Datasets:

UniverseTBD
/

mmu_plasticc

configs:
  - config_name: default
    data_dir: mmu_plasticc/dataset
tags:
  - astronomy
license: cc-by-4.0
pretty_name: mmu_plasticc
size_categories:
  - 1M<n<10M

mmu_plasticc HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_plasticc.

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_plasticc")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_plasticc",
    search_filter=lsdb.ConeSearch(ra=330.0, dec=-6.0, 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_plasticc — 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_plasticc_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
7,001,476	8	1,509	3.9 GiB	hats-import v0.7.3, hats v0.7.3

Catalog columns

The main HATS catalog contains the following columns:

Name	`_healpix_29`	`lightcurve.band`	`lightcurve.time`	`lightcurve.flux`	`lightcurve.flux_err`	`hostgal_photoz`	`hostgal_specz`	`redshift`	`ra`	`dec`	`obj_type`	`object_id`
Data Type	int64	list[string]	list[float]	list[float]	list[float]	float	float	float	double	double	string	string
Nested?	—	lightcurve	lightcurve	lightcurve	lightcurve	—	—	—	—	—	—	—
Value count	7,001,476	5,849,936,616	5,849,936,616	5,849,936,616	5,849,936,616	7,001,476	7,001,476	7,001,476	7,001,476	7,001,476	7,001,476	7,001,476
Example row	1310263451060905914	[u, u, u, u, u, u, u, u, u, u, u, … (816 total)]	[5.979e+04, 5.982e+04, 5.982e+04, … (816 total)]	[-13.15, 7.379, 2.236, -2.3, … (816 total)]	[13.62, 8.448, 8.948, 13.9, 10.11, … (816 total)]	0.545	-9	0.551	330.5	-6.129	SNII	75227491
Minimum value	64871186081855	Y	-0.0	-8935484.0	-0.0	-0.0	-9.0	-0.0	-0.0	-64.7609	AGN	100000051
Maximum value	3458741424076622916	z	60674.26171875	13675792.0	13791667.0	3.0	3.444999933242798	3.446000099182129	359.8242	4.1815	TDE	99999980

"ra" and "dec" are random numbers and not real coordinates.

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

crossmatched = mmu_plasticc.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
The PLAsTiCC dataset is a collection of simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge, designed to match the expected characteristics of the Legacy Survey of Space and Time (LSST).

It includes 14 types of time-varying astronomical objects, broadly divided into two categories: transients (short-lived events such as supernovae) and variable sources (objects with fluctuating brightness such as pulsating stars).

Data modality
The dataset consists of time-series data (light curves) including time of observation, flux, flux error, and filter information. Observations are provided in six filters (u, g, r, i, z, Y). Each time series is also associated with a class label and a redshift.

Typical use cases
The dataset has been widely used to develop and test models for supernova classification.

Caveats
The dataset consists of simulated light curves generated using the expected instrument characteristics and survey strategy of LSST.

Citation
The dataset is released under the CC BY 4.0 license and can be cited using its DOI: 10.5281/zenodo.2539456.