mmu_foundation HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_foundation.

This dataset is part of the Multimodal Universe, a large-scale collection of multimodal astronomical data. For full details, see the paper: The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data.

Access the catalog

We recommend the use of the LSDB Python framework to access HATS catalogs. LSDB can be installed via pip install lsdb or conda install conda-forge::lsdb, see more details in the docs. The following code provides a minimal example of opening this catalog:

import lsdb

# Full sky coverage.
catalog = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_foundation")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_foundation",
    search_filter=lsdb.ConeSearch(ra=126.0, dec=25.0, radius_arcsec=3600.0),
)

Each catalog in this collection is represented as a separate Apache Parquet dataset and can be accessed with a variety of tools, including pandas, pyarrow, dask, Spark, DuckDB.

File structure

This catalog is represented by the following files and directories:

• collection.properties � textual metadata file describing the HATS collection of catalogs
• mmu_foundation � main HATS catalog directory
  • dataset/ � Apache Parquet dataset directory for the main catalog
    • ... parquet metadata and data files in sub directories ...
  • hats.properties � textual metadata file describing the main HATS catalog
  • partition_info.csv � CSV file with a list of catalog HEALPix tiles (catalog partitions)
  • skymap.fits � HEALPix skymap FITS file with row-counts per HEALPix tile of fixed order 10
• mmu_foundation_10arcs/ � default margin catalog to ensure data completeness in cross-matching, the margin threshold is 10.0 arcseconds
  • ... margin catalog files and directories ...

Catalog metadata

Metadata of the main HATS catalog, excluding margins and indexes:

Number of rows	Number of columns	Number of partitions	Size on disk	HATS Builder
360	7	171	193.0 MiB	hats-import v0.7.3, hats v0.7.3

Catalog columns

The main HATS catalog contains the following columns:

Name	_healpix_29	lightcurve.band	lightcurve.time	lightcurve.flux	lightcurve.flux_err	redshift	host_log_mass	ra	dec	obj_type	object_id
Data Type	int64	list[string]	list[float]	list[float]	list[float]	float	float	double	double	string	string
Nested?	�	lightcurve	lightcurve	lightcurve	lightcurve	�	�	�	�	�	�
Value count	360	17,008	17,008	17,008	17,008	360	360	360	360	360	360
Example row	339588456743818884	[g, g, g, g, g, g, g, g, g, g, g, g, � (56 total)]	[5.778e+04, 5.779e+04, 5.78e+04, � (56 total)]	[1219, 682.2, 547.6, 129.3, 88.42, � (56 total)]	[95.98, 95.54, 193, 90.31, 76.68, 0, � (56 total)]	0.09284	9.137	126.1	25	Ia	iPTF17dz
Minimum value	3547481393604017	g	-99.0	-2181.111083984375	-0.0	0.004886799957603216	6.0	1.4732304811477661	-28.45744514465332	Ia	2016W
Maximum value	3424691708467754140	z	57970.5625	544487.625	7568.98779296875	0.10942360013723373	11.739999771118164	358.35638427734375	84.6783218383789	Ia	iPTF17dz

"Nested" indicates whether the column is stored as a nested field inside another "struct" column.

"Value count" may be different from the total number of rows for nested columns: each nested element is counted as a single value.

Crossmatch with another catalog

HATS catalogs can be efficiently crossmatched using LSDB, which leverages the HEALPix partitioning to avoid loading the full datasets into memory:

import lsdb

mmu_foundation = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_foundation")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_foundation.crossmatch(other, radius_arcsec=1.0)
print(crossmatched)

See the LSDB documentation for more details on crossmatching and other operations.

Dataset-specific context

Original survey
This dataset is based on the Foundation Data Release 3 (Foundation DR3) and contains a collection of 180 spectroscopically confirmed Type Ia supernova light curves.

Data modality
The dataset consists of light curve data including time of observation, flux, flux error, and band pass filter information (griz bands). Additional metadata includes supernova classification, coordinates, redshift, and host galaxy mass.

Typical use cases
The dataset can be used for photometric redshift prediction and light curve inpainting.

Caveats
The data is collected from the Pantheon+ compilation, which applies a series of selection cuts that define the final sample.

Citation
Users should cite the original Foundation DR3 data and the Pantheon+ compilation. The data is accessed through the compilation’s GitHub