mmu_jwst_gdn HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_jwst_gdn.

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_gdn")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_jwst_gdn",
    search_filter=lsdb.ConeSearch(ra=189.0, dec=62.0, 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_gdn — 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_gdn_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
29,435	11	11	9.2 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	29,435	206,045	N/A	N/A	N/A	206,045	206,045	29,435	29,435	29,435	29,435	29,435	29,435	29,435	29,435	29,435	29,435
Example row	791790208707182804	[f090w, f115w, f150w, f200w, f277w, … (7 total)]	[[[0.0003051, 0.01758, 0.007788, … (96 total)], … (96 total)], … (7 t…	[[[6395, 5902, 5432, 5561, 5916, 6041, … (96 total)], … (96 total)], …	[[[True, True, True, True, True, True, … (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)]	27.21	3.845	0.03933	0.000739	0.1824	0.1795	0.06391	7251073904309204425	189.2	62.13
Minimum value	791790207886246552	f090w	N/A	N/A	N/A	0.032999999821186066	0.019999999552965164	17.300647735595703	0.5765308141708374	0.02993936277925968	0.0005087985191494226	2.9649861971847713e-05	3.4777178370859474e-05	-2.554218053817749	7251073904309204381	188.96725600580982	62.12745097303882
Maximum value	791799302667255993	f444w	N/A	N/A	N/A	0.14499999582767487	0.03999999910593033	27.4999942779541	403.9623107910156	359.7336730957031	2.076092481613159	2.7494616508483887	2.032952070236206	2.0402920246124268	7251073904309274726	189.40077013564573	62.33559761881421

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

crossmatched = mmu_jwst_gdn.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).