Datasets:

UniverseTBD
/

mmu_cfa_seccsn

_healpix_29 int64	lightcurve dict	ra float64	dec float64	obj_type string	object_id string
27,677,490,352,178,670	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", ...	50.415459	16.86739	SN Ib/c	SN2006aj
2,829,561,277,534,609,400	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", ...	148.625885	-25.708139	SN IIb	SN2006T
2,835,488,839,065,606,700	{ "band": [ "B", "B", "B", "B", "H", "H", "H", "H", "I", "I", "I", "I", "J", "J", "J", "J", "Ks", "Ks", "Ks", "Ks", "R", "R", "R", "R", "U", "U", "U", "U", "V", "V", "V", "V", "i'", ...	149.350677	-19.356421	SN Ic	SN2007aw
2,870,298,439,381,265,400	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "I", "I", "J", "J", "J", "J", "J", "J", "J", "J", "Ks", "Ks"...	145.805832	-9.61472	SN IIb	SN2006ba
314,745,796,130,558,400	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "J", ...	150.399963	21.61167	SN Ib	SN2007ag
356,194,615,710,917,900	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", ...	137.397293	33.119141	SN Ib	SN2007uy
356,194,751,035,896,960	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", ...	137.377716	33.138969	SN Ib	SN2008D
379,977,268,336,903,360	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", ...	173.013748	36.701	SN Ib/c	SN2006lv
396,044,076,338,267,260	{ "band": [ "B", "B", "B", "H", "H", "H", "I", "I", "I", "J", "J", "J", "Ks", "Ks", "Ks", "R", "R", "R", "U", "U", "U", "V", "V", "V", "i'", "i'", "i'", "r'", "r'", "r'", "u'", "u'", "u'...	177.359085	51.82272	SN Ic	SN2007bg
407,975,593,271,372,540	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", ...	139.336578	41.909081	SN Ibn	SN2006jc
43,256,176,323,661,384	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "J", "J", "J", "J", ...	37.047379	19.60375	SN Ib	SN2006F
437,471,792,971,433,860	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "J", ...	116.860458	26.925671	SN Ic	SN2005kf
446,670,850,767,360,800	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "I", "I", "J", "J", "J", "J", "J", "J", "J", "J", "Ks", "Ks"...	110.93013	33.44389	SN Ib	SN2001ej
504,770,873,312,005,300	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", ...	137.176376	44.814281	SN Ic	SN2005mf
539,018,042,342,926,100	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", ...	154.194824	73.440666	SN Ib	SN2006gi
553,502,723,412,288,600	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", ...	93.384712	69.730331	SN Ic	SN2007iq
610,586,881,407,441,400	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", ...	234.874329	24.434811	SN Ib	SN2009er
678,505,876,556,635,300	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "I", "I", "I", "J", "J", "J", "J", "J", "J", "J", ...	248.159454	41.459221	SN IIb	SN2008cw
69,513,900,429,126,850	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "I", "I", ...	46.453999	36.769611	SN Ic	SN2005ek
708,628,372,409,470,000	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", ...	267.088287	54.15144	SN Ic	SN2007cl
717,993,244,981,365,600	{ "band": [ "B", "B", "B", "H", "H", "H", "I", "I", "I", "J", "J", "J", "Ks", "Ks", "Ks", "R", "R", "R", "U", "U", "U", "V", "V", "V", "i'", "i'", "i'", "r'", "r'", "r'", "u'", "u'", "u'...	264.617432	61.03714	SN Ic	SN2008an
730,465,052,017,719,200	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "H", ...	196.445709	27.73567	SN Ic	SN2005az
737,167,435,107,677,600	{ "band": [ "B", "B", "H", "H", "I", "I", "J", "J", "Ks", "Ks", "R", "R", "U", "U", "V", "V", "i'", "i'", "r'", "r'", "u'", "u'" ], "time": [ 52266.5234375, 52320.46484375, 0, 0, 52266.53125, 52320.4687...	198.349533	36.638248	SN IIb	SN2001gd
742,844,162,613,118,200	{ "band": [ "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "I", "J", "J", "J", "J", "J", "J", "Ks", "Ks", "Ks", "Ks", "Ks", "Ks", "R", "R", "R", ...	214.132507	39.587749	SN Ib	SN2006cb

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mmu_cfa_seccsn HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_cfa_seccsn.

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_cfa_seccsn")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_cfa_seccsn",
    search_filter=lsdb.ConeSearch(ra=41.0, dec=37.0, 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_cfa_seccsn � 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_cfa_seccsn_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
64	5	62	192.4 MiB	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.mag`	`lightcurve.mag_err`	`ra`	`dec`	`obj_type`	`object_id`
Data Type	int64	list[string]	list[float]	list[float]	list[float]	double	double	string	string
Nested?	�	lightcurve	lightcurve	lightcurve	lightcurve	�	�	�	�
Value count	64	16,291	16,291	16,291	16,291	64	64	64	64
Example row	166916028924337739	[B, B, B, B, B, B, B, B, B, B, B, � (748 total)]	[5.435e+04, 5.435e+04, 5.435e+04, � (748 total)]	[14.11, 14.23, 14.35, 14.73, 14.85, � (748 total)]	[0.014, 0.014, 0.014, 0.014, 0.014, � (748 total)]	40.87	37.35	SN Ib/c	SN2007gr
Minimum value	27677490352178670	B	-0.0	-0.0	-0.0	0.3315800130367279	-25.708139419555664	SN II-pec	SN2001ej
Maximum value	2870298439381265425	u'	55244.17578125	22.079999923706055	0.7300000190734863	350.2640686035156	74.57247161865234	SN Ic-pec	SN2009jf

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

crossmatched = mmu_cfa_seccsn.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 Harvard-Smithsonian Center for Astrophysics (CfA) Supernova Group observations and contains light curves of stripped-envelope core-collapse supernovae observed between 2000 and 2011.

Data modality
The dataset consists of light curve data for 64 stripped-envelope core-collapse supernovae in the u’UBVRIr’i’JHKs bands. Each sample includes sky coordinates (ra, dec), object identifiers and classification, and a light curve with time (modified Julian date), band, magnitude (mag), and magnitude error (mag_err).

Typical use cases
The dataset is used in studies of core-collapse supernovae and their observational properties.

Caveats
The dataset follows the CfA data structure. The flux and flux_err fields are replaced by mag and mag_err fields, which present the original photometric measurements in the standard photometric system. It represents a specific class of supernovae (stripped-envelope core-collapse), which differs from Type Ia samples.

Citation
Data are obtained from the CfA Supernova Archive. Users should cite the corresponding CfA-SECCSN publication and follow the archive acknowledgement guidelines.

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Collection including UniverseTBD/mmu_cfa_seccsn

Multimodal Universe 🫶 LSDB/HATS 🎩 💫

Collection

Multimodal Universe Datasets in HATS format (https://hats.readthedocs.io/) • 38 items • Updated 10 days ago • 3

Paper for UniverseTBD/mmu_cfa_seccsn

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data

Paper • 2412.02527 • Published Dec 3, 2024 • 13