mmu_cfa_cfa3 HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_cfa_cfa3.

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_cfa_cfa3")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_cfa_cfa3",
    search_filter=lsdb.ConeSearch(ra=12.0, 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_cfa_cfa3 � 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_cfa_cfa3_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
185	5	170	192.9 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.mag	lightcurve.mag_err	ra	dec	obj_type	object_id
Data Type	int64	list[string]	list[float]	list[float]	list[float]	double	double	string	string
Nested?	�	lightcurve	lightcurve	lightcurve	lightcurve	�	�	�	�
Value count	185	21,105	21,105	21,105	21,105	185	185	185	185
Example row	183542961553446571	[B, B, B, B, B, B, B, B, B, B, B, B, � (84 total)]	[5.405e+04, 5.405e+04, 5.406e+04, � (84 total)]	[17.76, 17.83, 18.15, 18.31, 19.47, � (84 total)]	[0.02, 0.018, 0.02, 0.022, 0.047, � (84 total)]	11.66	36.33	SN Ia	SN2006mo
Minimum value	15796931177724156	B	-0.0	-0.0	-0.0	-0.0	-27.562610626220703	?	SN2001C
Maximum value	3169126945126157037	r'	54596.1875	22.381000518798828	0.8669999837875366	359.6354064941406	79.03182983398438	SN Ia-pec	SN2008bf

"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_cfa_cfa3 = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_cfa_cfa3")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_cfa_cfa3.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 Harvard-Smithsonian Center for Astrophysics (CfA) Supernova Group observations and contains light curves of Type Ia supernovae observed between 2000 and 2011.

Data modality
The dataset consists of light curve data for 185 Type Ia supernovae in the UBVRIr’i’ bands. Each sample includes sky coordinates (ra, dec), object identifiers and classification, and a light curve with time (modified Julian date), band, magnitude (mag), and magnitude error (mag_err).

Typical use cases
Type Ia supernovae from this dataset have been widely used in cosmological analyses and are included in major compilations such as Pantheon, Pantheon+, and other supernova samples.

Caveats
The dataset is part of a curated compilation and follows the structure defined by the CfA Supernova Group. The flux and flux_err fields are replaced by mag and mag_err fields, which present the original photometric measurements in the standard photometric system.

Citation
Data are obtained from the CfA Supernova Archive. Users should cite the corresponding CfA3 publication and acknowledge the CfA Supernova Archive.