mmu_snls HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_snls.

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_snls")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_snls",
    search_filter=lsdb.ConeSearch(ra=216.0, dec=52.0, 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_snls � 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_snls_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
478	7	7	192.2 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	obj_type	object_id	ra	dec
Data Type	int64	list[string]	list[float]	list[float]	list[float]	float	float	string	string	double	double
Nested?	�	lightcurve	lightcurve	lightcurve	lightcurve	�	�	�	�	�	�
Value count	478	55,072	55,072	55,072	55,072	478	478	478	478	478	478
Example row	802467230274757920	[g, g, g, g, g, g, g, g, g, g, g, g, � (88 total)]	[5.314e+04, 5.315e+04, 5.315e+04, � (88 total)]	[-0.374, -0.003, -0.119, 116.4, � (88 total)]	[1.003, 0.863, 0.727, 0.963, 1.153, � (88 total)]	0.3405	9.356	Ia	04D3nh	215.6	52.33
Minimum value	802465652001458125	g	-99.0	-48.99100112915039	-0.0	0.12580470740795135	5.294000148773193	Ia	03D1au	36.02301025390625	-18.232067108154297
Maximum value	3412972210937248373	z	54095.55859375	1387.178955078125	54.29999923706055	1.0607936382293701	11.579999923706055	Ia	06D4dh	334.3792724609375	53.18427658081055

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

crossmatched = mmu_snls.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 Supernova Legacy Survey (SNLS) and contains a collection of 239 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.

Citation
The dataset is released under the GNU General Public License v3.0. Users should attribute the original SNLS data source appropriately.