mmu_jwst_gds HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_jwst_gds.

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_jwst_gds")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_jwst_gds",
    search_filter=lsdb.ConeSearch(ra=53.0, dec=-28.0, 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_jwst_gds — 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_jwst_gds_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
17,494	11	8	6.3 GiB	hats-import v0.7.3, hats v0.7.3

Catalog columns

The main HATS catalog contains the following columns:

Name	_healpix_29	image.band	image.flux	image.ivar	image.mask	image.psf_fwhm	image.scale	mag_auto	flux_radius	flux_auto	fluxerr_auto	cxx_image	cyy_image	cxy_image	object_id	ra	dec
Data Type	int64	list[string]	list[list<element: list<element: float>>]	list[list<element: list<element: float>>]	list[list<element: list<element: bool>>]	list[float]	list[float]	float	float	float	float	float	float	float	string	double	double
Nested?	—	image	image	image	image	image	image	—	—	—	—	—	—	—	—	—	—
Value count	17,494	122,458	N/A	N/A	N/A	122,458	122,458	17,494	17,494	17,494	17,494	17,494	17,494	17,494	17,494	17,494	17,494
Example row	2528743702220181960	[f090w, f115w, f150w, f200w, f277w, … (7 total)]	[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … (96 total)], … (96 total)], …	[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … (96 total)], … (96 total)], …	[[[False, False, False, False, False, … (96 total)], … (96 total)], ……	[0.033, 0.04, 0.05, 0.066, 0.092, … (7 total)]	[0.02, 0.02, 0.02, 0.02, 0.04, 0.04, … (7 total)]	25.85	4.665	0.1431	0.0007781	0.05192	0.07733	-0.03422	-7567302259786491648	53.17	-27.87
Minimum value	2528743693306351542	f090w	N/A	N/A	N/A	0.032999999821186066	0.019999999552965164	17.325851440429688	0.6315596699714661	0.02997756563127041	0.00019976860494352877	7.402321352856234e-05	2.9240540243336e-05	-1.2243539094924927	-7567302259786424111	53.051927973650635	-27.87760950265767
Maximum value	2528752620411181000	f444w	N/A	N/A	N/A	0.14499999582767487	0.03999999910593033	27.499950408935547	276.15155029296875	415.23297119140625	0.3415043354034424	2.141709089279175	1.9386420249938965	1.0062775611877441	-7567302259786494449	53.22671287579358	-27.723125018856216

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

crossmatched = mmu_jwst_gds.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 James Webb Space Telescope (JWST) NIRCam observations from early deep field surveys.

Data modality
The dataset consists of fixed-size image cutouts (96×96 pixels) centered on sources from photometric catalogs. The images are multi-band, with 6 or 7 filters covering wavelengths from approximately 0.9μm to 4.4μm.

Typical use cases
Images from these JWST deep field surveys have been used in a large number of scientific publications, including machine learning applications.

Caveats
Different surveys have different wavelength coverage, and missing bands are represented as arrays of zeros to simplify data loading.

Citation
The data are in the public domain. The dataset uses data products retrieved from the Dawn JWST Archive (DJA), an initiative of the Cosmic Dawn Center (DAWN).