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mmu_jwst_primer_uds_grizli_v6.0_all_96

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1,244,729,608,448,616,000	{"band":["jwst_nircam_f090w","jwst_nircam_f115w","jwst_nircam_f150w","jwst_nircam_f200w","jwst_nirca(...TRUNCATED)	2.18485	1,157,611.668872	131,363,840.00133	259,896,081.402094	90,268,775.101358	111,229,198,726.14096	57,859,541.646311	567,350,570.212512	37,218,199,945.63257	1,113,083,169.633554	9,422,855,840.771599	9.163262	29.171089	9.995604	6.384659	4.269809	5,094.724978	8,249	6.295175	0.00593	0.011421	0.02233	0.392471	66.692664	0.136128	0.004252	-0.016071	0.022157	4,036.809412	2,290,867.652768	64.795677	111,229,198,726.14096	0.000187	0.000069	0.000049	0.001005	0.001929	0.003744	6.39215	8.014905	9.388576	0.096156	0.084711	0.077359	0.096156	0.084711	0.077359	0	0	0	0.639496	1.255338	2.198828	0.00962	0.013268	0.018118	0	0	0	6.39215	8.014905	9.388576	1	0.00634	0.01219	0.023746	9.252382	10.930821	12.793147	0.172493	0.15139	0.141739	0.178577	0.156729	0.146739	0	0	0	0.925645	1.712044	2.996187	0.017257	0.023711	0.033196	9.578738	11.31638	13.244396	1.035273	0.000652	0.001222	0.002241	13.835387	16.536502	18.817453	0.111684	0.097784	0.08793	0.111684	0.097784	0.08793	0	0	0	1.384147	2.590037	4.407095	0.011173	0.015315	0.020594	0	0	0	13.835387	16.536502	18.817453	1	0.00656	0.012634	0.02469	14.627182	16.615315	18.802944	0.120425	0.103838	0.093981	0.125461	0.10818	0.097911	0	0	0	1.463361	2.602381	4.403697	0.012048	0.016264	0.022011	0	0	0	15.238871	17.310145	19.589257	1.041819	0.011275	0.021709	0.0424	19.125134	21.647768	24.364905	0.184075	0.156013	0.139465	0.192457	0.163117	0.145816	0	0	0	1.913354	3.390591	5.706322	0.018416	0.024436	0.032663	19.995986	22.633488	25.474348	1.045534	0.001614	0.003091	0.005971	30.165904	33.58598	36.310789	0.127192	0.108172	0.094249	0.127192	0.108172	0.094249	0	0	0	3.017917	5.260418	8.504078	0.012725	0.016943	0.022073	0	0	0	30.165904	33.58598	36.310789	1	0.012452	0.023964	0.046775	25.312627	28.432895	31.731286	0.16847	0.144239	0.129176	0.176679	0.151267	0.13547	0	0	0	2.532376	4.453314	7.431547	0.016854	0.022592	0.030253	0	0	0	26.545986	29.81829	33.277394	1.048725	0.005089	0.009805	0.019175	55.853887	58.932338	61.186928	0.149512	0.123749	0.104798	0.149512	0.123749	0.104798	0	0	0	5.587845	9.230303	14.330133	0.014958	0.019382	0.024544	0	0	0	55.853887	58.932338	61.186928	1	0.008335	0.01606	0.031431	80.826204	81.03392	80.991992	0.178288	0.143754	0.11878	0.178288	0.143754	0.11878	0	0	0	8.086175	12.691972	18.968529	0.017837	0.022516	0.027819	0	0	0	80.826204	81.03392	80.991992	1	null	null	null	-99	-99	-99	-99	-99	-99	-99	-99	-99	32	32	32	null	null	null	null	null	null	63.617251	122.718954	240.528325	-99	-99	-99	1.027133	0.007953	0.015322	0.029971	67.379275	66.902539	66.420431	0.157435	0.126336	0.103146	0.157435	0.126336	0.103146	0	0	0	6.740891	10.478639	15.555833	0.01575	0.019788	0.024157	0	0	0	67.379275	66.902539	66.420431	1	0.011305	0.021778	0.042583	55.25896	54.827577	54.233592	0.200206	0.162575	0.13572	0.200206	0.162575	0.13572	0	0	0	5.528326	8.587393	12.701644	0.020029	0.025463	0.031786	0	0	0	55.25896	54.827577	54.233592	1	null	null	null	-99	-99	-99	-99	-99	-99	-99	-99	-99	32	32	32	null	null	null	null	null	null	63.617251	122.718954	240.528325	-99	-99	-99	1.036776	0.007594	0.014615	0.028497	52.000933	51.901647	51.879707	0.146381	0.119265	0.100321	0.146381	0.119265	0.100321	0	0	0	5.20238	8.129118	12.150358	0.014645	0.01868	0.023495	0	0	0	52.000933	51.901647	51.879707	1	null	null	null	-99	-99	-99	-99	-99	-99	-99	-99	-99	32	32	32	null	null	null	null	null	null	63.617251	122.718954	240.528325	-99	-99	-99	1.037007	null	null	null	-99	-99	-99	-99	-99	-99	-99	-99	-99	32	32	32	null	null	null	null	null	null	63.617251	122.718954	240.528325	-99	-99	-99	1.038635	null	null	null	-99	-99	-99	-99	-99	-99	-99	-99	-99	32	32	32	null	null	null	null	null	null	63.617251	122.718954	240.528325	-99	-99	-99	1.040079	null	null	null	-99	-99	-99	-99	-99	-99	-99	-99	-99	32	32	32	null	null	null	null	null	null	63.617251	122.718954	240.528325	-99	-99	-99	1.050648	0	0	0	0	32	66.749908	6.482417	10.148651	15.175311	64.795677	66.749225	19.822963	7.634192	45.358617	32	0.008028	0.0101	0.012435	0.022835	0.025547	1,105	2,273	2.971702	40.270353	44,036.710977	9,022.92217	1.483096	19.322789	19.338884	0.000383	0	0	0	86,344,094,733.16281	0.011218	8,252	2,282	12	2282	0.164387	8.255592	1.29188	0.352574	1.21429	0.328543	1.706593	0.342896	5.190371	0.513205	10.545458	0.331968	17.233145	0.421028	26.802992	0.604399	1.229728	0.310147	3.580828	0.254987	17.280827	0.418457	3.662473	0.379921	52.778252	0.911703	1.831706	0.360502	7.43966	0.302526	3.902267	0.365743	0.026045	7,882	1.045534	1,011.349398	747.177161	1,012.349398	34.527978	1,011	1,096	926	1,011	270.97223	969.829079	143.404453	970.829079	-5.316193	970	1,016	922	970	0.657118	0.664396	0.677877	0.714398	0.767325	0.681944	0.669906	8.837607	0.013315	0.669906	8.837607	0.013315	0.665208	-1	34.527978	-5.316193

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mmu_jwst_primer_uds_grizli_v6.0_all_96 HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_jwst_primer_uds_grizli_v6.0_all_96.

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_jwst_primer_uds_grizli_v6.0_all_96")

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_jwst_primer_uds_grizli_v6.0_all_96 — 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_jwst_primer_uds_grizli_v6.0_all_96_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
64,679	647	11	6.1 GiB	hats-import v0.7.3, hats v0.7.3

Catalog columns

The main HATS catalog contains the following columns:

Name	`_healpix_29`	`image.band`	`image.flux`	`image.psf_fwhm`	`image.scale`	`Av`	`L1400`	`L2800`	`LHa`	`LHb`	`LIR`	`LOII`	`LOIII`	`Lj`	`Lu`	`Lv`	`MLv`	`a_image`	`apcorr_0`	`apcorr_1`	`apcorr_2`	`area_auto`	`area_iso`	`b_image`	`bkg_aper_0`	`bkg_aper_1`	`bkg_aper_2`	`bkg_auto`	`cflux`	`cpeak`	`cxx_image`	`cxy_image`	`cyy_image`	`dL`	`dummy_err`	`dummy_flux`	`energy_abs`	`errx2`	`errxy`	`erry2`	`f090w_bkg_aper_0`	`f090w_bkg_aper_1`	`f090w_bkg_aper_2`	`f090w_corr_0`	`f090w_corr_1`	`f090w_corr_2`	`f090w_ecorr_0`	`f090w_ecorr_1`	`f090w_ecorr_2`	`f090w_etot_0`	`f090w_etot_1`	`f090w_etot_2`	`f090w_flag_aper_0`	`f090w_flag_aper_1`	`f090w_flag_aper_2`	`f090w_flux_aper_0`	`f090w_flux_aper_1`	`f090w_flux_aper_2`	`f090w_fluxerr_aper_0`	`f090w_fluxerr_aper_1`	`f090w_fluxerr_aper_2`	`f090w_mask_aper_0`	`f090w_mask_aper_1`	`f090w_mask_aper_2`	`f090w_tot_0`	`f090w_tot_1`	`f090w_tot_2`	`f090w_tot_corr`	`f105w_bkg_aper_0`	`f105w_bkg_aper_1`	`f105w_bkg_aper_2`	`f105w_corr_0`	`f105w_corr_1`	`f105w_corr_2`	`f105w_ecorr_0`	`f105w_ecorr_1`	`f105w_ecorr_2`	`f105w_etot_0`	`f105w_etot_1`	`f105w_etot_2`	`f105w_flag_aper_0`	`f105w_flag_aper_1`	`f105w_flag_aper_2`	`f105w_flux_aper_0`	`f105w_flux_aper_1`	`f105w_flux_aper_2`	`f105w_fluxerr_aper_0`	`f105w_fluxerr_aper_1`	`f105w_fluxerr_aper_2`	`f105w_mask_aper_0`	`f105w_mask_aper_1`	`f105w_mask_aper_2`	`f105w_tot_0`	`f105w_tot_1`	`f105w_tot_2`	`f105w_tot_corr`	`f115w_bkg_aper_0`	`f115w_bkg_aper_1`	`f115w_bkg_aper_2`	`f115w_corr_0`	`f115w_corr_1`	`f115w_corr_2`	`f115w_ecorr_0`	`f115w_ecorr_1`	`f115w_ecorr_2`	`f115w_etot_0`	`f115w_etot_1`	`f115w_etot_2`	`f115w_flag_aper_0`	`f115w_flag_aper_1`	`f115w_flag_aper_2`	`f115w_flux_aper_0`	`f115w_flux_aper_1`	`f115w_flux_aper_2`	`f115w_fluxerr_aper_0`	`f115w_fluxerr_aper_1`	`f115w_fluxerr_aper_2`	`f115w_mask_aper_0`	`f115w_mask_aper_1`	`f115w_mask_aper_2`	`f115w_tot_0`	`f115w_tot_1`	`f115w_tot_2`	`f115w_tot_corr`	`f125w_bkg_aper_0`	`f125w_bkg_aper_1`	`f125w_bkg_aper_2`	`f125w_corr_0`	`f125w_corr_1`	`f125w_corr_2`	`f125w_ecorr_0`	`f125w_ecorr_1`	`f125w_ecorr_2`	`f125w_etot_0`	`f125w_etot_1`	`f125w_etot_2`	`f125w_flag_aper_0`	`f125w_flag_aper_1`	`f125w_flag_aper_2`	`f125w_flux_aper_0`	`f125w_flux_aper_1`	`f125w_flux_aper_2`	`f125w_fluxerr_aper_0`	`f125w_fluxerr_aper_1`	`f125w_fluxerr_aper_2`	`f125w_mask_aper_0`	`f125w_mask_aper_1`	`f125w_mask_aper_2`	`f125w_tot_0`	`f125w_tot_1`	`f125w_tot_2`	`f125w_tot_corr`	`f140w_bkg_aper_0`	`f140w_bkg_aper_1`	`f140w_bkg_aper_2`	`f140w_corr_0`	`f140w_corr_1`	`f140w_corr_2`	`f140w_ecorr_0`	`f140w_ecorr_1`	`f140w_ecorr_2`	`f140w_etot_0`	`f140w_etot_1`	`f140w_etot_2`	`f140w_flag_aper_0`	`f140w_flag_aper_1`	`f140w_flag_aper_2`	`f140w_flux_aper_0`	`f140w_flux_aper_1`	`f140w_flux_aper_2`	`f140w_fluxerr_aper_0`	`f140w_fluxerr_aper_1`	`f140w_fluxerr_aper_2`	`f140w_mask_aper_0`	`f140w_mask_aper_1`	`f140w_mask_aper_2`	`f140w_tot_0`	`f140w_tot_1`	`f140w_tot_2`	`f140w_tot_corr`	`f150w_bkg_aper_0`	`f150w_bkg_aper_1`	`f150w_bkg_aper_2`	`f150w_corr_0`	`f150w_corr_1`	`f150w_corr_2`	`f150w_ecorr_0`	`f150w_ecorr_1`	`f150w_ecorr_2`	`f150w_etot_0`	`f150w_etot_1`	`f150w_etot_2`	`f150w_flag_aper_0`	`f150w_flag_aper_1`	`f150w_flag_aper_2`	`f150w_flux_aper_0`	`f150w_flux_aper_1`	`f150w_flux_aper_2`	`f150w_fluxerr_aper_0`	`f150w_fluxerr_aper_1`	`f150w_fluxerr_aper_2`	`f150w_mask_aper_0`	`f150w_mask_aper_1`	`f150w_mask_aper_2`	`f150w_tot_0`	`f150w_tot_1`	`f150w_tot_2`	`f150w_tot_corr`	`f160w_bkg_aper_0`	`f160w_bkg_aper_1`	`f160w_bkg_aper_2`	`f160w_corr_0`	`f160w_corr_1`	`f160w_corr_2`	`f160w_ecorr_0`	`f160w_ecorr_1`	`f160w_ecorr_2`	`f160w_etot_0`	`f160w_etot_1`	`f160w_etot_2`	`f160w_flag_aper_0`	`f160w_flag_aper_1`	`f160w_flag_aper_2`	`f160w_flux_aper_0`	`f160w_flux_aper_1`	`f160w_flux_aper_2`	`f160w_fluxerr_aper_0`	`f160w_fluxerr_aper_1`	`f160w_fluxerr_aper_2`	`f160w_mask_aper_0`	`f160w_mask_aper_1`	`f160w_mask_aper_2`	`f160w_tot_0`	`f160w_tot_1`	`f160w_tot_2`	`f160w_tot_corr`	`f200w_bkg_aper_0`	`f200w_bkg_aper_1`	`f200w_bkg_aper_2`	`f200w_corr_0`	`f200w_corr_1`	`f200w_corr_2`	`f200w_ecorr_0`	`f200w_ecorr_1`	`f200w_ecorr_2`	`f200w_etot_0`	`f200w_etot_1`	`f200w_etot_2`	`f200w_flag_aper_0`	`f200w_flag_aper_1`	`f200w_flag_aper_2`	`f200w_flux_aper_0`	`f200w_flux_aper_1`	`f200w_flux_aper_2`	`f200w_fluxerr_aper_0`	`f200w_fluxerr_aper_1`	`f200w_fluxerr_aper_2`	`f200w_mask_aper_0`	`f200w_mask_aper_1`	`f200w_mask_aper_2`	`f200w_tot_0`	`f200w_tot_1`	`f200w_tot_2`	`f200w_tot_corr`	`f277w_bkg_aper_0`	`f277w_bkg_aper_1`	`f277w_bkg_aper_2`	`f277w_corr_0`	`f277w_corr_1`	`f277w_corr_2`	`f277w_ecorr_0`	`f277w_ecorr_1`	`f277w_ecorr_2`	`f277w_etot_0`	`f277w_etot_1`	`f277w_etot_2`	`f277w_flag_aper_0`	`f277w_flag_aper_1`	`f277w_flag_aper_2`	`f277w_flux_aper_0`	`f277w_flux_aper_1`	`f277w_flux_aper_2`	`f277w_fluxerr_aper_0`	`f277w_fluxerr_aper_1`	`f277w_fluxerr_aper_2`	`f277w_mask_aper_0`	`f277w_mask_aper_1`	`f277w_mask_aper_2`	`f277w_tot_0`	`f277w_tot_1`	`f277w_tot_2`	`f277w_tot_corr`	`f350lpu_bkg_aper_0`	`f350lpu_bkg_aper_1`	`f350lpu_bkg_aper_2`	`f350lpu_corr_0`	`f350lpu_corr_1`	`f350lpu_corr_2`	`f350lpu_ecorr_0`	`f350lpu_ecorr_1`	`f350lpu_ecorr_2`	`f350lpu_etot_0`	`f350lpu_etot_1`	`f350lpu_etot_2`	`f350lpu_flag_aper_0`	`f350lpu_flag_aper_1`	`f350lpu_flag_aper_2`	`f350lpu_flux_aper_0`	`f350lpu_flux_aper_1`	`f350lpu_flux_aper_2`	`f350lpu_fluxerr_aper_0`	`f350lpu_fluxerr_aper_1`	`f350lpu_fluxerr_aper_2`	`f350lpu_mask_aper_0`	`f350lpu_mask_aper_1`	`f350lpu_mask_aper_2`	`f350lpu_tot_0`	`f350lpu_tot_1`	`f350lpu_tot_2`	`f350lpu_tot_corr`	`f356w_bkg_aper_0`	`f356w_bkg_aper_1`	`f356w_bkg_aper_2`	`f356w_corr_0`	`f356w_corr_1`	`f356w_corr_2`	`f356w_ecorr_0`	`f356w_ecorr_1`	`f356w_ecorr_2`	`f356w_etot_0`	`f356w_etot_1`	`f356w_etot_2`	`f356w_flag_aper_0`	`f356w_flag_aper_1`	`f356w_flag_aper_2`	`f356w_flux_aper_0`	`f356w_flux_aper_1`	`f356w_flux_aper_2`	`f356w_fluxerr_aper_0`	`f356w_fluxerr_aper_1`	`f356w_fluxerr_aper_2`	`f356w_mask_aper_0`	`f356w_mask_aper_1`	`f356w_mask_aper_2`	`f356w_tot_0`	`f356w_tot_1`	`f356w_tot_2`	`f356w_tot_corr`	`f410m_bkg_aper_0`	`f410m_bkg_aper_1`	`f410m_bkg_aper_2`	`f410m_corr_0`	`f410m_corr_1`	`f410m_corr_2`	`f410m_ecorr_0`	`f410m_ecorr_1`	`f410m_ecorr_2`	`f410m_etot_0`	`f410m_etot_1`	`f410m_etot_2`	`f410m_flag_aper_0`	`f410m_flag_aper_1`	`f410m_flag_aper_2`	`f410m_flux_aper_0`	`f410m_flux_aper_1`	`f410m_flux_aper_2`	`f410m_fluxerr_aper_0`	`f410m_fluxerr_aper_1`	`f410m_fluxerr_aper_2`	`f410m_mask_aper_0`	`f410m_mask_aper_1`	`f410m_mask_aper_2`	`f410m_tot_0`	`f410m_tot_1`	`f410m_tot_2`	`f410m_tot_corr`	`f435w_bkg_aper_0`	`f435w_bkg_aper_1`	`f435w_bkg_aper_2`	`f435w_corr_0`	`f435w_corr_1`	`f435w_corr_2`	`f435w_ecorr_0`	`f435w_ecorr_1`	`f435w_ecorr_2`	`f435w_etot_0`	`f435w_etot_1`	`f435w_etot_2`	`f435w_flag_aper_0`	`f435w_flag_aper_1`	`f435w_flag_aper_2`	`f435w_flux_aper_0`	`f435w_flux_aper_1`	`f435w_flux_aper_2`	`f435w_fluxerr_aper_0`	`f435w_fluxerr_aper_1`	`f435w_fluxerr_aper_2`	`f435w_mask_aper_0`	`f435w_mask_aper_1`	`f435w_mask_aper_2`	`f435w_tot_0`	`f435w_tot_1`	`f435w_tot_2`	`f435w_tot_corr`	`f444w_bkg_aper_0`	`f444w_bkg_aper_1`	`f444w_bkg_aper_2`	`f444w_corr_0`	`f444w_corr_1`	`f444w_corr_2`	`f444w_ecorr_0`	`f444w_ecorr_1`	`f444w_ecorr_2`	`f444w_etot_0`	`f444w_etot_1`	`f444w_etot_2`	`f444w_flag_aper_0`	`f444w_flag_aper_1`	`f444w_flag_aper_2`	`f444w_flux_aper_0`	`f444w_flux_aper_1`	`f444w_flux_aper_2`	`f444w_fluxerr_aper_0`	`f444w_fluxerr_aper_1`	`f444w_fluxerr_aper_2`	`f444w_mask_aper_0`	`f444w_mask_aper_1`	`f444w_mask_aper_2`	`f444w_tot_0`	`f444w_tot_1`	`f444w_tot_2`	`f444w_tot_corr`	`f606w_bkg_aper_0`	`f606w_bkg_aper_1`	`f606w_bkg_aper_2`	`f606w_corr_0`	`f606w_corr_1`	`f606w_corr_2`	`f606w_ecorr_0`	`f606w_ecorr_1`	`f606w_ecorr_2`	`f606w_etot_0`	`f606w_etot_1`	`f606w_etot_2`	`f606w_flag_aper_0`	`f606w_flag_aper_1`	`f606w_flag_aper_2`	`f606w_flux_aper_0`	`f606w_flux_aper_1`	`f606w_flux_aper_2`	`f606w_fluxerr_aper_0`	`f606w_fluxerr_aper_1`	`f606w_fluxerr_aper_2`	`f606w_mask_aper_0`	`f606w_mask_aper_1`	`f606w_mask_aper_2`	`f606w_tot_0`	`f606w_tot_1`	`f606w_tot_2`	`f606w_tot_corr`	`f775w_bkg_aper_0`	`f775w_bkg_aper_1`	`f775w_bkg_aper_2`	`f775w_corr_0`	`f775w_corr_1`	`f775w_corr_2`	`f775w_ecorr_0`	`f775w_ecorr_1`	`f775w_ecorr_2`	`f775w_etot_0`	`f775w_etot_1`	`f775w_etot_2`	`f775w_flag_aper_0`	`f775w_flag_aper_1`	`f775w_flag_aper_2`	`f775w_flux_aper_0`	`f775w_flux_aper_1`	`f775w_flux_aper_2`	`f775w_fluxerr_aper_0`	`f775w_fluxerr_aper_1`	`f775w_fluxerr_aper_2`	`f775w_mask_aper_0`	`f775w_mask_aper_1`	`f775w_mask_aper_2`	`f775w_tot_0`	`f775w_tot_1`	`f775w_tot_2`	`f775w_tot_corr`	`f814w_bkg_aper_0`	`f814w_bkg_aper_1`	`f814w_bkg_aper_2`	`f814w_corr_0`	`f814w_corr_1`	`f814w_corr_2`	`f814w_ecorr_0`	`f814w_ecorr_1`	`f814w_ecorr_2`	`f814w_etot_0`	`f814w_etot_1`	`f814w_etot_2`	`f814w_flag_aper_0`	`f814w_flag_aper_1`	`f814w_flag_aper_2`	`f814w_flux_aper_0`	`f814w_flux_aper_1`	`f814w_flux_aper_2`	`f814w_fluxerr_aper_0`	`f814w_fluxerr_aper_1`	`f814w_fluxerr_aper_2`	`f814w_mask_aper_0`	`f814w_mask_aper_1`	`f814w_mask_aper_2`	`f814w_tot_0`	`f814w_tot_1`	`f814w_tot_2`	`f814w_tot_corr`	`f850lp_bkg_aper_0`	`f850lp_bkg_aper_1`	`f850lp_bkg_aper_2`	`f850lp_corr_0`	`f850lp_corr_1`	`f850lp_corr_2`	`f850lp_ecorr_0`	`f850lp_ecorr_1`	`f850lp_ecorr_2`	`f850lp_etot_0`	`f850lp_etot_1`	`f850lp_etot_2`	`f850lp_flag_aper_0`	`f850lp_flag_aper_1`	`f850lp_flag_aper_2`	`f850lp_flux_aper_0`	`f850lp_flux_aper_1`	`f850lp_flux_aper_2`	`f850lp_fluxerr_aper_0`	`f850lp_fluxerr_aper_1`	`f850lp_fluxerr_aper_2`	`f850lp_mask_aper_0`	`f850lp_mask_aper_1`	`f850lp_mask_aper_2`	`f850lp_tot_0`	`f850lp_tot_1`	`f850lp_tot_2`	`f850lp_tot_corr`	`flag`	`flag_aper_0`	`flag_aper_1`	`flag_aper_2`	`flag_auto`	`flux`	`flux_aper_0`	`flux_aper_1`	`flux_aper_2`	`flux_auto`	`flux_iso`	`flux_radius`	`flux_radius_20`	`flux_radius_90`	`flux_radius_flag`	`fluxerr_aper_0`	`fluxerr_aper_1`	`fluxerr_aper_2`	`fluxerr_auto`	`fluxerr_iso`	`healpix`	`index`	`kron_radius`	`kron_rcirc`	`lc_max`	`lc_min`	`lwAgeV`	`mag_auto`	`mag_iso`	`magerr_auto`	`mask_aper_0`	`mask_aper_1`	`mask_aper_2`	`mass`	`min_risk`	`npix`	`number`	`nusefilt`	`object_id`	`peak`	`raw_chi2`	`rest120`	`rest120_err`	`rest121`	`rest121_err`	`rest156`	`rest156_err`	`rest157`	`rest157_err`	`rest158`	`rest158_err`	`rest159`	`rest159_err`	`rest160`	`rest160_err`	`rest414`	`rest414_err`	`rest415`	`rest415_err`	`rest416`	`rest416_err`	`restB`	`restB_err`	`restJ`	`restJ_err`	`restU`	`restU_err`	`restV`	`restV_err`	`sfr`	`theta_image`	`thresh`	`tnpix`	`tot_corr`	`x`	`x2_image`	`x_image`	`x_world`	`xcpeak`	`xmax`	`xmin`	`xpeak`	`xy_image`	`y`	`y2_image`	`y_image`	`y_world`	`ycpeak`	`ymax`	`ymin`	`ypeak`	`z025`	`z160`	`z500`	`z840`	`z975`	`z_min_risk`	`z_ml`	`z_ml_chi2`	`z_ml_risk`	`z_phot`	`z_phot_chi2`	`z_phot_risk`	`z_raw_chi2`	`z_spec`	`ra`	`dec`
Data Type	int64	list[string]	list[list<element: list<element: float>>]	list[float]	list[float]	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int64	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	double	int16	int16	int16	double	double	double	double	double	double	double	double	double	double	double	double	double	int64	int16	int16	int16	int64	double	double	double	double	double	double	double	double	double	int16	double	double	double	double	double	int64	int64	double	double	double	double	double	double	double	double	double	double	double	double	float	int64	int32	int64	string	double	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	double	double	double	int64	double	double	double	double	double	int64	int64	int64	int64	double	double	double	double	double	int64	int64	int64	int64	float	float	float	float	float	float	float	float	float	float	float	float	float	double	double	double
Nested?	—	image	image	image	image	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—

"Nested" indicates whether the column is stored as a nested field inside another "struct" column.

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_jwst_primer_uds_grizli_v6.0_all_96 = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_jwst_primer_uds_grizli_v6.0_all_96")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_jwst_primer_uds_grizli_v6.0_all_96.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 James Webb Space Telescope (JWST) NIRCam observations from early deep field surveys.

Data modality
The dataset consists of fixed-size image cutouts (96×96 pixels) centered on sources from photometric catalogs. The images are multi-band, with 6 or 7 filters covering wavelengths from approximately 0.9μm to 4.4μm.

Typical use cases
Images from these JWST deep field surveys have been used in a large number of scientific publications, including machine learning applications.

Caveats
Different surveys have different wavelength coverage, and missing bands are represented as arrays of zeros to simplify data loading.

Citation
The data are in the public domain. The dataset uses data products retrieved from the Dawn JWST Archive (DJA), an initiative of the Cosmic Dawn Center (DAWN).

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Collection including UniverseTBD/mmu_jwst_primer_uds_grizli_v6.0_all_96

Multimodal Universe 🫶 HATS 🎩 💫

Collection

Multimodal Universe Datasets and friends in HATS format • 43 items • Updated 18 days ago • 10

Paper for UniverseTBD/mmu_jwst_primer_uds_grizli_v6.0_all_96

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

Paper • 2412.02527 • Published Dec 3, 2024 • 13