Datasets:

UniverseTBD
/

mmu_csp_csp

_healpix_29 int64	lightcurve dict	redshift float32	ra float64	dec float64	spec_class string	object_id string
70,274,246,734,234	{ "band": [ "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", "J", "J", "J", "J", "J", "J", ...	0.0726	44.972374	1.160722	SN Ia	SN2007jd
2,778,972,986,467,636,700	{ "band": [ "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", "H", "H", "H", "H", "H", "H", "H", ...	0.0389	104.393585	-45.812305	SN Ia	SN2008O
2,788,523,459,996,487,000	{ "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", ...	0.034	100.26046	-38.038612	SN Ia	SN2008bq
2,803,255,825,911,804,400	{ "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", "B", "B", "B", "B", "B", "B", "B", ...	0.0057	109.135834	-29.3255	SN Ia	SN2008fp
2,805,148,384,331,415,000	{ "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", "B", "B", "B", "B", "B", "B", "H", "H", ...	0.0086	107.920044	-26.685083	SN Ia	SN2009ag
2,825,012,003,497,899,000	{ "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", "B", "H", "H", "H", "H", "H", "H", "H", ...	0.0534	138.823456	-25.600082	SN Ia	SN2006lu
2,835,467,422,749,439,500	{ "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", "J", "J", ...	0.0122	149.826996	-19.473833	SN Ia	SN2007al
2,841,462,459,912,026,600	{ "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", ...	0.0079	139.051956	-16.304443	SN Ia	SN2005am
2,856,956,135,660,962,300	{ "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", "J", "J", ...	0.0497	122.311501	-18.653639	SN Ia	SN2008hu
289,509,930,429,027,600	{ "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", "H", "H", "H", ...	0.0126	136.891922	3.39225	SN Ia	SN2008hv
3,087,570,537,128,850,000	{ "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", "B", "B", "B", "B", "B", "B", "B", "B", ...	0.0124	207.501373	-30.576166	SN Ia	SN2005al
3,096,621,977,412,214,000	{ "band": [ "B", "B", "B", "B", "B", "B", "B", "B", "H", "H", "H", "H", "H", "H", "H", "H", "J", "J", "J", "J", "J", "J", "J", "J", "Jrc2", "Jrc2", "Jrc2", "Jrc2", "Jrc2", "Jrc2", "Jrc2", "J...	0.0332	201.389923	-24.65225	SN Ia	SN2007cg
310,768,026,101,281,000	{ "band": [ "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", "H", "H", "H", ...	0.0309	152.333252	14.992444	SN Ia	SN2007ux
3,121,501,022,196,821,500	{ "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", ...	0.0317	243.223923	-21.630194	SN Ia	SN2007ai
3,135,325,505,265,373,000	{ "band": [ "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", "J", "J", "J", "J", "J", "J", "J", "J", ...	0.046	211.885666	-26.551832	SN Ia	SN2008cf
3,142,733,029,004,124,000	{ "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", "B", "B", "B", "B", "B", "H", "H", "H", ...	0.0094	220.59938	-17.246778	SN Ia	SN2009Y

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mmu_csp_csp HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_csp_csp.

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_csp_csp")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_csp_csp",
    search_filter=lsdb.ConeSearch(ra=139.0, dec=30.0, 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_csp_csp � 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_csp_csp_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
134	6	122	192.8 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`	`redshift`	`ra`	`dec`	`spec_class`	`object_id`
Data Type	int64	list[string]	list[float]	list[float]	list[float]	float	double	double	string	string
Nested?	�	lightcurve	lightcurve	lightcurve	lightcurve	�	�	�	�	�
Value count	134	36,120	36,120	36,120	36,120	134	134	134	134	134
Example row	349459259295784838	[B, B, B, B, B, B, B, B, B, B, B, � (144 total)]	[1938, 1939, 1943, 1946, 1947, � (144 total)]	[16.01, 15.93, 15.78, 15.85, 15.89, � (144 total)]	[0.007, 0.007, 0.007, 0.007, 0.007, � (144 total)]	0.0211	138.8	29.74	SN Ia	SN2009cz
Minimum value	70274246734234	B	-0.0	-0.0	-0.0	0.003700000001117587	1.0079580545425415	-80.17755889892578	SN Ia	SN2004dt
Maximum value	3410168382924176502	u	2305.530029296875	22.347000122070312	0.20000000298023224	0.08349999785423279	359.6354064941406	29.735305786132812	SN Ia	SN2010ae

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

crossmatched = mmu_csp_csp.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 third data release of the first stage of the Carnegie Supernova Project (CSP) and contains light curves of 134 spectroscopically confirmed Type Ia supernovae observed between 2004 and 2009.

Data modality
The dataset consists of light curve data. The data fields are identical to those of the CfA datasets, with measurements provided in the ugriBVYJH bands.

Typical use cases
The dataset has been used in many studies, including those mentioned in the CfA section, as well as several CSP studies.

Caveats
The dataset uses different photometric bands (ugriBVYJH) while keeping the same data fields as the CfA datasets. No preprocessing has been applied.

Citation
The dataset is released under the CC BY 4.0 license. Users should cite the corresponding data release paper and may access the data through the CSP data products page.

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Collection including UniverseTBD/mmu_csp_csp

Multimodal Universe 🫶 LSDB/HATS 🎩 💫

Collection

Multimodal Universe Datasets in HATS format (https://hats.readthedocs.io/) • 39 items • Updated about 13 hours ago • 3

Paper for UniverseTBD/mmu_csp_csp

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

Paper • 2412.02527 • Published Dec 3, 2024 • 13