mmu_sdss_sdss HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_sdss_sdss.

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/LSDB/mmu_sdss_sdss")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/LSDB/mmu_sdss_sdss",
    search_filter=lsdb.ConeSearch(ra=136.0, dec=24.0, 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_sdss_sdss — 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_sdss_sdss_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
806,176	30	789	31.8 GiB	hats-import v0.7.1, hats v0.7.1

Catalog columns

The main HATS catalog contains the following columns:

Name	_healpix_29	spectrum.flux	spectrum.ivar	spectrum.lsf_sigma	spectrum.lambda	spectrum.mask	VDISP	VDISP_ERR	Z	Z_ERR	ra	dec	healpix	ZWARNING	SPECTROFLUX_U	SPECTROFLUX_G	SPECTROFLUX_R	SPECTROFLUX_I	SPECTROFLUX_Z	SPECTROFLUX_IVAR_U	SPECTROFLUX_IVAR_G	SPECTROFLUX_IVAR_R	SPECTROFLUX_IVAR_I	SPECTROFLUX_IVAR_Z	SPECTROSYNFLUX_U	SPECTROSYNFLUX_G	SPECTROSYNFLUX_R	SPECTROSYNFLUX_I	SPECTROSYNFLUX_Z	SPECTROSYNFLUX_IVAR_U	SPECTROSYNFLUX_IVAR_G	SPECTROSYNFLUX_IVAR_R	SPECTROSYNFLUX_IVAR_I	SPECTROSYNFLUX_IVAR_Z	object_id
Data Type	int64	list[float]	list[float]	list[float]	list[float]	list[bool]	float	float	float	float	double	double	int64	bool	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	string
Nested?	—	spectrum	spectrum	spectrum	spectrum	spectrum	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Value count	806,176	3,113,669,518	3,113,669,518	3,113,669,518	3,113,669,518	3,113,669,518	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176	806,176
Example row	343977106285581288	[2.24, 2.24, 2.24, 2.239, 2.239, … (3864 total)]	[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … (3864 total)]	[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … (3864 total)]	[3805, 3806, 3807, 3808, 3809, … (3864 total)]	[True, True, True, True, True, … (3864 total)]	182.7	11.25	0.1305	3.128e-05	136.5	23.81	305	False	4.558	17.05	48.13	73.82	100.3	1.352	4.696	2.782	1.695	0.6382	4.557	17.02	48.1	73.43	99.54	3.227	4.65	2.981	1.962	0.7785	b' 2575025233207519232'
Minimum value	168688995934	-1399.4620361328125	-0.0	-0.0	-1.0	False	-0.0	-4.0	-0.011087613180279732	-6.0	0.000686	-11.25283	0	False	-53.29541778564453	-8.221423149108887	-3.079059362411499	-5.200530052185059	-56.53141784667969	0.018610242754220963	0.021417152136564255	0.018582580611109734	0.01028701663017273	0.0032096304930746555	-903.9955444335938	-431.1527099609375	-6.962734699249268	-41.03329849243164	-120.22126770019531	0.014560655690729618	0.020548932254314423	0.013832802884280682	0.009603755548596382	0.004050940275192261	b' 299489677444933632'
Maximum value	3458764448209686099	224369.609375	126889.46875	1.6645588874816895	9272.5673828125	True	850.0	2262.74169921875	7.003869533538818	867.6547241210938	359.99936	70.287347	3071	True	28427.263671875	129483.3828125	346752.21875	780947.1875	1373210.875	3.3445169925689697	10.681514739990234	6.103046417236328	3.457676649093628	2.023735523223877	26742.1796875	127758.609375	349161.75	727656.0	1344424.875	9.424415588378906	10.719206809997559	6.276713848114014	3.996629238128662	2.6291794776916504	b' 3381253423394562048'

"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_sdss_sdss = lsdb.open_catalog("https://huggingface.co/datasets/LSDB/mmu_sdss_sdss")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_sdss_sdss.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 Sloan Digital Sky Survey (SDSS), which has mapped a large portion of the sky using a dedicated optical telescope. It includes data from multiple SDSS programs, including the Legacy survey, SEGUE-1, SEGUE-2, BOSS, and eBOSS surveys.

Data modality
The dataset consists of optical spectra covering wavelength ranges from 3800 to 9200 (SDSS spectrograph) and 3650 to 10400 (BOSS spectrograph). Each spectrum includes flux measurements, wavelength values, and inverse variance (ivar), along with pixel-level masks indicating potential quality issues. The dataset contains approximately 4 million spectra.

Typical use cases
SDSS spectra have been used in numerous scientific publications. Machine learning applications include building data-driven representations of galaxy spectra and identifying outliers.

Caveats
The dataset includes spectra of varying lengths. It also applies selection cuts, including avoiding duplicate spectra for the same object, keeping only good-quality plates, and selecting only science targets.

Citation
SDSS data releases are in the public domain. Users should include the appropriate acknowledgements for the SDSS, SEGUE-1, SEGUE-2, BOSS, and eBOSS samples when using this dataset.