mmu_yse_dr1 HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_yse_dr1.

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_yse_dr1")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_yse_dr1",
    search_filter=lsdb.ConeSearch(ra=142.0, dec=36.0, 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_yse_dr1 � 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_yse_dr1_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
2,003	7	312	195.5 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	2,003	269,628	269,628	269,628	269,628	2,003	2,003	2,003	2,003	2,003	2,003
Example row	399200651177352460	[X, X, X, X, X, X, X, X, X, X, X, � (168 total)]	[5.887e+04, 5.887e+04, 5.887e+04, � (168 total)]	[328.3, 435.8, 580.4, 681.8, 773, � (168 total)]	[76.91, 74.19, 56.67, 59.37, 67.84, � (168 total)]	0.14	-99	141.9	35.53	SN-Ia-norm	2020azn
Minimum value	1126237237925017	X	-99.0	-0.0	-0.0	-99.0	-99.0	0.3768570125102997	-29.100194931030273	LBV	2019lbi
Maximum value	3458687311145453838	z	59839.5703125	1408506.75	97039.4609375	0.5249999761581421	-99.0	359.86798095703125	74.1773452758789	TDE	2021zzv

"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_yse_dr1 = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_yse_dr1")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_yse_dr1.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 Young Supernova Experiment Data Release 1 (YSE DR1) and contains a collection of 1975 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 classification of supernovae.

Caveats
No additional preprocessing or selection criteria beyond those in the original release are described.

Citation
The dataset is released under the CC BY 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.