mmu_tess_spoc HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_tess_spoc.

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_tess_spoc")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_tess_spoc",
    search_filter=lsdb.ConeSearch(ra=20.0, dec=61.0, 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_tess_spoc — 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_tess_spoc_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
1,122,883	4	1,084	64.4 GiB	hats-import v0.7.3, hats v0.7.3

Catalog columns

The main HATS catalog contains the following columns:

Name	_healpix_29	lightcurve.time	lightcurve.flux	lightcurve.flux_err	ra	dec	object_id
Data Type	int64	list[float]	list[float]	list[float]	double	double	string
Nested?	—	lightcurve	lightcurve	lightcurve	—	—	—
Value count	1,122,883	12,180,623,431	12,180,623,431	12,180,623,431	1,122,883	1,122,883	1,122,883
Example row	255238671391160662	[2882, 2882, 2882, 2882, 2882, … (11673 total)]	[1.448e+04, 1.447e+04, 1.448e+04, … (11673 total)]	[13.36, 13.35, 13.35, 13.35, … (11673 total)]	19.52	60.78	54374808
Minimum value	34898257120247167	-0.0	-544389.875	-0.0	0.0002006297289342	-89.8896741927853	10000003212
Maximum value	3423932021944551628	3208.14306640625	60974292.0	682.621337890625	359.999893323771	89.8323304826145	9993478

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

crossmatched = mmu_tess_spoc.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 NASA Transiting Exoplanet Survey Satellite (TESS), an all-sky photometric survey observing millions of sources to discover exoplanets and study variable stars.

Data modality
The dataset consists of light curve data (brightness over time) including object_id, time (BTJD; Barycenter corrected TESS Julian Date), flux (electrons per second) and flux_error (electrons per second). It contains light curves for approximately 1,120,000 stars observed in selected sectors.

Stars are typically observed for about 27 days per sector and may be observed multiple times across different sectors. Observations have a cadence between 20 seconds and 30 minutes.

Typical use cases
TESS light curves have been used in machine learning applications such as light curve classification (e.g. transiting exoplanets, pulsating stars, eclipsing binaries) and stellar parameter estimation.

Caveats
The dataset includes data from selected observing sectors. Light curves are processed using the PDC_SAP flux, with additional cleaning applied through quality flag filtering to remove potentially anomalous data points.

Citation
The data are publicly available through the Mikulski Archive for Space Telescopes (MAST) and are released under the CC BY 4.0 license.