We present results on the star cluster properties from a series of high resolution smoothed particles hydrodynamics (SPH) simulations of isolated dwarf galaxies as part of the GRIFFIN project. The simulations at sub-parsec spatial resolution and a minimum particle mass of 4 M_odot incorporate non-equilibrium heating, cooling and chemistry processes, and realise individual massive stars. All the simulations follow feedback channels of massive stars that include the interstellar-radiation field, that is variable in space and time, the radiation input by photo-ionisation and supernova explosions. Varying the star formation efficiency per free-fall time in the range epsilon_ff = 0.2 - 50% neither changes the star formation rates nor the outflow rates. While the environmental densities at star formation change significantly with epsilon_ff, the ambient densities of supernovae are independent of epsilon_ff indicating a decoupling of the two processes. At low epsilon_ff, more massive, and increasingly more bound star clusters are formed, which are typically not destroyed. With increasing epsilon_ff there is a trend for shallower cluster mass functions and the cluster formation efficiency Gamma for young bound clusters decreases from 50 % to sim 1 % showing evidence for cluster disruption. However, none of our simulations form low mass (< 10^3 M_odot) clusters with structural properties in perfect agreement with observations. Traditional star formation models used in galaxy formation simulations based on local free-fall times might therefore not be able to capture low mass star cluster properties without significant fine-tuning.

The decay of metastable 'false vacuum' states via bubble nucleation plays a crucial role in many cosmological scenarios. Cold-atom analog experiments will soon provide the first empirical probes of this process, with potentially far-reaching implications for early-Universe cosmology and high-energy physics. However, an inevitable difference between these analog systems and the early Universe is that the former have a boundary. We show, using a combination of Euclidean calculations and real-time lattice simulations, that these boundaries generically cause rapid bubble nucleation on the edge of the experiment, obscuring the bulk nucleation that is relevant for cosmology. We demonstrate that implementing a high-density 'trench' region at the boundary completely eliminates this problem, and recovers the desired cosmological behavior. Our findings are relevant for ongoing efforts to probe vacuum decay in the laboratory, providing a practical solution to a key experimental obstacle.

We revisit the flat-sky approximation for evaluating the angular power spectra of projected random fields by retaining information about the correlations along the line of sight. With broad, overlapping radial window functions, these line-of-sight correlations are suppressed and are ignored in the Limber approximation. However, retaining the correlations is important for narrow window functions or unequal-time spectra but introduces significant computational difficulties due to the highly oscillatory nature of the integrands involved. We deal with the integral over line-of-sight wave-modes in the flat-sky approximation analytically, using the FFTlog expansion of the 3D power spectrum. This results in an efficient computational method, which is a substantial improvement compared to any full-sky approaches. We apply our results to galaxy clustering (with and without redshift-space distortions), CMB lensing and galaxy lensing observables. For clustering, we find excellent agreement with the full-sky results on large (percent-level agreement) and intermediate or small (subpercent agreement) scales, dramatically out-performing the Limber approximation for both wide and narrow window functions, and in equal- and unequal-time cases. In the case of lensing, we show on the full sky that the angular power spectrum of the convergence can be very well approximated by projecting the 3D Laplacian (rather than the correct angular Laplacian) of the gravitational potential, even on large scales. Combining this approximation with our flat-sky techniques provides an efficient and accurate evaluation of the CMB lensing angular power spectrum on all scales.

We analyse the robustness of the DESI 2024 cosmological inference from fits to the full shape of the galaxy power spectrum to uncertainties in the Halo Occupation Distribution (HOD) model of the galaxy-halo connection and the choice of priors on nuisance parameters. We assess variations in the recovered cosmological parameters across a range of mocks populated with different HOD models and find that shifts are often greater than 20% of the expected statistical uncertainties from the DESI data. We encapsulate the effect of such shifts in terms of a systematic covariance term, C_{rm HOD}, and an additional diagonal contribution quantifying the impact of our choice of nuisance parameter priors on the ability of the effective field theory (EFT) model to correctly recover the cosmological parameters of the simulations. These two covariance contributions are designed to be added to the usual covariance term, C_{rm stat}, describing the statistical uncertainty in the power spectrum measurement, in order to fairly represent these sources of systematic uncertainty. This approach is more general and robust to choices of model free parameters or additional external datasets used in cosmological fits than the alternative approach of adding systematic uncertainties at the level of the recovered marginalised parameter posteriors. We compare the approaches within the context of a fixed LambdaCDM model and demonstrate that our method gives conservative estimates of the systematic uncertainty that nevertheless have little impact on the final posteriors obtained from DESI data.

The objective of this study is to develop a fully compressible magnetohydrodynamic solver for fast simulations of the global dynamo of the Sun using unstructured grids and GPUs. Accurate modeling of the Sun's convective layers is vital to predicting the Sun's behavior, including the solar dynamo and sunspot cycles. Currently, there are many efficient codes capable of conducting these large simulations; however, many assume an anealastic density distribution. The anelastic assumption is capable of producing accurate results for low mach numbers; however, it fails in regions with a higher mach number and a fully compressible flow must be considered. To avoid these issues, Wang et al. [1] created a Compressible High-ORder Unstructured Spectral difference (CHORUS) code for simulating fluid dynamics inside stars and planets. CHORUS++ augmented the CHORUS code to adopt a higher degree of polynomials by using cubed-sphere meshing and transfinite mapping to perform simulations on unstructured grids [2]. Recently, CHORUS++ was further developed for parallel magnetohydrodynamic (MHD) solutions on GPUs at Clarkson University. In this study the solar benchmark problems presented by Chen et al. [2] are extended to unsteady solar dynamo problems, with two different density scale heights. The CHORUS-MHD code is further accelerated by multiple GPUs and used to successfully solve these solar dynamo benchmark problems. [1] Wang, J., Liang, C., and Miesch, M. S., "A Compressible High-Order Unstructured Spectral Difference Code for Stratified Convection in Rotating Spherical Shells," Journal of Computational Physics, Vol. 290, 2015, pp. 90-111. [2] Chen, K., Liang, C., and Wan, M., "Arbitrarily high-order accurate simulations of compressible rotationally constrained convection using a transfinite mapping on cubed-sphere grids," Physics of Fluids, Vol. 35, 2023, p. 086120.

The reionization of helium is thought to occur at 2.5lesssim zlesssim4, marking the last phase transition and final global heating event of the intergalactic medium (IGM). Since it is driven by rare quasars, helium reionization should give rise to strong temperature fluctuations in the IGM between neutral and recently-ionized regions of order sigma (ln T) sim Delta T/T = 20-50%. We introduce a novel method to search for reionization-induced temperature fluctuations in the IGM by using the effective optical depths of the Lyman-alpha forest towards a large number of background quasars. Higher IGM temperatures give rise to lower effective optical depths in the Lyman-alpha forest, implying that temperature fluctuations will broaden the observed optical depth distribution. We measured the distributions of effective Lyman-alpha forest optical depths across 71 X-Shooter spectra from the XQ-100 survey in four redshift bins from z=3.76 to z=4.19 and compared them to a large-volume cosmological hydrodynamical simulation. A good agreement is found between the observations and the simulation, which does not include temperature fluctuations; therefore, we do not detect a signature of helium reionization. We then post-process the simulations to include an increasing amount of temperature fluctuations until the model becomes inconsistent with the observations. We obtain tight constraints on sigma (ln T) < 0.29 (<0.40) at 2 sigma (3 sigma) at z=3.76 when averaging over scales of 100 comoving Mpc, and weaker constraints for higher redshifts and smaller scales. Our constraints are the tightest to date, and imply that either the IGM temperature contrast caused by helium reionization is less than sim30%, or that the process has not yet significantly started at z=3.76.

Stars on the AGB can exhibit acoustic pulsation modes of different radial orders, along with non-radial modes. These pulsations are essential to the mass-loss process and influence the evolutionary pathways of AGB stars. P-L relations serve as a valuable diagnostic for understanding stellar evolution along the AGB. 3D RHD simulations provide a powerful tool for investigating pulsation phenomena driven by convective processes and their non-linear coupling with stellar oscillations. We investigate multi-mode pulsations in AGB stars using advanced 3D 'star-in-a-box' simulations with the CO5BOLD code. Signatures of these multi-mode pulsations were weak in our previous 3D models. Our focus is on identifying and characterising the various pulsation modes, examining their persistence and transitions, and comparing the results with 1D model predictions and observational data where applicable. We produced a new model grid comprising AGB stars with current masses of 0.7, 0.8, and 1,M_{odot}. Fourier analysis was applied to dynamic, time-dependent quantities to extract dominant pulsation modes and their corresponding periods. Additionally, wavelet transforms were employed to identify mode-switching behaviour over time. The models successfully reproduce the P-L sequences found in AGB stars. Mode-switching phenomena are found in both the models and wavelet analyses of observational data, allowing us to infer similarities in the underlying pulsation dynamics. These 3D simulations highlight the natural emergence of multi-mode pulsations, including both radial and non-radial modes, driven by the self-consistent interplay of convection and oscillations. Our findings underscore the value of 3D RHD models in capturing the non-linear behaviour of AGB pulsations, providing insights into mode switching, envelope structures, and potential links to episodic mass-loss events.

We present an open-source Python library for simulating two-dimensional incompressible Kelvin-Helmholtz instabilities in stratified shear flows. The solver employs a fractional-step projection method with spectral Poisson solution via Fast Sine Transform, achieving second-order spatial accuracy. Implementation leverages NumPy, SciPy, and Numba JIT compilation for efficient computation. Four canonical test cases explore Reynolds numbers 1000--5000 and Richardson numbers 0.1--0.3: classical shear layer, double shear configuration, rotating flow, and forced turbulence. Statistical analysis using Shannon entropy and complexity indices reveals that double shear layers achieve 2.8times higher mixing rates than forced turbulence despite lower Reynolds numbers. The solver runs efficiently on standard desktop hardware, with 384times192 grid simulations completing in approximately 31 minutes. Results demonstrate that mixing efficiency depends on instability generation pathways rather than intensity measures alone, challenging Richardson number-based parameterizations and suggesting refinements for subgrid-scale representation in climate models.

Based on their cosmological origin, the stars of a galaxy can be divided into two categories: those that enter through merger events (ex situ) and those born in the main progenitor (in situ). We used the TNG50 cosmological magnetohydrodynamical simulation and its Lagrangian tracer particles to explore and quantify the origin of gas that ultimately forms the in situ stars of galaxies. We tracked back the baryonic mass contributing to the z=0 in situ stellar populations of galaxies, studying trends in mass from dwarfs to group-scale halos. We find that more massive halos acquire this matter earlier than lower-mass halos, reflecting an overall earlier assembly of their in situ stellar mass. Defining the Lagrangian half-mass radius R_{rm L, 1/2} of a galaxy as the distance containing half of the mass that will form its in situ stars by z=0, we find that R_{rm L, 1/2} is larger for more massive halos at early times, reflecting larger "in situ Lagrangian regions." However, the dependence of this radius on halo mass becomes flat at z simeq 3 and then inverts toward z=0. In addition, R_{rm L, 1/2} increases rapidly with redshift, surpassing the virial radii of halos at z sim 2. This marks the cosmic epoch at which most of the gas that eventually forms the in situ stars of galaxies leaves the intergalactic medium (IGM) and enters halos, a transition that occurs earlier for more massive halos. The formation redshift of the in situ stellar component increases with halo mass, while the formation redshift of the dark matter halo decreases, indicative of a differential assembly history between these two components. Finally, we decomposed the z=0 in situ stellar mass into its distinct modes of accretion. Smooth accretion from the IGM is the most important for low-mass galaxies, while mergers and satellite-stripped gas become relevant and even dominant only for high-mass galaxies.

In astrophysical and cosmological analyses, the increasing quality and volume of astronomical data demand efficient and precise computational tools. This work introduces a novel adaptive algorithm for automatic knots (AutoKnots) allocation in spline interpolation, designed to meet user-defined precision requirements. Unlike traditional methods that rely on manually configured knot distributions with numerous parameters, the proposed technique automatically determines the optimal number and placement of knots based on interpolation error criteria. This simplifies configuration, often requiring only a single parameter. The algorithm progressively improves the interpolation by adaptively sampling the function-to-be-approximated, f(x), in regions where the interpolation error exceeds the desired threshold. All function evaluations contribute directly to the final approximation, ensuring efficiency. While each resampling step involves recomputing the interpolation table, this process is highly optimized and usually computationally negligible compared to the cost of evaluating f(x). We show the algorithm's efficacy through a series of precision tests on different functions. However, the study underscores the necessity for caution when dealing with certain function types, notably those featuring plateaus. To address this challenge, a heuristic enhancement is incorporated, improving accuracy in flat regions. This algorithm has been extensively used and tested over the years. NumCosmo includes a comprehensive set of unit tests that rigorously evaluate the algorithm both directly and indirectly, underscoring its robustness and reliability. As a practical application, we compute the surface mass density Sigma(R) and the average surface mass density Sigma(<R) for Navarro-Frenk-White and Hernquist halo density profiles, which provide analytical benchmarks. (abridged)

Modern techniques for physical simulations rely on numerical schemes and mesh-refinement methods to address trade-offs between precision and complexity, but these handcrafted solutions are tedious and require high computational power. Data-driven methods based on large-scale machine learning promise high adaptivity by integrating long-range dependencies more directly and efficiently. In this work, we focus on fluid dynamics and address the shortcomings of a large part of the literature, which are based on fixed support for computations and predictions in the form of regular or irregular grids. We propose a novel setup to perform predictions in a continuous spatial and temporal domain while being trained on sparse observations. We formulate the task as a double observation problem and propose a solution with two interlinked dynamical systems defined on, respectively, the sparse positions and the continuous domain, which allows to forecast and interpolate a solution from the initial condition. Our practical implementation involves recurrent GNNs and a spatio-temporal attention observer capable of interpolating the solution at arbitrary locations. Our model not only generalizes to new initial conditions (as standard auto-regressive models do) but also performs evaluation at arbitrary space and time locations. We evaluate on three standard datasets in fluid dynamics and compare to strong baselines, which are outperformed both in classical settings and in the extended new task requiring continuous predictions.

A faster than real time forecast system for solar wind and interplanetary magnetic field transients that is driven by hourly updated solar magnetograms is proposed to provide a continuous nowcast of the solar corona (<0.1AU) and 24-hours forecast of the solar wind at 1 AU by solving a full 3-D MHD model. This new model has been inspired by the concept of relativity of simultaneity used in the theory of special relativity. It is based on time transformation between two coordinate systems: the solar rest frame and a boosted system in which the current observations of the solar magnetic field and tomorrow's measurement of the solar wind at 1 AU are simultaneous. In this paper we derive the modified governing equations for both hydrodynamics (HD) and magnetohydrodynamics (MHD) and present a new numerical algorithm that only modifies the conserved quantities but preserves the original HD/MHD numerical flux. The proposed method enables an efficient numerical implementation, and thus a significantly longer forecast time than the traditional method.

Using the First Light And Reionisation Epoch Simulations ({rm F{small LARES}}), a suite of hydrodynamical simulations we explore the consequences of a realistic model for star--dust geometry on the observed properties of galaxies. We find that the UV attenuation declines rapidly from the central regions of galaxies, and bright galaxies have spatially extended star formation that suffers less obscuration than their fainter counterparts, demonstrating a non-linear relationship between the UV luminosity and the UV attenuation, giving a double power-law shape to the UVLF. Spatially distinct stellar populations within galaxies experience a wide range of dust attenuation due to variations in the dust optical depth along their line-of-sight; which can range from completely dust obscured to being fully unobscured. The overall attenuation curve of a galaxy is then a complex combination of various lines-of-sight within the galaxy. We explore the manifestation of this effect to study the reliability of line ratios to infer galaxy properties, in particular the Balmer decrement and the BPT diagram. We find the Balmer decrement predicted Balmer line attenuation to be higher (factor of 1 to gtrsim10) than expected from commonly used attenuation curves. The observed BPT line ratios deviate from their intrinsic values (median difference of 0.08 (0.02) and standard deviation of 0.2 (0.05) for log_{10}([N{small II}]lambda 6585/H_{alpha}) (log_{10}([O{small III}]lambda 5008/H_{beta})). Finally, we explore the variation in observed properties (UV attenuation, UV slope and Balmer decrement) with viewing angle, finding average differences of sim0.3 magnitudes in the UV attenuation.

Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Given that it is infeasible to directly train a model on 'whole' images from domains with potential gigapixel sizes, diffusion-based generative methods have focused on synthesizing small, fixed-size patches extracted from these images. However, generating small patches has limited applicability since patch-based models fail to capture the global structures and wider context of large images, which can be crucial for synthesizing (semantically) accurate samples. To overcome this limitation, we present ZoomLDM, a diffusion model tailored for generating images across multiple scales. Central to our approach is a novel magnification-aware conditioning mechanism that utilizes self-supervised learning (SSL) embeddings and allows the diffusion model to synthesize images at different 'zoom' levels, i.e., fixed-size patches extracted from large images at varying scales. ZoomLDM synthesizes coherent histopathology images that remain contextually accurate and detailed at different zoom levels, achieving state-of-the-art image generation quality across all scales and excelling in the data-scarce setting of generating thumbnails of entire large images. The multi-scale nature of ZoomLDM unlocks additional capabilities in large image generation, enabling computationally tractable and globally coherent image synthesis up to 4096 times 4096 pixels and 4times super-resolution. Additionally, multi-scale features extracted from ZoomLDM are highly effective in multiple instance learning experiments.

The dynamical realisation of the equation of state p +rho =0 is studied. A non-pathological dynamics for the perturbations of such a system mimicking a dynamical cosmological constant (DCC) requires to go beyond the perfect fluid paradigm. It is shown that an anisotropic stress must be always present. The Hamiltonian of the system in isolation resembles the one of a Pais-Uhlenbeck oscillator and linear stability requires that it cannot be positive definite. The dynamics of linear cosmological perturbations in a DCC dominated Universe is studied in detail showing that when DCC is minimally coupled to gravity no dramatic instability is present. In contrast to what happens in a cosmological constant dominated Universe, the non-relativistic matter contrast is no longer constant and exhibits an oscillator behaviour at small scales while it grows weakly at large scales. In the gravitational waves sector, at small scales, the amplitude is still suppressed as the inverse power of the scale factor while it grows logarithmically at large scales. Also the vector modes propagate, though no growing mode is found.

The dynamics of Einstein-conformally coupled Higgs field (EccH) system is investigated near the initial singularities in the presence of Friedman-Robertson--Walker symmetries. We solve the field equations asymptotically up to fourth order near the singularities analytically, and determine the solutions numerically as well. We found all the asymptotic, power series singular solutions, which are (1) solutions with a scalar polynomial curvature singularity but the Higgs field is bounded (`Small Bang'), or (2) solutions with a Milne type singularity with bounded spacetime curvature and Higgs field, or (3) solutions with a scalar polynomial curvature singularity and diverging Higgs field (`Big Bang'). Thus, in the present EccH model there is a new kind of physical spacetime singularity (`Small Bang'). We also show that, in a neighbourhood of the singularity in these solutions, the Higgs sector does not have any symmetry breaking instantaneous vacuum state, and hence then the Brout-Englert-Higgs mechanism does not work. The large scale behaviour of the solutions is investigated numerically as well. In particular, the numerical calculations indicate that there are singular solutions that cannot be approximated by power series.

Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have been proposed to address this problem. In this paper, we explore in detail a new approach based on kernel regression estimates, which is able to estimate a single time delay given several datasets for the same quasar. We develop realistic artificial data sets in order to carry out controlled experiments to test of performance of this new approach. We also test our method on real data from strongly lensed quasar Q0957+561 and compare our estimates against existing results.

Through gravitational lensing, galaxy clusters can magnify supernovae (SNe) and create multiple images of the same SN. This enables measurements of cosmological parameters, which will be increasingly important in light of upcoming telescopic surveys. We study the prospects of detecting strongly lensed SNe in cluster fields with the Nancy Grace Roman Space Telescope (Roman)'s High Latitude Time Domain Survey (HLTDS) and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We employed two approaches: one focusing on known multiply imaged galaxies behind clusters, along with the SN rates specific to those galaxies, and another based on the expected number of lensed SNe exploding in a given volume behind a galaxy cluster. We collected all the clusters in the literature that feature a well-constrained lens model and multiply imaged galaxies behind clusters with high-quality data for the lensed galaxies. This allowed us to determine the SN rate for each galaxy. We provide predictions for 46 clusters visible to the Vera C. Rubin Observatory, as well as for 9 observable by Roman's HLTDS, depending on whether the clusters fall within the survey's observing field. We predict that the number of multiply imaged SNe discovered by LSST in its first three years is 3.95 pm 0.89 from the first approach or 4.94 pm 1.02 from the second. For the HLTDS, the expected number of multiply imaged SNe ranges from 0.38 pm 0.15 to 5.2 pm 2.2, depending on the specific cluster observed, however, the fields to be targeted remain a matter of discussion. We conclude that LSST offers great prospects for detecting multiply imaged SNe. Our predictions are effectively lower limits, as we only considered the most massive and well-studied clusters. We provide a recommendation for HLTDS observing field selection, namely: either MACS J0553.4-3342 or Abell 1758a should be observed by the survey.

In this study of the `Resolving supermAssive Black hole Binaries In galacTic hydrodynamical Simulations' (RABBITS) series, we investigate the orbital evolution of supermassive black holes (SMBHs) during galaxy mergers. We simulate both disc and elliptical galaxy mergers using the KETJU code, which can simultaneously follow galaxy (hydro-)dynamics and small-scale SMBH dynamics with post-Newtonian corrections. With our SMBH binary subgrid model, we show how active galactic nuclei (AGNs) feedback affects galaxy properties and SMBH coalescence. We find that simulations without AGN feedback exhibit excessive star formation, resulting in merger remnants that deviate from observed properties. Kinetic AGN feedback proves more effective than thermal AGN feedback in expelling gas from the centre and quenching star formation. The different central galaxy properties, which are a result of distinct AGN feedback models, lead to varying rates of SMBH orbital decay. In the dynamical friction phase, galaxies with higher star formation and higher SMBH masses possess denser centres, become more resistant to tidal stripping, experience greater dynamical friction, and consequently form SMBH binaries earlier. As AGN feedback reduces gas densities in the centres, dynamical friction by stars dominates over gas. In the SMBH hardening phase, compared to elliptical mergers, disc mergers exhibit higher central densities of newly formed stars, resulting in accelerated SMBH hardening and shorter merger time-scales (i.e. lesssim 500 Myr versus gtrsim 1 Gyr). Our findings highlight the importance of AGN feedback and its numerical implementation in understanding the SMBH coalescing process, a key focus for low-frequency gravitational wave observatories.

We present the photometric properties of galaxies in the First Light and Reionisation Epoch Simulations (FLARES). The simulations trace the evolution of galaxies in a range of overdensities through the Epoch of Reionistion (EoR). With a novel weighting scheme we combine these overdensities, extending significantly the dynamic range of observed composite distribution functions compared to periodic simulation boxes. FLARES predicts a significantly larger number of intrinsically bright galaxies, which can be explained through a simple model linking dust-attenuation to the metal content of the interstellar medium, using a line-of-sight (LOS) extinction model. With this model we present the photometric properties of the FLARES galaxies for z in [5,10]. We show that the ultraviolet (UV) luminosity function (LF) matches the observations at all redshifts. The function is fit by Schechter and double power-law forms, with the latter being favoured at these redshifts by the FLARES composite UV LF. We also present predictions for the UV continuum slope as well as the attenuation in the UV. The impact of environment on the UV LF is also explored, with the brightest galaxies forming in the densest environments. We then present the line luminosity and equivalent widths of some prominent nebular emission lines arising from the galaxies, finding rough agreement with available observations. We also look at the relative contribution of obscured and unobscured star formation, finding comparable contributions at these redshifts.

In the FLARES (First Light And Reionisation Epoch Simulations) suite of hydrodynamical simulations, we find the high redshift (z>5) intrinsic size-luminosity relation is, surprisingly, negatively sloped. However, after including the effects of dust attenuation we find a positively sloped UV observed size-luminosity relation in good agreement with other simulated and observational studies. In this work, we extend this analysis to probe the underlying physical mechanisms driving the formation and evolution of the compact galaxies driving the negative size-mass/size-luminosity relation. We find the majority of compact galaxies (R_{1/2, star}< 1 pkpc), which drive the negative slope of the size-mass relation, have transitioned from extended to compact sizes via efficient centralised cooling, resulting in high specific star formation rates in their cores. These compact stellar systems are enshrouded by non-star forming gas distributions as much as 100times larger than their stellar counterparts. By comparing with galaxies from the EAGLE simulation suite, we find that these extended gas distributions `turn on' and begin to form stars between z=5 and z=0 leading to increasing sizes, and thus the evolution of the size-mass relation from a negative to a positive slope. This explicitly demonstrates the process of inside-out galaxy formation in which compact bulges form earlier than the surrounding discs.

In this article, we consider a newly proposed parameterization of the viscosity coefficient zeta, specifically zeta=zeta_0 {Omega^s_m} H , where zeta_0 = zeta_0{{Omega^s_{m_0}}} within the coincident f(Q) gravity formalism. We consider a non-linear function f(Q)= -Q +alpha Q^n, where alpha and n are arbitrary model parameters, which is a power-law correction to the STEGR scenario. We find an autonomous system by invoking the dimensionless density parameters as the governing phase-space variables. We discuss the physical significance of the model corresponding to the parameter choices n=-1 and n=2 along with the exponent choices s=0, 0.5, and 1.05. We find that model I shows the stable de-Sitter type or stable phantom type (depending on the choice of exponent s) behavior with no transition epoch, whereas model II shows the evolutionary phase from the radiation epoch to the accelerated de-Sitter epoch via passing through the matter-dominated epoch. Hence, we conclude that model I provides a good description of the late-time cosmology but fails to describe the transition epoch, whereas model II modifies the description in the context of the early universe and provides a good description of the matter and radiation era along with the transition phase.

A major outstanding challenge in cosmology is the persistent discrepancy between the Hubble constant obtained from early and late universe measurements -- the Hubble tension. Examining cosmological evolution through the lens of information growth within a black hole we show the appearence of two fractal growing processes characterizing the early and late ages. These fractals induce space growth rates of 62.79pm5.59 km/s/Mpc and 70.07pm0.09 km/s/Mpc; close to the current values of the Hubble constants involved in the tension. These results strongly suggest that the Hubble tension is not given by unexpected large-scale structures or multiple, unrelated errors but by innate properties underlying the universe dynamics.

Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics. Classic numerical solvers have traditionally been computationally expensive and challenging to use in inverse problems, whereas Neural solvers aim to address both concerns through machine learning. We propose a general formulation for continuous convolutions using separable basis functions as a superset of existing methods and evaluate a large set of basis functions in the context of (a) a compressible 1D SPH simulation, (b) a weakly compressible 2D SPH simulation, and (c) an incompressible 2D SPH Simulation. We demonstrate that even and odd symmetries included in the basis functions are key aspects of stability and accuracy. Our broad evaluation shows that Fourier-based continuous convolutions outperform all other architectures regarding accuracy and generalization. Finally, using these Fourier-based networks, we show that prior inductive biases, such as window functions, are no longer necessary. An implementation of our approach, as well as complete datasets and solver implementations, is available at https://github.com/tum-pbs/SFBC.

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel and automated machine-learning pipeline called Cavity Detection Tool (CADET), developed to detect and estimate the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra-like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 % at an 89 % true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 % false-positive rate. When applied to real Chandra images, the pipeline recovered 91 out of 100 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered 8 new cavity pairs in atmospheres of early-type galaxies and galaxy clusters (IC4765, NGC533, NGC2300, NGC3091, NGC4073, NGC4125, NGC4472, NGC5129) and a number of potential cavity candidates.

Parameter inference is a crucial task in modern cosmology that requires accurate and fast computational methods to handle the high precision and volume of observational datasets. In this study, we explore a hybrid vision transformer, the Convolution vision Transformer (CvT), which combines the benefits of vision transformers (ViTs) and convolutional neural networks (CNNs). We use this approach to infer the Omega_m and sigma_8 cosmological parameters from simulated dark matter and halo fields. Our experiments indicate that the constraints on Omega_m and sigma_8 obtained using CvT are better than ViT and CNN, using either dark matter or halo fields. For CvT, pretraining on dark matter fields proves advantageous for improving constraints using halo fields compared to training a model from the beginning. However, ViT and CNN do not show these benefits. The CvT is more efficient than ViT since, despite having more parameters, it requires a training time similar to that of ViT and has similar inference times. The code is available at https://github.com/Yash-10/cvt-cosmo-inference/.

The James Webb Space Telescope (JWST) is set to transform many areas of astronomy, one of the most exciting is the expansion of the redshift frontier to z>10. In its first year alone JWST should discover hundreds of galaxies, dwarfing the handful currently known. To prepare for these powerful observational constraints, we use the First Light And Reionisation Epoch (FLARES) simulations to predict the physical and observational properties of the z>10 population of galaxies accessible to JWST. This is the first time such predictions have been made using a hydrodynamical model validated at low redshift. Our predictions at z=10 are broadly in agreement with current observational constraints on the far-UV luminosity function and UV continuum slope beta, though the observational uncertainties are large. We note tension with recent constraints zsim 13 from Harikane et al. 2022 - compared to these constraints, FLARES predicts objects with the same space density should have an order of magnitude lower luminosity, though this is mitigated slightly if dust attenuation is negligible in these systems. Our predictions suggest that in JWST's first cycle alone, around 600 galaxies should be identified at z>10, with the first small samples available at z>13.

In this study of the `Resolving supermAssive Black hole Binaries In galacTic hydrodynamical Simulations' (RABBITS) series, we focus on the hardening and coalescing process of supermassive black hole (SMBH) binaries in galaxy mergers. For simulations including different galaxy formation processes (i.e. gas cooling, star formation, SMBH accretion, stellar and AGN feedback), we systematically control the effect of stochastic eccentricity by fixing it to similar values during the SMBH hardening phase. We find a strong correlation between the SMBH merger time-scales and the presence of nuclear star formation. Throughout the galaxy merging process, gas condenses at the centre due to cooling and tidal torques, leading to nuclear star formation. These recently formed stars, which inherit low angular momenta from the gas, contribute to the loss cone and assist in the SMBH hardening via three-body interactions. Compared to non-radiative hydrodynamical runs, the SMBH merger time-scales measured from the runs including cooling, stellar and SMBH physical processes tend to be shortened by a factor of {sim}1.7. After fixing the eccentricity to the range of e sim 0.6--0.8 during the hardening phase, the simulations with AGN feedback reveal merger time-scales of {sim} 100--500 Myr for disc mergers and {sim} 1--2 Gyr for elliptical mergers. With a semi-analytical approach, we find that the torque interaction between the binary and its circumbinary disc has minimal impact on the shrinking of the binary orbit in our retrograde galaxy merger. Our results are useful in improving the modelling of SMBH merger time-scales and gravitational wave event rates.

The statistical power of weak lensing measurements is principally driven by the number of high redshift galaxies whose shapes are resolved. Conventional wisdom and physical intuition suggest this is optimised by deep imaging at long (red or near IR) wavelengths, to avoid losing redshifted Balmer break and Lyman break galaxies. We use the synthetic Emission Line EL-COSMOS catalogue to simulate lensing observations using different filters, from various altitudes. Here were predict the number of exposures to achieve a target z > 0.3 source density, using off-the-shelf and custom filters. Ground-based observations are easily better at red wavelengths, as (more narrowly) are space-based observations. However, we find that SuperBIT, a diffraction-limited observatory operating in the stratosphere, should instead perform its lensing-quality observations at blue wavelengths.

Gravitational lensing is an invaluable probe of the nature of dark matter, and the structures it forms. Lensed gravitational waves in particular allow for unparalleled sensitivity to small scale structures within the lenses, due to the precise time resolution in combination with the continuous monitoring of the entire sky. In this work, we show two distinct ways of using strongly lensed gravitational waves to identify the presence of dark matter subhalos: {i)} through higher order caustics generating high relative magnification (mu_r > 2), short time delay image pairs that break the caustic universality relations of single dark matter halos, which occur for sim 1-10 percent of strongly lensed events in our cold dark matter models, and ii) through the presence of more than three highly magnified images, which occur for sim 0.01-1 percent of the same simulated events. We find that these results are highly sensitive to the concentrations of subhalos in our simulations, and more mildly to their number densities. The presence of low-mass subhalos increases the probability of observing wave-optics lensing in lensed gravitational waves, which is studied by solving the diffraction integral with the stationary phase approximation, as well as numerically. We also report distinct quantitative and qualitative differences in the distributions of relative magnifications and time delays for subhalo populations with increased number densities or concentrations. With the upcoming detection of strongly lensed events by ground- and space- based detectors, comparisons against these simulated distributions will provide insight into the nature of dark matter.

Aims. We aim to investigate the influence of the distance to filaments and dark-matter haloes on galaxy cold-gas content in the empirical model NeutralUniverseMachine (NUM) and the hydrodynamical simulation IllustrisTNG. Methods. We used DisPerSE to identify cosmic web structures and calculate the distance of galaxies to filaments for both observations and models. We show the results of the HI and H2 mass functions, HI- and H2-halo-mass relations, HI- and H2-stellar-mass relations for galaxies in the NUM model and IllustrisTNG with different distances to filaments and compare them with observational measurements. We also show the evolution of HI and H2 mass densities at different distances to filament bins. Results. We find that how filaments affect the HI gas is generally less significant compared to the halo environment. There is a weak trend in the observations at z=0 that low-mass haloes lying closer to the filaments tend to have reduced HI masses. However, this trend reverses for massive haloes with log(Mvir/Msun) > 12.5. This behaviour is accurately reproduced in the NUM model due to the dependence of HI gas on the halo formation time, but it does not appear in IllustrisTNG. The influence of filaments on the HI gas becomes slightly weaker at higher redshifts and is only significant for galaxies that reside in massive haloes in the NUM model. Filaments have almost no impact on the H2-stellar-mass relation in both models, confirming that H2 is primarily determined by the galaxy stellar mass and star formation rate.

We present a new method for measuring the E_G statistic that combines two CMB secondaries -- the kinematic Sunyaev-Zeldovich (kSZ) effect and CMB lensing -- for the first time to probe gravity on linear scales. The E_G statistic is a discriminating tool for modified gravity theories, which leave imprints in lensing observables and peculiar velocities. Existing E_G measurements rely on redshift space distortions (RSD) to infer the velocity field. Here, we employ kSZ velocity-reconstruction instead of RSD, a complementary technique that constrains the largest-scale modes better than the galaxy survey it uses. We construct a novel V_G estimator that involves a ratio between cross-correlations of a galaxy sample with a CMB convergence map and that with a 3D kSZ-reconstructed velocity field. We forecast for current and upcoming CMB maps from the Atacama Cosmology Telescope (ACT) and the Simons Observatory (SO), respectively, in combination with three spectroscopic galaxy samples from the Dark Energy Spectroscopic Instrument (DESI). We find cumulative detection significances in the range S/N sim 20-55, which can robustly test the scale-independent E_G prediction under general relativity (GR) at different effective redshifts of the galaxy samples (zapprox 0.73, 1.33, 1.84). In particular, the SOtimesDESI LRG measurement would be able to distinguish between GR and certain modified gravity models, including Hu-Sawicki f(R) and Chameleon theories, with high confidence. The proposed V_G estimator opens up a new avenue for stress-testing gravity and the LambdaCDM+GR model at the largest observable scales.

The observed cosmological constant may originate as the minimum value U_{min} of a scalar field potential, where the scalar field is frozen due to a large mass. If this vacuum is metastable, it may decay to a true vacuum either at present or in the future. Assuming its decay rate Gamma is comparable to the Hubble expansion rate H_0, we estimate the scale of true vacuum bubbles and analyze their evolution. We find that their initial formation scale is sub-millimeter and their tension causes rapid collapse if m gtrsim 1.7 cdot 10^{-3}, eV. For smaller masses, the bubbles expand at the speed of light. We extend our analysis to scalar-tensor theories with non-minimal coupling, finding that the nucleation scale of gravitational constant bubbles remains consistent with the sub-millimeter regime of General Relativity. The critical mass scale remains around 10^{-3},eV. A theoretical estimate at redshift z_{obs} sim 0.01 suggests an observable bubble radius of sim 50 Mpc, implying a gravitational transition triggered sim 300 Myr ago, with a present-day size approaching 100 Mpc. Additionally, we explore mass ranges (m < 10^{-3},eV) and non-minimal coupling xi ranges (10^{-8},eV^{2-n} - 10^{-1},eV^{2-n}) that lead to a variation Delta G/G_N within the 1%-7% range. We assume non-minimal coupling of the form F(phi)=1/kappa - xi phi^n, with kappa=8pi G_N and 2 leq n leq 9. Finally, we review various local physics or/and transition based proposed solutions to the Hubble tension, including ultra-late-time transitional models (z sim 0.01), screened fifth-force mechanisms, and the Lambda_{rm s}CDM model, which features a transition at z sim 2. We discuss observational hints supporting these scenarios and the theoretical challenges they face.

We present the new public version of the KETJU supermassive black hole (SMBH) dynamics module, as implemented into GADGET-4. KETJU adds a small region around each SMBH where the dynamics of the SMBHs and stellar particles are integrated using an algorithmically regularised integrator instead of the leapfrog integrator with gravitational softening used by GADGET-4. This enables modelling SMBHs as point particles even during close interactions with stellar particles or other SMBHs, effectively removing the spatial resolution limitation caused by gravitational softening. KETJU also includes post-Newtonian corrections, which allows following the dynamics of SMBH binaries to sub-parsec scales and down to tens of Schwarzschild radii. Systems with multiple SMBHs are also supported, with the code also including the leading non-linear cross terms that appear in the post-Newtonian equations for such systems. We present tests of the code showing that it correctly captures, at sufficient mass resolution, the sinking driven by dynamical friction and binary hardening driven by stellar scattering. We also present an example application demonstrating how the code can be applied to study the dynamics of SMBHs in mergers of multiple galaxies and the effect they have on the properties of the surrounding galaxy. We expect that the presented KETJU SMBH dynamics module can also be straightforwardly incorporated into other codes similar to GADGET-4, which would allow coupling small-scale SMBH dynamics to the rich variety of galactic physics models that exist in the literature.

One of the central challenges in modern cosmology is understanding the nature of dark energy and its evolution throughout the history of the Universe. Dark energy is commonly modeled as a perfect fluid with a time-varying equation-of-state parameter, w(z), often modeled under CPL parametrization using two parameters w_0 and w_a. In this study, we explore both parametric and non-parametric methods to reconstruct the dark energy Equation of State (EoS) using Gravitational Wave (GW) sources, with and without electromagnetic (EM) counterparts called as bright sirens and dark sirens respectively. In the parametric approach, we extend the widely used w_0-w_a model by introducing an additional term, w_b, to better capture the evolving dynamics of dark energy up to high redshift which is accessible from GW sources. This extension provides increased flexibility in modeling the EoS and enables a more detailed investigation of dark energy's evolution. Our analysis indicates that, with five years of observation time and a 75% duty cycle using Cosmic Explorer and the Einstein Telescope, it will be possible to measure the dark energy EoS with remarkable precision better than any other cosmological probes in the coming years from bright standard sirens using multi-messenger avenue. These findings highlight the potential of GW observations in synergy with EM telescopes to offer valuable insights into the nature of dark energy, overcoming the current limitations in cosmological measurements.

Deep wide spectral line surveys with the Square Kilometre Array (SKA) will expand the cosmic frontiers of neutral atomic hydrogen (HI) in galaxies. However, at cosmologically significant redshifts (z gtrsim 0.5), detections will typically be spatially unresolved and limited to the highest mass systems. Gravitational lensing could potentially alleviate these limitations, enabling lower mass systems to be studied at higher redshift and spatially resolved dynamical studies of some HI discs. Additionally, lensed HI systems would select foreground dark matter haloes using a different, more extended baryonic tracer compared to other lens surveys. This may result in a wider selected range of foreground dark matter halo properties, such as the concentration parameter. This paper uses the distortion of the observed HI mass function (HIMF) produced by strong gravitational lensing to find a flux density criterion for selecting lensed HI sources in future SKA-Mid spectral line surveys. This selection approach could yield lensed HI source densities in the range of sim 0.1--10 galaxies per square degree out to a redshift of z simeq 3 covered by SKA-MID Band 1. Although the sample sizes are modest, even with the proposed SKA-Mid surveys, the selection approach is straightforward and should have a 50% efficiency without any additional information, such as low-impact-factor or lower-redshift massive galaxies. The efficiency of selecting high-redshift, neutral-hydrogen-rich, lensed galaxies should then be greatly enhanced by using SKA-MID data in concert with the Vera C. Rubin Large Survey of Space and Time.

Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications. However, integrating with state-of-the-art 3D scene rendering techniques such as Gaussian Splatting (GS) remains challenging. Existing models use additional meshing mechanisms, including triangle or tetrahedron meshing, marching cubes, or cage meshes. Alternatively, we can modify the physics-grounded Newtonian dynamics to align with 3D Gaussian components. Current models take the first-order approximation of a deformation map, which locally approximates the dynamics by linear transformations. In contrast, our GS for Physics-Based Simulations (GASP) pipeline uses parametrized flat Gaussian distributions. Consequently, the problem of modeling Gaussian components using the physics engine is reduced to working with 3D points. In our work, we present additional rules for manipulating Gaussians, demonstrating how to adapt the pipeline to incorporate meshes, control Gaussian sizes during simulations, and enhance simulation efficiency. This is achieved through the Gaussian grouping strategy, which implements hierarchical structuring and enables simulations to be performed exclusively on selected Gaussians. The resulting solution can be integrated into any physics engine that can be treated as a black box. As demonstrated in our studies, the proposed pipeline exhibits superior performance on a diverse range of benchmark datasets designed for 3D object rendering. The project webpage, which includes additional visualizations, can be found at https://waczjoan.github.io/GASP.

Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a so-called "physics-informed deep learning" approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys. We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM), in which a neural network learns appropriate hyperparameters and denoising priors from simulated galaxy images. We characterise the time-performance trade-off of several methods for galaxies of differing brightness levels as well as our method's robustness to systematic PSF errors and network ablations. We show an improvement in reduced shear ellipticity error of 38.6% (SNR=20)/45.0% (SNR=200) compared to classic methods and 7.4% (SNR=20)/33.2% (SNR=200) compared to modern methods.

Primordial Magnetic Fields (PMFs) -- magnetic fields originating in the early Universe and permeating the cosmological scales today -- can explain the observed microGauss-level magnetisation of galaxies and their clusters. In light of current and upcoming all-sky radio surveys, PMFs have drawn attention not only as major candidates for explaining the large-scale magnetisation of the Universe, but also as potential probes of early-Universe physics. In this paper, using cosmological simulations coupled with light-cone analysis, we study for the first time the imprints of the PMF structure on the mean rotation measure (RM) originating in the intergalactic medium (IGM), langle RM_{IGM}rangle. We introduce a new method for producing full-sky RM_{IGM} distributions and analyse the autocorrelation of RM_{IGM} on small and large angular scales; we find that PMF structures indeed show distinct signatures. The large-scale uniform model (characterised by an initially unlimited coherence scale) leads to correlations up to 90 degrees, while correlations for small-scale stochastic PMF models drop by factor of 100 at 0.17, 0.13 and 0.11 degrees angular scales, corresponding to 5.24, 4.03 and 3.52 Mpc scales (at z=2 redshift) for magnetic fields with comoving 3.49, 1.81, 1.00 Mpc/h coherence scales, respectively; the correlation amplitude of the PMF model with comoving sim 19 Mpc/h coherence scale drops only by factor of 10 at 1 degree (30.6 Mpc). These results suggests that improvements in the modelling of Galactic RM will be necessary to investigate the signature of large-scale correlated PMFs. A comparison of langle RM_{IGM}rangle redshift dependence obtained from our simulations with that from the LOFAR Two-metre Sky Survey shows agreement with our previous upper limits' estimates on the PMF strength derived from RM-rms analysis.

We develop a linear perturbative formalism to compute the response of an inhomogeneous stellar disk embedded in a non-responsive dark matter halo to perturbations like bars, spiral arms and satellite galaxy encounters. Without self-gravity to reinforce it, the response of a Fourier mode phase mixes away due to an intrinsic spread in the vertical (Omega_z), radial (Omega_r) and azimuthal (Omega_phi) frequencies, giving rise to local phase-space spirals. Collisional diffusion due to scattering of stars by structures like giant molecular clouds causes super-exponential damping of the phase-spiral amplitude. The z-v_z phase-spiral is 1-armed (2-armed) for vertically anti-symmetric (symmetric) bending (breathing) modes. Only transient perturbations with timescales (tau_{P}) comparable to the vertical oscillation period (tau_z sim 1/Omega_z) trigger z-v_z phase-spirals. Each (n,l,m) mode of the response to impulsive (tau_{P}<tau=1/(nOmega_z+lOmega_r+mOmega_phi)) perturbations is power law (sim tau_{P}/tau) suppressed, but that to adiabatic (tau_{P}>tau) perturbations is exponentially weak (sim left[-left(tau_{mathrm{P}/tauright)^alpharight]}) except resonant (tauto infty) modes. Slower (tau_{P}>tau_z) perturbations, e.g., distant encounters with satellite galaxies, induce stronger bending modes. If the Gaia phase-spiral was triggered by a satellite, Sagittarius is the leading contender as it dominates the Solar neighborhood response of the Milky Way disk to satellite encounters. However, survival against collisional damping necessitates that the impact occurred within sim 0.6-0.7 Gyr ago. We discuss how the detailed galactic potential dictates the phase-spiral shape: phase mixing occurs slower and phase-spirals are less wound in the outer disk and in presence of an ambient halo.

The direct collapse scenario, which predicts the formation of supermassive stars (SMSs) as precursors to supermassive black holes (SMBHs), has been explored primarily under the assumption of metal-free conditions. However, environments exposed to strong far-ultraviolet (FUV) radiation, which is another requirement for the direct collapse, are often chemically enriched to varying degrees. In this study, we perform radiation hydrodynamic simulations of star-cluster formation in clouds with finite metallicities, Z=10^{-6} to 10^{-2} Z_{odot}, incorporating detailed thermal and chemical processes and radiative feedback from forming stars. Extending the simulations to approximately two million years, we demonstrate that SMSs with masses exceeding 10^4~M_odot can form even in metal-enriched clouds with Z lesssim 10^{-3} Z_{odot}. The accretion process in these cases, driven by "super-competitive accretion," preferentially channels gas into central massive stars in spite of small (sub-pc) scale fragmentation. At Z simeq 10^{-2} Z_{odot}, however, enhanced cooling leads to intense fragmentation on larger scales, resulting in the formation of dense star clusters dominated by very massive stars with 10^3 M_{odot} rather than SMSs. These clusters resemble young massive or globular clusters observed in the distant and local universe, exhibiting compact morphologies and high stellar surface densities. Our findings suggest that SMS formation is viable below a metallicity threshold of approximately 10^{-3} Z_{odot}, significantly increasing the number density of massive seed black holes to levels sufficient to account for the ubiquitous SMBHs observed in the local universe. Moreover, above this metallicity, this scenario naturally explains the transition from SMS formation to dense stellar cluster formation.

Galaxy number counts suggest that we are located within the Gpc-scale KBC void. The Hubble tension might arise due to gravitationally driven outflow from this void, as explored in detail by Haslbauer et al. We explore how the impact of the void on redshift decays at large distances. We define H_0(z) as the present expansion rate H_0 that would be inferred from observations in a narrow redshift range centred on z. We find H_0(z) in three different ways, all of which give similar results. We then compare these results with the observations of Jia et al., who were careful to minimise the impact of correlations between H_0 measurements from data in different redshift bins. We find reasonable agreement with their results for the Gaussian and Exponential void underdensity profiles, although the agreement is less good in the Maxwell-Boltzmann case. The latter profile causes severe disagreement with the observed bulk flow curve at z < 0.1 (Mazurenko et al.), so the tension with higher redshift data further highlights that the deepest part of the KBC void is probably near its centre. The observations show a decline of H_0(z) towards the background Planck value in qualitative agreement with the considered models, even if we use a larger void. The good overall agreement with the recent results of Jia et al. suggests that the local supervoid evident from the galaxy luminosity density out to a Gpc might also solve the Hubble tension while retaining a low background H_0 consistent with Planck data, assuming enhanced structure formation on >100 Mpc scales.

The Superpressure Balloon-borne Imaging Telescope (SuperBIT) is a diffraction-limited, wide-field, 0.5 m, near-infrared to near-ultraviolet observatory designed to exploit the stratosphere's space-like conditions. SuperBIT's 2023 science flight will deliver deep, blue imaging of galaxy clusters for gravitational lensing analysis. In preparation, we have developed a weak lensing measurement pipeline with modern algorithms for PSF characterization, shape measurement, and shear calibration. We validate our pipeline and forecast SuperBIT survey properties with simulated galaxy cluster observations in SuperBIT's near-UV and blue bandpasses. We predict imaging depth, galaxy number (source) density, and redshift distribution for observations in SuperBIT's three bluest filters; the effect of lensing sample selections is also considered. We find that in three hours of on-sky integration, SuperBIT can attain a depth of b = 26 mag and a total source density exceeding 40 galaxies per square arcminute. Even with the application of lensing-analysis catalog selections, we find b-band source densities between 25 and 30 galaxies per square arcminute with a median redshift of z = 1.1. Our analysis confirms SuperBIT's capability for weak gravitational lensing measurements in the blue.

We confirm for the first time the existence of distinctive halo bias associated with the quadrupolar type of statistical anisotropy (SA) of the linear matter density field using cosmological N-body simulations. We find that the coefficient of the SA-induced bias for cluster-sized halos takes negative values and exhibits a decreasing trend with increasing halo mass. This results in the quadrupole halo power spectra in a statistically anisotropic universe being less amplified compared to the monopole spectra. The anisotropic feature in halo bias that we found presents a promising new tool for testing the hypothesis of a statistically anisotropic universe, with significant implications for the precise verification of anisotropic inflation scenarios and vector dark matter and dark energy models.

We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter LambdaCDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted "base LambdaCDM" in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density Omega_c h^2 = 0.120pm 0.001, baryon density Omega_b h^2 = 0.0224pm 0.0001, scalar spectral index n_s = 0.965pm 0.004, and optical depth tau = 0.054pm 0.007 (in this abstract we quote 68,% confidence regions on measured parameters and 95,% on upper limits). The angular acoustic scale is measured to 0.03,% precision, with 100theta_*=1.0411pm 0.0003. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-LambdaCDM cosmology, the inferred late-Universe parameters are: Hubble constant H_0 = (67.4pm 0.5)km/s/Mpc; matter density parameter Omega_m = 0.315pm 0.007; and matter fluctuation amplitude sigma_8 = 0.811pm 0.006. We find no compelling evidence for extensions to the base-LambdaCDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be N_{rm eff} = 2.99pm 0.17, and the neutrino mass is tightly constrained to sum m_nu< 0.12eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base -LambdaCDM at over 2,sigma, which pulls some parameters that affect the lensing amplitude away from the base-LambdaCDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. (Abridged)

We conduct an extended analysis of dark energy constraints, in support of the findings of the DESI DR2 cosmology key paper, including DESI data, Planck CMB observations, and three different supernova compilations. Using a broad range of parametric and non-parametric methods, we explore the dark energy phenomenology and find consistent trends across all approaches, in good agreement with the w_0w_aCDM key paper results. Even with the additional flexibility introduced by non-parametric approaches, such as binning and Gaussian Processes, we find that extending LambdaCDM to include a two-parameter w(z) is sufficient to capture the trends present in the data. Finally, we examine three dark energy classes with distinct dynamics, including quintessence scenarios satisfying w geq -1, to explore what underlying physics can explain such deviations. The current data indicate a clear preference for models that feature a phantom crossing; although alternatives lacking this feature are disfavored, they cannot yet be ruled out. Our analysis confirms that the evidence for dynamical dark energy, particularly at low redshift (z lesssim 0.3), is robust and stable under different modeling choices.

In this paper, we study the existence and uniqueness of solutions to the Euler equations with initial conditions that exhibit analytic regularity near the boundary and Sobolev regularity away from it. A key contribution of this work is the introduction of the diamond-analyticity framework, which captures the spatial decay of the analyticity radius in a structured manner, improving upon uniform analyticity approaches. We employ the Leray projection and a nonstandard mollification technique to demonstrate that the quotient between the imaginary and real parts of the analyticity radius remains unrestricted, thus extending the analyticity persistence results beyond traditional constraints. Our methodology combines analytic-Sobolev estimates with an iterative scheme which is nonstandard in the Cauchy-Kowalevskaya framework, ensuring rigorous control over the evolution of the solution. These results contribute to a deeper understanding of the interplay between analyticity and boundary effects in fluid equations. They might have implications for the study of the inviscid limit of the Navier-Stokes equations and the role of complex singularities in fluid dynamics.

Strong gravitational lensing of variable sources, such as quasars or supernovae, can be used to constrain cosmological parameters through a technique known as "time-delay cosmography''. Competitive constraints on the Hubble constant have been achieved with electromagnetic observations of lensed quasars and lensed supernovae. Gravitational wave (GW) astronomy may open up a new channel for time-delay cosmography with GW signal replacing the electromagnetic (EM) one. We highlight the similarities of using GW signals to be applied to time-delay cosmography compared to EM signal. We then discuss key differences between GW and EM signals and their resulting advantages and inconveniences from the angle of the current state-of-the-art using quasars and lensed supernovae for time-delay cosmography. We identify the astrometric precision requirement of the images as a key challenge to overcome and highlight the potentially significant impact that near-perfect time-delay measurements of lensed GWs can bring to the table.

How momentum, energy, and magnetic fields are transported in the presence of macroscopic gradients is a fundamental question in plasma physics. Answering this question is especially challenging for weakly collisional, magnetized plasmas, where macroscopic gradients influence the plasma's microphysical structure. In this paper, we introduce thermodynamic forcing, a new method for systematically modeling how macroscopic gradients in magnetized or unmagnetized plasmas shape the distribution functions of constituent particles. In this method, we propose to apply an anomalous force to those particles inducing the anisotropy that would naturally emerge due to macroscopic gradients in weakly collisional plasmas. We implement thermodynamic forcing in particle-in-cell (TF-PIC) simulations using a modified Vay particle pusher and validate it against analytic solutions of the equations of motion. We then carry out a series of simulations of electron-proton plasmas with periodic boundary conditions using TF-PIC. First, we confirm that the properties of two electron-scale kinetic instabilities -- one driven by a temperature gradient and the other by pressure anisotropy -- are consistent with previous results. Then, we demonstrate that in the presence of multiple macroscopic gradients, the saturated state can differ significantly from current expectations. This work enables, for the first time, systematic and self-consistent transport modeling in weakly collisional plasmas, with broad applications in astrophysics, laser-plasma physics, and inertial confinement fusion.

We present new time delays, the main ingredient of time delay cosmography, for 22 lensed quasars resulting from high-cadence r-band monitoring on the 2.6 m ESO VLT Survey Telescope and Max-Planck-Gesellschaft 2.2 m telescope. Each lensed quasar was typically monitored for one to four seasons, often shared between the two telescopes to mitigate the interruptions forced by the COVID-19 pandemic. The sample of targets consists of 19 quadruply and 3 doubly imaged quasars, which received a total of 1 918 hours of on-sky time split into 21 581 wide-field frames, each 320 seconds long. In a given field, the 5-{\sigma} depth of the combined exposures typically reaches the 27th magnitude, while that of single visits is 24.5 mag - similar to the expected depth of the upcoming Vera-Rubin LSST. The fluxes of the different lensed images of the targets were reliably de-blended, providing not only light curves with photometric precision down to the photon noise limit, but also high-resolution models of the targets whose features and astrometry were systematically confirmed in Hubble Space Telescope imaging. This was made possible thanks to a new photometric pipeline, lightcurver, and the forward modelling method STARRED. Finally, the time delays between pairs of curves and their uncertainties were estimated, taking into account the degeneracy due to microlensing, and for the first time the full covariance matrices of the delay pairs are provided. Of note, this survey, with 13 square degrees, has applications beyond that of time delays, such as the study of the structure function of the multiple high-redshift quasars present in the footprint at a new high in terms of both depth and frequency. The reduced images will be available through the European Southern Observatory Science Portal.

We demonstrate that violent kinetic preheating following inflation can lead to the formation of black holes in the early Universe. In alpha-attractor models with derivative inflaton couplings, nonlinear amplification of field fluctuations drives large spacetime curvature and gravitational collapse shortly after inflation ends. Using fully general-relativistic lattice simulations, we find that these dynamics produce black holes with masses of order tens of grams at sub-horizon scales, without requiring large primordial curvature perturbations. Although such micro-black holes evaporate rapidly via Hawking radiation, their formation modifies the post-inflationary equation of state and their evaporation can successfully reheat the Universe before Big Bang nucleosynthesis. These results identify kinetic preheating as a new, efficient channel for black-hole production and establish a direct connection between inflationary symmetries and strong-gravity phenomena at reheating.

JWST/NIRCam slitless spectroscopy enables dynamical mass measurements for typical star-forming galaxies only a billion years after the Big Bang. We model the Halpha morpho-kinematics of 163 galaxies at redshift zapprox4-6 from FRESCO and CONGRESS (with JADES imaging), using the geko code, and infer rotational velocities and dispersions within r_{rm e}. Our sample spans log M_{star}approx7-10 and log M_{rm dyn}approx9-11. Gas masses are estimated via scaling relations, yielding baryonic masses and dark-matter (DM) fractions f_{rm DM}(r<r_{rm e}) within the Halpha half-light radius. We find high median fractions of langle f_{rm gas}rangle=0.77 and langle f_{rm DM}rangle=0.73, where f_{rm gas} is measured with respect to the baryonic mass and f_{rm DM} with respect to the DM+baryonic mass. About two-thirds of systems are DM-dominated within r_{rm e}sim0.5-1 kpc. Both f_{rm gas} and f_{rm DM} decrease with stellar mass, consistent with simulations. The stellar Tully-Fisher relation shows a tentative offset to higher v_{rm circ} at fixed M_{star} and substantial intrinsic scatter, suggesting that the relation is only beginning to emerge at zsim5. We measure a negative correlation between f_{rm DM} and baryonic surface density Sigma_{rm bar}, weaker but broadly consistent with trends at cosmic noon and at zsim0. Qualitatively comparing with modified NFW profiles coupled to an empirical stellar-to-halo mass relation suggests that the lowest f_{rm DM} (lesssim0.4) require cored inner DM profiles, while the highest fractions favour cuspier profiles, potentially reflecting adiabatic contraction. Overall, the elevated f_{rm gas} and f_{rm DM} at zgtrsim4 are compatible with progenitors of baryon-dominated systems at zsim2 and naturally anticipate overmassive black holes at fixed M_{star}.

Cosmic hot-gas emission is closely related to halo gas acquisition and galactic feedback processes. Their X-ray observations reveal important physical properties and movements of the baryonic cycle of galactic ecosystems. However, the measured emissions toward a target at a cosmological distance would always include contributions from hot gases along the entire line of sight to the target. Observationally, such contaminations are routinely subtracted via different strategies. With this work, we aim to answer an interesting theoretical question regarding the amount of soft X-ray line emissions from intervening hot gases of different origins. We tackled this problem with the aid of the TNG100 simulation. We generated typical wide-field light cones and estimated their impacts on spectral and flux measurements toward X-ray-emitting galaxy-, group- and cluster-halo targets at lower redshifts. We split the intervening hot gases into three categories; that is, the hot gas that is gravitationally bound to either star-forming or quenched galaxy halos, and the diffuse gas, which is more tenuously distributed permeating the cosmic web structures. We find that along a given line of sight, the diffuse gas that permeates the cosmic web structures produces strong oxygen and iron line emissions at different redshifts. The diffuse gas emission in the soft X-ray band can be equal to the emission from hot gases that are gravitationally bound to intervening galaxy halos. The hot-gas emission from the quiescent galaxy halos can be significantly less than that from star-forming halos along the line of sight. The fluxes from all of the line-of-sight emitters as measured in the energy band of 0.4--0.85 keV can reach ~20--200 % of the emission from the target galaxy, group, and cluster halos.

Reconstructing subsurface ocean dynamics, such as vertical velocity fields, from incomplete surface observations poses a critical challenge in Earth science, a field long hampered by the lack of standardized, analysis-ready benchmarks. To systematically address this issue and catalyze research, we first build and release KD48, a high-resolution ocean dynamics benchmark derived from petascale simulations and curated with expert-driven denoising. Building on this benchmark, we introduce VISION, a novel reconstruction paradigm based on Dynamic Prompting designed to tackle the core problem of missing data in real-world observations. The essence of VISION lies in its ability to generate a visual prompt on-the-fly from any available subset of observations, which encodes both data availability and the ocean's physical state. More importantly, we design a State-conditioned Prompting module that efficiently injects this prompt into a universal backbone, endowed with geometry- and scale-aware operators, to guide its adaptive adjustment of computational strategies. This mechanism enables VISION to precisely handle the challenges posed by varying input combinations. Extensive experiments on the KD48 benchmark demonstrate that VISION not only substantially outperforms state-of-the-art models but also exhibits strong generalization under extreme data missing scenarios. By providing a high-quality benchmark and a robust model, our work establishes a solid infrastructure for ocean science research under data uncertainty. Our codes are available at: https://github.com/YuanGao-YG/VISION.

We present griz light curves of 251 Type Ia Supernovae (SNe Ia) from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis (Brout et al. 2018-SYS, DES Collaboration et al. 2018) by employing a scene modeling approach that simultaneously forward models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.

The angular power spectrum is a gauge-independent observable that is in principle the natural tool for analysing galaxy number counts. In practice, the problem is that the computational requirements for next-generation spectroscopic surveys such as Euclid and the Square Kilometre Array are currently unfeasible. We propose a new method to save computational time for spectroscopic angular power spectra. This hybrid method is modelled on the Fourier power spectrum approach of treating relatively thick redshift bins (redshift width ~0.1) as separate surveys. In the hybrid method, each thick bin is further subdivided into thin bins (redshift width ~0.01); all the correlations within each thick bin are computed, while cross-bin correlations beyond the thick bins are neglected. Constraints on cosmological parameters from the hybrid method are comparable to those from the standard galaxy power spectrum analysis - but they have the advantage that cosmic evolution, wide-angle and lensing effects are naturally included, while no Alcock-Paczynski correction is needed. The hybrid method delivers much tighter constraints than a 2D tomographic approach that is typical for photometric surveys, which considers only thick bins and the correlations between them. Furthermore, for standard cosmological parameters our method is not biased by neglecting the effects of lensing on number counts, while the tomographic method is strongly biased.

Here we present 1701 light curves of 1550 spectroscopically confirmed Type Ia supernovae (SNe Ia) that will be used to infer cosmological parameters as part of the Pantheon+ SN analysis and the SH0ES (Supernovae and H0 for the Equation of State of dark energy) distance-ladder analysis. This effort is one part of a series of works that perform an extensive review of redshifts, peculiar velocities, photometric calibration, and intrinsic-scatter models of SNe Ia. The total number of light curves, which are compiled across 18 different surveys, is a significant increase from the first Pantheon analysis (1048 SNe), particularly at low redshift (z). Furthermore, unlike in the Pantheon analysis, we include light curves for SNe with z<0.01 such that SN systematic covariance can be included in a joint measurement of the Hubble constant (H_0) and the dark energy equation-of-state parameter (w). We use the large sample to compare properties of 151 SNe Ia observed by multiple surveys and 12 pairs/triplets of "SN siblings" - SNe found in the same host galaxy. Distance measurements, application of bias corrections, and inference of cosmological parameters are discussed in the companion paper by Brout et al. (2022b), and the determination of H_0 is discussed by Riess et al. (2022). These analyses will measure w with sim3% precision and H_0 with 1 km/s/Mpc precision.

In this paper, we develop and implement an efficient asymptotic-preserving (AP) scheme to solve the gas mixture of Boltzmann equations under the disparate mass scaling relevant to the so-called "epochal relaxation" phenomenon. The disparity in molecular masses, ranging across several orders of magnitude, leads to significant challenges in both the evaluation of collision operators and the designing of time-stepping schemes to capture the multi-scale nature of the dynamics. A direct implementation of the spectral method faces prohibitive computational costs as the mass ratio increases due to the need to resolve vastly different thermal velocities. Unlike [I. M. Gamba, S. Jin, and L. Liu, Commun. Math. Sci., 17 (2019), pp. 1257-1289], we propose an alternative approach based on proper truncation of asymptotic expansions of the collision operators, which significantly reduces the computational complexity and works well for small varepsilon. By incorporating the separation of three time scales in the model's relaxation process [P. Degond and B. Lucquin-Desreux, Math. Models Methods Appl. Sci., 6 (1996), pp. 405-436], we design an AP scheme that captures the specific dynamics of the disparate mass model while maintaining computational efficiency. Numerical experiments demonstrate the effectiveness of the proposed scheme in handling large mass ratios of heavy and light species, as well as capturing the epochal relaxation phenomenon.

Methods. We utilized the combined five SRG/eROSITA All-Sky Survey data (eRASS:5) to perform X-ray imaging and spectral analyses of the Centaurus cluster in various directions to large radii. Surface brightness (SB) profiles out to 2R_{200} were constructed. We acquired gas temperature, metallicity, and normalization per area profiles out to R_{200}. We compared our results with previous Centaurus studies, cluster outskirts measurements, and simulations. Comprehensive sky background analysis was done across the FoV, in particular, to assess the variation of the eROSITA Bubble emission that partially contaminates the field. Results. The processed X-ray images show the known sloshing-induced structures in the core. The core (rleq11~kpc) is better described with a 2T model than a 1T model. Here, we measured lower T from the cooler component (~1.0 keV) and higher Z (sim!1.6Z_odot), signifying an iron bias. In the intermediate radial range, we observed prominent SB and normalization per area excesses in the eastern sector (Cen 45 location), reaching out to R_{500}. Temperature enhancements near the location of Cen 45 imply that the gas is shock-heated due to the interaction with Cen 30, the significant excess behind Cen 45 center might be the tail/ram-pressure-stripped gas. We found good agreement between the outskirt temperatures with the profile from simulations and fit from Suzaku outskirts measurements. We detected significant SB emission to the sky background level out to R_{200} with a 3.5sigma and followed by 2.9sigma at 1.1R_{200}. The metallicity at R_{500}-R_{200} is low but within the ranges of other outskirts studies. Conclusions. We present the first measurement of ICM morphology and properties of Centaurus cluster sampling the whole azimuth beyond 30', increasing the probed volume by a factor of almost 30.

Passive galaxies are ubiquitous in the local universe, and various physical channels have been proposed that lead to this passivity. To date, robust passive galaxy candidates have been detected up to z leqslant 5, but it is still unknown if they exist at higher redshifts, what their relative abundances are, and what causes them to stop forming stars. We present predictions from the First Light And Reionisation Epoch Simulations (FLARES), a series of zoom simulations of a range of overdensities using the EAGLE code. Passive galaxies occur naturally in the EAGLE model at high redshift, and are in good agreement with number density estimates from HST and early JWST results at 3 leqslant z leqslant 5. Due to the unique FLARES approach, we extend these predictions to higher redshifts, finding passive galaxy populations up to z sim 8. Feedback from supermassive black holes is the main driver of passivity, leading to reduced gas fractions and star forming gas reservoirs. We find that passive galaxies at z geqslant 5 are not identified in the typical UVJ selection space due to their still relatively young stellar populations, and present new rest--frame selection regions. We also present NIRCam and MIRI fluxes, and find that significant numbers of passive galaxies at z geqslant 5 should be detectable in upcoming wide surveys with JWST. Finally, we present JWST colour distributions, with new selection regions in the observer--frame for identifying these early passive populations.

In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a robust framework for extracting theoretical insights from observational data. However, its computational demands can be substantial, primarily due to the need for numerous likelihood function evaluations. Our proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the likelihood function dynamically during the Bayesian inference process. Unlike traditional approaches, our method trains neural networks on-the-fly using the current set of live points as training data, without the need for pre-training. This flexibility enables adaptation to various theoretical models and datasets. We perform simple hyperparameter optimization using genetic algorithms to suggest initial neural network architectures for learning each likelihood function. Once sufficient accuracy is achieved, the neural network replaces the original likelihood function. The implementation integrates with nested sampling algorithms and has been thoroughly evaluated using both simple cosmological dark energy models and diverse observational datasets. Additionally, we explore the potential of genetic algorithms for generating initial live points within nested sampling inference, opening up new avenues for enhancing the efficiency and effectiveness of Bayesian inference methods.

In this work we propose an analytical model that reproduces the core-bounds phase of gravitational waves (GW) of Rapidly Rotating (RR) from Core Collapse Supernovae (CCSNe), as a function of three parameters, the arrival time tau, the ratio of the kinetic and potential energy beta and a phenomenological parameter alpha related to rotation and equation of state (EOS). To validate the model we use 126 waveforms from the Richers catalog Richers_2017 selected with the criteria of exploring a range of rotation profiles, and involving EOS. To quantify the degree of accuracy of the proposed model, with a particular focus on the rotation parameter beta, we show that the average Fitting Factor (FF) between the simulated waveforms with the templates is 94.4\%. In order to estimate the parameters we propose a frequentist matched filtering approach in real interferometric noise which does not require assigning any priors. We use the Matched Filter (MF) technique, where we inject a bank of templates considering simulated colored Gaussian noise and the real noise of O3L1. For example for A300w6.00\_BHBLP at 10Kpc we obtain a standar deviation of sigma = 3.34times 10^{-3} for simulated colored Gaussian noise and sigma= 1.46times 10^{-2} for real noise. On the other hand, from the asymptotic expansion of the variance we obtain the theoretical minimum error for beta at 10 kpc and optimal orientation. The estimation error in this case is from 10^{-2} to 10^{-3} as beta increases. We show that the results of the estimation error of beta for the 3-parameter space (3D) is consistent with the single-parameter space (1D), which allows us to conclude that beta is decoupled from the others two parameters.

We review recent findings from a detailed simulation study of the merging cluster El Gordo and present new results inferred from weak lensing data. We found that the observed spatial offsets between the different mass components are well reproduced in merging simulations that include self-interacting dark matter (DM), with an elastic cross-section per unit mass of approximately \sigma_DM/m_X ~ 4 -5 cm^2/gr. Moreover, a relative line-of-sight peculiar velocity on the order of several hundred km/s is found between the two stellar components of the colliding subclusters. These findings strongly suggest the possibility that, in a very energetic cluster collision, DM could possess collisional properties. However, the self-interacting DM merger model presented here is not without difficulties. The values found for \sigma_DM/m_X being in conflict with the current upper bounds on cluster scales. As a solution to this tension we argue that in major cluster mergers the physical modeling of DM interactions, based on the scattering of DM particles, should be considered too simplistic. Additionally, the DM halos of the post-collision clusters have cored density profiles with core radii r_c ~ 300 kpc. Consequently, the associated reduced tangential shear lensing profiles consistently tend to zero at angles \theta <~ 40^{''}. This result is inconsistent with what is deduced from the measured profiles. These profiles exhibit a diverging behavior when \theta --> 0, as predicted by an NFW mass model. We argue that such contradictions cannot be easily reconciled within the DM models presented so far as an alternative to the collisionless paradigm. However, we suggest that this tension can be used as a unique test bed to probe new DM physics.

Many problems in astrophysics cover multiple orders of magnitude in spatial and temporal scales. While simulating systems that experience rapid changes in these conditions, it is essential to adapt the (time-) step size to capture the behavior of the system during those rapid changes and use a less accurate time step at other, less demanding, moments. We encounter three problems with traditional methods. Firstly, making such changes requires expert knowledge of the astrophysics as well as of the details of the numerical implementation. Secondly, some parameters that determine the time-step size are fixed throughout the simulation, which means that they do not adapt to the rapidly changing conditions of the problem. Lastly, we would like the choice of time-step size to balance accuracy and computation effort. We address these challenges with Reinforcement Learning by training it to select the time-step size dynamically. We use the integration of a system of three equal-mass bodies that move due to their mutual gravity as an example of its application. With our method, the selected integration parameter adapts to the specific requirements of the problem, both in terms of computation time and accuracy while eliminating the expert knowledge needed to set up these simulations. Our method produces results competitive to existing methods and improve the results found with the most commonly-used values of time-step parameter. This method can be applied to other integrators without further retraining. We show that this extrapolation works for variable time-step integrators but does not perform to the desired accuracy for fixed time-step integrators.

We describe in the context of the particle physics (PP) standard model (SM) `PP-SM' the understanding of the primordial properties and composition of the Universe in the temperature range 130GeV>T>20keV. The Universe evolution is described using FLRW cosmology. We present a global view on particle content across time and describe the different evolution eras using deceleration parameter q. We follow the arrow of time in the expanding and cooling Universe: After the PP-SM heavies (t, h, W, Z) diminish in abundance below Tsimeq 50GeV, the PP-SM plasma in the Universe is governed by the strongly interacting Quark-Gluon content. Once the temperature drops below Tsimeq 150MeV, quarks and gluons hadronize into strongly interacting matter particles. Rapid disappearance of baryonic antimatter completes at T_B=38.2MeV. We study the ensuing disappearance of strangeness and mesons in general. We show that the different eras defined by particle populations are barely separated from each other with abundance of muons fading out just prior to T=O(2.5)MeV, the era of emergence of the free-streaming neutrinos. We discuss the two relevant fundamental constants controlling the decoupling of neutrinos. We subsequently follow the primordial Universe as it passes through the hot dense electron-positron plasma epoch. The high density of positron antimatter disappears near T=20.3keV: Nuclear reactions occur in the presence of a highly mobile and relatively strongly interacting electron-positron plasma phase. We apply plasma theory methods to describe the strong screening effects between heavy dust particle (nucleons). We analyze the paramagnetic characteristics of the electron-positron plasma when exposed to an external primordial magnetic field.

We investigate magnetic-field amplification driven by the nonresonant hybrid (NRH or Bell) instability and its impact on cosmic-ray (CR) acceleration at reverse shocks of ultrafast outflows (UFOs) from active galactic nuclei (AGN). Previous kinetic studies by particle-in-cell simulations have demonstrated that when maximum CR energy is near the injection scale, NRH instability efficiently amplifies magnetic field up to the saturation level. However, the efficiency of NRH instability goes down as maximum energy increase since CR current is carried by escaping CRs near the maximum energy. We employ a one-dimensional MHD--CR framework solving telegraph-type diffusion--convection equations to trace the coupled evolution of CRs, magnetic fields, and shock dynamics under realistic parameters. We find a distinct transition with magnetic field strength: for weak background fields (B_{0}!lesssim!10^{-4},G), NRH instability efficiently amplifies upstream turbulence, driving a self-regulated state where E_{max} becomes independent of initial strength of magnetic turbulence. In contrast, for stronger background fields (B_{0}!gtrsim!10^{-3},G), the escaping CR current is too weak to drive NRH instability, and magnetic turbulence further decays through parametric instabilities, potentially reducing the acceleration efficiency. We give the physical interpretation for the transition and discuss conditions for PeV--EeV acceleration at UFO reverse shocks.

Fuzzy (wave) dark matter (FDM), the dynamical model underlying an ultralight bosonic dark matter species, produces a rich set of non-gravitational signatures that distinguishes it markedly from the phenomenologically related warm (particle) dark matter (WDM) scenario. The emergence of extended interference fringes hosted by cosmic filaments is one such phenomenon reported by cosmological simulations, and a detailed understanding of such may strengthen existing limits on the boson mass but also break the degeneracy with WDM, and provide a unique fingerprint of interference in cosmology. In this paper, we provide initial steps towards this goal. In particular, we show in a bottom-up approach, how the presence of interference in an idealised filament population can lead to a non-suppressive feature in the matter power spectrum -- an observation supported by fully-cosmological FDM simulations. To this end, we build on a theoretically motivated and numerically observed steady-state approximation for filaments and express the equilibrium dynamics of such in an expansion of FDM eigenstates. We optimise the size of the expansion by incorporating classical phase-space information. Ellipsoidal collapse considerations are used to construct a fuzzy filament mass function which, together with the reconstructed FDM wave function, allow us to efficiently compute the one-filament power spectrum. We showcase our non-perturbative interference model for a selection of boson masses and confirm our approach is able to produce the matter power boost observed in fully-cosmological FDM simulations. More precisely, we find an excess in correlation between the spatial scale associated with the FDM ground state and the quantum pressure scale. We speculate about applications of this effect in data analysis.

General-purpose large language models, despite their broad capabilities, often struggle with specialized domain knowledge, a limitation particularly pronounced in more accessible, lower-parameter versions. This gap hinders their deployment as effective agents in demanding fields such as astronomy. Building on our prior work with AstroSage-8B, this study introduces AstroSage-70B, a significantly larger and more advanced domain-specialized natural-language AI assistant. It is designed for research and education across astronomy, astrophysics, space science, astroparticle physics, cosmology, and astronomical instrumentation. Developed from the Llama-3.1-70B foundation, AstroSage-70B underwent extensive continued pre-training on a vast corpus of astronomical literature, followed by supervised fine-tuning and model merging. Beyond its 70-billion parameter scale, this model incorporates refined datasets, judiciously chosen learning hyperparameters, and improved training procedures, achieving state-of-the-art performance on complex astronomical tasks. Notably, we integrated reasoning chains into the SFT dataset, enabling AstroSage-70B to either answer the user query immediately, or first emit a human-readable thought process. Evaluated on the AstroMLab-1 benchmark -- comprising 4,425 questions from literature withheld during training -- AstroSage-70B achieves state-of-the-art performance. It surpasses all other tested open-weight and proprietary models, including leading systems like o3, Gemini-2.5-Pro, Claude-3.7-Sonnet, Deepseek-R1, and Qwen-3-235B, even those with API costs two orders of magnitude higher. This work demonstrates that domain specialization, when applied to large-scale models, can enable them to outperform generalist counterparts in specialized knowledge areas like astronomy, thereby advancing the frontier of AI capabilities in the field.

We develop a novel way to probe subgalactic-scale matter distribution with diffractive lensing on gravitational waves. Five-year observations from Einstein Telescope and DECIGO are expected to probe k= 10^5sim 10^8 ,{rm Mpc}^{-1} down to P(k) = 10^{-16} sim 10^{-14} ,{rm Mpc}^3 level. These results can be interpreted in terms of primordial black holes in the range M_{rm PBH} gtrsim 10^{-3}M_odot down to f_{rm PBH} = 10^{-6} level, or QCD axion minihalos in the range m_a = 10^{-3} sim 10^{-12} ,{rm eV}. A key result of the paper is the approximate relation between the scale k and the gravitational wave frequency f, derived in an ensemble of `multi-lensing' events. This relation enables direct measurement of the power spectrum at specific scales, with sensitivities characterized by model-independent kernels delta P(k). Additionally, we delineate the statistical properties of `multi-lensing' based on the `Fresnel number' N_F. When N_F cal O(1), the statistical significance can be approximately calculated by Variance of lensing effects, which is directly related to the power spectrum among other moments of matter distribution.

Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unstable long-term dynamics inhibit learning from sufficiently diverse dynamics, while varying resolutions and dimensionalities challenge efficient training on modern hardware. Through empirical and theoretical analysis, we incorporate new approaches to mitigate these obstacles, including a harmonic-analysis-based stabilization method, load-balanced distributed 2D and 3D training strategies, and compute-adaptive tokenization. Using these tools, we develop Walrus, a transformer-based foundation model developed primarily for fluid-like continuum dynamics. Walrus is pretrained on nineteen diverse scenarios spanning astrophysics, geoscience, rheology, plasma physics, acoustics, and classical fluids. Experiments show that Walrus outperforms prior foundation models on both short and long term prediction horizons on downstream tasks and across the breadth of pretraining data, while ablation studies confirm the value of our contributions to forecast stability, training throughput, and transfer performance over conventional approaches. Code and weights are released for community use.

Feedback from active galactic nuclei (AGN) has become established as a fundamental process in the evolution of the most massive galaxies. Its impact on Milky Way (MW)-mass systems, however, remains comparatively unexplored. In this work, we use the Auriga simulations to probe the impact of AGN feedback on the dynamical and structural properties of galaxies, focussing on the bar, bulge, and disc. We analyse three galaxies -- two strongly and one unbarred/weakly barred -- using three setups: (i) the fiducial Auriga model, which includes both radio and quasar mode feedback, (ii) a setup with no radio mode, and (iii) one with neither the radio nor the quasar mode. When removing the radio mode, gas in the circumgalactic medium cools more efficiently and subsequently settles in an extended disc, with little effect on the inner disc. Contrary to previous studies, we find that although the removal of the quasar mode results in more massive central components, these are in the form of compact discs, rather than spheroidal bulges. Therefore, galaxies without quasar mode feedback are more baryon-dominated and thus prone to forming stronger and shorter bars, which reveals an anti-correlation between the ejective nature of AGN feedback and bar strength. Hence, we report that the effect of AGN feedback (i.e. ejective or preventive) can significantly alter the dynamical properties of MW-like galaxies. Therefore, the observed dynamical and structural properties of MW-mass galaxies can be used as additional constraints for calibrating the efficiency of AGN feedback models.

Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster readiness, reduced economic risk, and improved policy-making amidst climate change. Yet, S2S prediction remains challenging due to the chaotic nature of the system. At present, existing benchmarks for weather and climate applications, tend to (1) have shorter forecasting range of up-to 14 days, (2) do not include a wide range of operational baseline forecasts, and (3) lack physics-based constraints for explainability. Thus, we propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for S2S prediction. ChaosBench has over 460K frames of real-world observations and simulations, each with 60 variable-channels and spanning for up-to 45 years. We also propose several physics-based, in addition to vision-based metrics, that enables for a more physically-consistent model. Furthermore, we include a diverse set of physics-based forecasts from 4 national weather agencies as baselines to our data-driven counterpart. We establish two tasks that vary in complexity: full and sparse dynamics prediction. Our benchmark is one of the first to perform large-scale evaluation on existing models including PanguWeather, FourCastNetV2, GraphCast, and ClimaX, and finds methods originally developed for weather-scale applications fails on S2S task. We release our benchmark code and datasets at https://leap-stc.github.io/ChaosBench.

Accurate Subseasonal-to-Seasonal (S2S) ocean simulation is critically important for marine research, yet remains challenging due to its substantial thermal inertia and extended time delay. Machine learning (ML)-based models have demonstrated significant advancements in simulation accuracy and computational efficiency compared to traditional numerical methods. Nevertheless, a significant limitation of current ML models for S2S ocean simulation is their inadequate incorporation of physical consistency and the slow-changing properties of the ocean system. In this work, we propose a neural ocean model (NeuralOM) for S2S ocean simulation with a multi-scale interactive graph neural network to emulate diverse physical phenomena associated with ocean systems effectively. Specifically, we propose a multi-stage framework tailored to model the ocean's slowly changing nature. Additionally, we introduce a multi-scale interactive messaging module to capture complex dynamical behaviors, such as gradient changes and multiplicative coupling relationships inherent in ocean dynamics. Extensive experimental evaluations confirm that our proposed NeuralOM outperforms state-of-the-art models in S2S and extreme event simulation. The codes are available at https://github.com/YuanGao-YG/NeuralOM.

While the influence of galaxy clusters on galaxy evolution is relatively well-understood, the impact of the dynamical states of these clusters is less clear. This paper series explores how the dynamical state of galaxy clusters affects their galaxy populations' physical and morphological properties. The primary aim of this first paper is to evaluate the dynamical state of 87 massive (M_{500} geq 1.5 times 10^{14} M_{odot}) galaxy clusters at low redshifts (0.10 leq z leq 0.35). This will allow us to have a well-characterized sample for analyzing physical and morphological properties in our next work. We employ six dynamical state proxies utilizing optical and X-ray imaging data. Principal Component Analysis (PCA) is applied to integrate these proxies effectively, allowing for robust classification of galaxy clusters into relaxed, intermediate, and disturbed states based on their dynamical characteristics. The methodology successfully segregates the galaxy clusters into the three dynamical states. Examination of the galaxy distributions in optical wavelengths and gas distributions in X-ray further confirms the consistency of these classifications. The clusters' dynamical states are statistically distinguishable, providing a clear categorization for further analysis.

Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine learning have enabled the efficient classification and characterization of these transient phenomena. We aim to develop a fully systematic pipeline to identify candidate stellar collision events in galactic nuclei, which may otherwise be identified as tidal disruption events or other transients. We also seek to validate our simulations by comparing key physical parameters derived from observations and used in modeling these events. We generate a comprehensive bank of simulated light curves spanning a range of physical parameters and employ an approximate nearest neighbor algorithm (via the annoy library) to match these with observed ZTF light curves. Our pipeline is successfully able to associate observed ZTF light curves with simulated events. The resulting estimated parameters, including supermassive black hole masses and ejecta mass, are presented and compared to known values when applicable. We demonstrate that a systematic, machine learning-based approach can effectively identify and characterize stellar collision candidate events from large-scale transient surveys. This methodology is especially promising for future surveys which will provide us with significantly high volumes of data, such as LSST, where automated, data-intensive analysis will be critical for advancing our understanding of transient astrophysical phenomena.

Astrophysical uncertainties in dark matter direct detection experiments are typically addressed by parametrizing the velocity distribution in terms of a few uncertain parameters that vary around some central values. Here we propose a method to optimize over all velocity distributions lying within a given distance measure from a central distribution. We discretize the dark matter velocity distribution as a superposition of streams, and use a variety of information divergences to parametrize its uncertainties. With this, we bracket the limits on the dark matter-nucleon and dark matter-electron scattering cross sections, when the true dark matter velocity distribution deviates from the commonly assumed Maxwell-Boltzmann form. The methodology pursued is general and could be applied to other physics scenarios where a given physical observable depends on a function that is uncertain.

We introduce a new model for the accretion and feedback of supermassive black hole (SMBH) binaries to the KETJU code, which enables us to resolve the evolution of SMBH binaries down to separations of tens of Schwarzschild radii in gas-rich galaxy mergers. Our subgrid binary accretion model extends the widely used Bondi--Hoyle--Lyttleton accretion into the binary phase and incorporates preferential mass accretion onto the secondary SMBH, which is motivated by results from small-scale hydrodynamical circumbinary disc simulations. We perform idealised gas-rich disc galaxy merger simulations using pure thermal or pure kinetic active galactic nuclei (AGN) feedback. Our binary accretion model provides more physically motivated SMBH mass ratios, which are one of the key parameters for computing gravitational wave (GW) induced recoil velocities. The merger time-scales of our simulated SMBH binaries are in the range t_{rm merge}{sim} 10--400 Myr. Prograde in-plane equal-mass galaxy mergers lead to the shortest merger time-scales, as they experience the strongest starbursts, with the ensuing high stellar density resulting in a rapid SMBH coalescence. Compared to the thermal AGN feedback, the kinetic AGN feedback predicts longer merger time-scales and results in more core-like stellar profiles, as it is more effective in removing gas from the galaxy centre and quenching star formation. This suggests that the AGN feedback implementation plays a critical role in modelling SMBH coalescences. Our model will be useful for improving the modelling of SMBH mergers in gas-rich galaxies, the prime targets for the upcoming LISA GW observatory.