mmu_vipers_w4 HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_vipers_w4.

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_vipers_w4")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/LSDB/mmu_vipers_w4",
    search_filter=lsdb.ConeSearch(ra=333.0, dec=1.0, 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_vipers_w4 � 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_vipers_w4_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
30,979	9	9	321.4 MiB	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.lambda	spectrum.mask	REDSHIFT	REDFLAG	EXPTIME	NORM	MAG	ra	dec	object_id
Data Type	int64	list[float]	list[float]	list[float]	list[float]	float	float	float	float	float	float	float	string
Nested?	�	spectrum	spectrum	spectrum	spectrum	�	�	�	�	�	�	�	�
Value count	30,979	17,255,303	17,255,303	17,255,303	17,255,303	30,979	30,979	30,979	30,979	30,979	30,979	30,979	30,979
Example row	1339537029392400028	[0.1504, 0.1525, 0.1538, 0.1543, � (557 total)]	[8.585e-17, 1.011e-16, 1.137e-16, � (557 total)]	[5514, 5521, 5529, 5536, 5543, � (557 total)]	[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, � (557 total)]	0.7298	0.7298	540	2.768	21.98	333.4	1.077	404055346.0
Minimum value	1339504075142965993	-1759.751953125	-2.9612807435456968e-18	5514.27978515625	-0.0	-0.0	-0.0	540.0	0.021366773173213005	15.182700157165527	330.0451965332031	0.8620766997337341	401007640.0
Maximum value	1341678248016204204	2786.57177734375	7.853208785491006e-07	9484.1201171875	3.0	4.395299911499023	4.395299911499023	540.0	3519.447509765625	23.96969985961914	335.39093017578125	2.3695244789123535	411162926.0

"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_vipers_w4 = lsdb.open_catalog("https://huggingface.co/datasets/LSDB/mmu_vipers_w4")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_vipers_w4.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 VIMOS Public Extragalactic Redshift Survey (VIPERS), which provides optical spectra of galaxies in the redshift range 0.5 < z < 1.0.

Data modality
The dataset consists of fixed-size optical spectra (1 × 557) covering a wavelength range from 5514 Å to 9484 Å. Each spectrum includes flux values, the corresponding wavelength vector, inverse variance (ivar), and a mask indicating the quality of each measurement. The dataset contains approximately 90,000 galaxy spectra.

Typical use cases
This dataset has been used in a number of scientific publications, as well as in machine learning specific applications, including source identification with SVMs and galaxy classification with unsupervised methods.

Caveats
The dataset includes spectra that have been normalized and transformed during preprocessing to ensure consistency with other datasets.

Citation
This dataset uses data from the VIMOS Public Extragalactic Redshift Survey (VIPERS), obtained with the ESO Very Large Telescope. Users should acknowledge the VIPERS survey and its participating institutions.