mmu_des_y3_sne_ia HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_des_y3_sne_ia.

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_des_y3_sne_ia")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_des_y3_sne_ia",
    search_filter=lsdb.ConeSearch(ra=35.0, dec=-6.0, 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_des_y3_sne_ia � 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_des_y3_sne_ia_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
496	7	11	192.3 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	496	46,344	46,344	46,344	46,344	496	496	496	496	496	496
Example row	1243942473816715777	[g, g, g, g, g, g, g, g, g, g, g, � (112 total)]	[5.694e+04, 5.695e+04, 5.696e+04, � (112 total)]	[1.838, 3.156, -1.974, -0.876, � (112 total)]	[4.258, 6.417, 3.601, 4.023, 5.391, � (112 total)]	0.2883	10.38	34.83	-5.789	Ia	DES_1313594
Minimum value	1243402345506524657	g	-99.0	-465.260009765625	-0.0	0.05962910130620003	2.0	6.669886112213135	-44.91709899902344	Ia	DES_1248677
Maximum value	2542315152193303737	z	57428.0546875	4431.10205078125	134.26699829101562	0.8485189080238342	11.562000274658203	55.340694427490234	0.9379519820213318	Ia	DES_1347120

"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_des_y3_sne_ia = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_des_y3_sne_ia")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_des_y3_sne_ia.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 observations from the Dark Energy Survey (DES), specifically the Year 3 supernova sample, and compiled as part of the Pantheon+ compilation. It contains spectroscopically confirmed Type Ia supernovae used for cosmological studies.

Data modality
The dataset consists of light curves, i.e. time-series data including observation time, flux, flux error, and bandpass filter (griz). It also includes metadata such as redshift, coordinates, host galaxy mass, and supernova classification.

Typical use cases
It is commonly used for photometric redshift prediction, light curve modeling, and inpainting tasks.

Caveats
The dataset includes only spectroscopically confirmed Type Ia supernovae and is subject to selection cuts from the Pantheon+ compilation.

Citation
Users should cite the Dark Energy Survey (DES) and the associated Pantheon+ and DES supernova papers. The dataset is released under the CC BY 4.0 license, requiring attribution to the original authors.