Cross-matching of large catalogs: DES to Gaia

In this tutorial we cross-match tables of Dark Energy Survey (DES) DR2 and Gaia DR3 catalogs. The outline of the tutorial is as follows:

1. Get original data files

2. Convert the data to HATS format using hats-import

3. Cross-match the catalogs using LSDB and save to a new HATS catalog

Install required packages and import modules

# Comment to skip hats-import installation
%pip install --quiet hats-import

# Uncomment to install lsdb
# %pip install --quiet lsdb

Note: you may need to restart the kernel to use updated packages.

# For files and directories manipulation
from pathlib import Path

# For Gaia columns data types inference
import pyarrow
from astropy.io import ascii

# We need this for 2D histogram
import dask.array

# Client for Dask distributed computing
from dask.distributed import Client

# For plots
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

# Numpy is always useful!
import numpy as np

# Explore the HATS catalogs and plot sky maps
from hats import read_hats
from hats.inspection import plot_pixels

# For reading the HATS catalogs and performing the cross-match
import lsdb

# For converting the data to HATS format and generate margin caches
from hats_import.catalog.file_readers import CsvReader
from hats_import.margin_cache.margin_cache_arguments import MarginCacheArguments
from hats_import.pipeline import ImportArguments, pipeline_with_client

Get original data files

To make this notebook faster to run and using less storage, we will use a subset of the data. However, the same pipeline, with no code changes, may be used to process the full catalogs.

# Change to the directories where the data will be stored
DES_DIR = Path("data/DES_DR2")
GAIA_DIR = Path("data/Gaia_DR3")

GAIA_SCHEMA_FILE = GAIA_DIR / "schema.parquet"

DES_HATS_NAME = "des_dr2"
GAIA_HATS_NAME = "gaia_dr3"
GAIA_MARGIN_CACHE_NAME = "gaia_dr3_1arcsec"
XMATCH_NAME = "des_dr2_x_gaia_dr3"

HATS_DIR = Path("catalogs")
DES_HATS_DIR = HATS_DIR / DES_HATS_NAME
GAIA_HATS_DIR = HATS_DIR / GAIA_HATS_NAME
GAIA_MARGIN_CACHE_DIR = HATS_DIR / GAIA_MARGIN_CACHE_NAME

XMATCH_DIR = HATS_DIR / XMATCH_NAME

DES DR2

The Dark Energy Survey DR2 catalog is hosted by NCSA, see the official website for more information. Data files, in FITS format, are located at https://desdr-server.ncsa.illinois.edu/despublic/dr2_tiles/. You may also prefer to get the data with S3 client as described by https://desdr-server.ncsa.illinois.edu.

We use *dr2_main.fits files for the main catalog table; see the schema here.

Here we download a few first files to demonstrate the pipeline. The full catalog is about 1.1TB–feel free to download it if you have enough storage.

# Comment / skip this cell if you already have the data

from shutil import copyfileobj

import requests  # For downloading files

# Download few first files
des_urls = [
    "https://desdr-server.ncsa.illinois.edu/despublic/dr2_tiles/DES0000+0209/DES0000+0209_dr2_main.fits",
    "https://desdr-server.ncsa.illinois.edu/despublic/dr2_tiles/DES0000+0252/DES0000+0252_dr2_main.fits",
    "https://desdr-server.ncsa.illinois.edu/despublic/dr2_tiles/DES0000+0335/DES0000+0335_dr2_main.fits",
]

for des_url in des_urls:
    des_file = DES_DIR / Path(des_url).name
    if not des_file.exists():
        des_file.parent.mkdir(parents=True, exist_ok=True)
        with requests.get(des_url, stream=True) as r, open(des_file, "wb") as f:
            copyfileobj(r.raw, f)

Gaia DR3

The Gaia DR3 catalog is hosted by ESA, see the official website for more information. Data files, in ECSV format are located at http://cdn.gea.esac.esa.int/Gaia/gdr3/gaia_source/.

We use gaia_source table, see its schema here.

Here we download a few files which barely correspond to the same area of the sky as the DES DR2 files above. The full catalog is much larger, feel free to download it all if you have enough storage.

# Comment / skip this cell if you already have the data

from shutil import copyfileobj

import requests  # For downloading files

# Download few first files
gaia_urls = [
    "http://cdn.gea.esac.esa.int/Gaia/gdr3/gaia_source/GaiaSource_310878-313367.csv.gz",
]

for gaia_url in gaia_urls:
    gaia_file = GAIA_DIR / Path(gaia_url).name
    if not gaia_file.exists():
        gaia_file.parent.mkdir(parents=True, exist_ok=True)
        with requests.get(gaia_url, stream=True) as r, open(gaia_file, "wb") as f:
            copyfileobj(r.raw, f)

Create Dask client

Both hats-import and lsdb use Dask for parallel processing. We create a Dask client here to be used in all the following pipelines steps, and we destroy it at the end of the notebook.

See Dask cluster configuration documentation page to learn more.

# See Dask documentation for the options, e.g. https://distributed.dask.org/en/latest/local-cluster.html
# You would likely want to change `n_workers` to have an optimal performance while not going out of memory.
client = Client()

Convert the data to HATS format

We use the hats-import tool to create HATS catalogs from the original data files.

Convert DES DR2 to HATS

• Plan the pipeline specifying all parameters of the conversion

• Run the pipeline with Dask

des_args = ImportArguments(
    sort_columns="COADD_OBJECT_ID",
    ra_column="RA",
    dec_column="DEC",
    input_path=DES_DIR,
    file_reader="fits",
    output_artifact_name=DES_HATS_NAME,
    output_path=HATS_DIR,
)

pipeline_with_client(des_args, client)

# Read the HATS catalog metadata, it does not load any data, just healpix pixels and other metadata
des_catalog = read_hats(DES_HATS_DIR)
plot_pixels(des_catalog)

(<Figure size 1000x500 with 2 Axes>,
 <WCSAxes: title={'center': 'Catalog pixel density map - des_dr2'}>)

This is what you would get for the full catalog, it would have many healpix tiles instead of just a single one. The reason is that hats-import splits the data into multiple files to have no more than a million rows per healpix tile / HATS parquet file.

Compare it to the DES DR2 footprint:

Convert Gaia DR3 to HATS

For Gaia we need to specify schema of the input data, because currently hats-import cannot infer column data types properly from this dataset.

See this GitHub issue for more details: astronomy-commons/hats-import#225

gaia_file = next(GAIA_DIR.glob("*.csv.gz"))
empty_astropy_table = ascii.read(gaia_file, format="ecsv", data_end=1)
empty_astropy_table.write(
    GAIA_SCHEMA_FILE,
    # Uncomment to overwrite existing schema file
    # overwrite=True,
)

gaia_args = ImportArguments(
    # sort columns are optional and works only if few objects are very close to each other
    sort_columns="source_id",
    ra_column="ra",
    dec_column="dec",
    # We select all Gaia ECSV files skipping the schema file
    input_file_list=list(GAIA_DIR.glob("*.csv.gz")),
    file_reader=CsvReader(
        comment="#",
        schema_file=GAIA_SCHEMA_FILE,
        compression="gzip",
        parquet_kwargs={"dtype_backend": "pyarrow", "engine": "pyarrow"},
    ),
    use_schema_file=GAIA_SCHEMA_FILE,
    output_artifact_name=GAIA_HATS_NAME,
    output_path=HATS_DIR,
)

pipeline_with_client(gaia_args, client)

# It should be full sky for the whole catalog
gaia_catalog = read_hats(GAIA_HATS_DIR)
plot_pixels(gaia_catalog)

(<Figure size 1000x500 with 2 Axes>,
 <WCSAxes: title={'center': 'Catalog pixel density map - gaia_dr3'}>)

And this is how it would look like for the entire Gaia catalog. Notice how Milky Way region is denser and thus has higher healpix order (smaller tiles), since hats-import limits the maximum number of objects per pixel to be 1 million.

LSDB requires right-catalog margin cache to generate the complete cross-match result. Without the margin cache, the objects located near the edges of Healpix tiles may be missed in the cross-match.

margin_cache_args = MarginCacheArguments(
    input_catalog_path=GAIA_HATS_DIR,
    output_path=HATS_DIR,
    margin_threshold=5.0,  # arcsec
    output_artifact_name=GAIA_MARGIN_CACHE_NAME,
)

pipeline_with_client(margin_cache_args, client)

Cross-match the catalogs

The cross-matching in LSDB is not symmetric, meaning the choice of which catalog is “left” and which is “right” is crucial. In our case, we cross-match DES (left) to Gaia (right). This setup generally allows for multiple DES objects to be matched to a single Gaia object, a result of margin caches. Margin caches are designed to prevent missing objects near the edges of Healpix tiles. However, they can lead to multiple matches, where the same Gaia object might match to one DES object in its own tile and another DES object in the neighboring tile that includes this Gaia object in its margin cache.

However, we do not expect it to be a frequent case, because the DES catalog is much deeper than Gaia, and we use a small cross-match radius.

• Plan the pipeline with LSDB

• Run the pipeline with Dask

des_catalog = lsdb.read_hats(DES_HATS_DIR)

# gaia_margin_cache_catalog = lsdb.read_hats(GAIA_MARGIN_CACHE_DIR)
gaia_catalog = lsdb.read_hats(GAIA_HATS_DIR)

xmatched = des_catalog.crossmatch(
    gaia_catalog,
    # Up to 1 arcsec distance, it is the default
    radius_arcsec=1.0,
    # Single closest object, it is the default
    n_neighbors=1,
    # Default would be to use names of the HATS catalogs
    suffixes=("_des", "_gaia"),
)

display(des_catalog)
display(gaia_catalog)
display(xmatched)

/home/delucchi/git/docs/lsdb/src/lsdb/dask/crossmatch_catalog_data.py:108: RuntimeWarning: Right catalog does not have a margin cache. Results may be incomplete and/or inaccurate.
  warnings.warn(

lsdb Catalog des_dr2:

	COADD_OBJECT_ID	TILENAME	HPIX_32	HPIX_64	HPIX_1024	HPIX_4096	HPIX_16384	RA	DEC	ALPHAWIN_J2000	DELTAWIN_J2000	GALACTIC_L	GALACTIC_B	XWIN_IMAGE	YWIN_IMAGE	A_IMAGE	ERRA_IMAGE	B_IMAGE	ERRB_IMAGE	THETA_J2000	ERRTHETA_IMAGE	KRON_RADIUS	EBV_SFD98	MAG_AUTO_G_DERED	MAG_AUTO_R_DERED	MAG_AUTO_I_DERED	MAG_AUTO_Z_DERED	MAG_AUTO_Y_DERED	WAVG_MAG_PSF_G_DERED	WAVG_MAG_PSF_R_DERED	WAVG_MAG_PSF_I_DERED	WAVG_MAG_PSF_Z_DERED	WAVG_MAG_PSF_Y_DERED	EXTENDED_CLASS_COADD	EXTENDED_CLASS_WAVG	FLAGS_G	IMAFLAGS_ISO_G	NEPOCHS_G	FLAGS_R	IMAFLAGS_ISO_R	NEPOCHS_R	FLAGS_I	IMAFLAGS_ISO_I	NEPOCHS_I	FLAGS_Z	IMAFLAGS_ISO_Z	NEPOCHS_Z	FLAGS_Y	IMAFLAGS_ISO_Y	NEPOCHS_Y	XWIN_IMAGE_G	XWIN_IMAGE_R	XWIN_IMAGE_I	XWIN_IMAGE_Z	XWIN_IMAGE_Y	YWIN_IMAGE_G	YWIN_IMAGE_R	YWIN_IMAGE_I	YWIN_IMAGE_Z	YWIN_IMAGE_Y	X2WIN_IMAGE_G	X2WIN_IMAGE_R	X2WIN_IMAGE_I	X2WIN_IMAGE_Z	X2WIN_IMAGE_Y	Y2WIN_IMAGE_G	Y2WIN_IMAGE_R	Y2WIN_IMAGE_I	Y2WIN_IMAGE_Z	Y2WIN_IMAGE_Y	XYWIN_IMAGE_G	XYWIN_IMAGE_R	XYWIN_IMAGE_I	XYWIN_IMAGE_Z	XYWIN_IMAGE_Y	ERRX2WIN_IMAGE_G	ERRX2WIN_IMAGE_R	ERRX2WIN_IMAGE_I	ERRX2WIN_IMAGE_Z	ERRX2WIN_IMAGE_Y	ERRY2WIN_IMAGE_G	ERRY2WIN_IMAGE_R	ERRY2WIN_IMAGE_I	ERRY2WIN_IMAGE_Z	ERRY2WIN_IMAGE_Y	ERRXYWIN_IMAGE_G	ERRXYWIN_IMAGE_R	ERRXYWIN_IMAGE_I	ERRXYWIN_IMAGE_Z	ERRXYWIN_IMAGE_Y	AWIN_IMAGE_G	AWIN_IMAGE_R	AWIN_IMAGE_I	AWIN_IMAGE_Z	AWIN_IMAGE_Y	BWIN_IMAGE_G	BWIN_IMAGE_R	BWIN_IMAGE_I	BWIN_IMAGE_Z	BWIN_IMAGE_Y	THETAWIN_IMAGE_G	THETAWIN_IMAGE_R	THETAWIN_IMAGE_I	THETAWIN_IMAGE_Z	THETAWIN_IMAGE_Y	ERRAWIN_IMAGE_G	ERRAWIN_IMAGE_R	ERRAWIN_IMAGE_I	ERRAWIN_IMAGE_Z	ERRAWIN_IMAGE_Y	ERRBWIN_IMAGE_G	ERRBWIN_IMAGE_R	ERRBWIN_IMAGE_I	ERRBWIN_IMAGE_Z	ERRBWIN_IMAGE_Y	ERRTHETAWIN_IMAGE_G	ERRTHETAWIN_IMAGE_R	ERRTHETAWIN_IMAGE_I	ERRTHETAWIN_IMAGE_Z	ERRTHETAWIN_IMAGE_Y	FLUX_RADIUS_G	FLUX_RADIUS_R	FLUX_RADIUS_I	FLUX_RADIUS_Z	FLUX_RADIUS_Y	FWHM_IMAGE_G	FWHM_IMAGE_R	FWHM_IMAGE_I	FWHM_IMAGE_Z	FWHM_IMAGE_Y	ISOAREA_IMAGE_G	ISOAREA_IMAGE_R	ISOAREA_IMAGE_I	ISOAREA_IMAGE_Z	ISOAREA_IMAGE_Y	BACKGROUND_G	BACKGROUND_R	BACKGROUND_I	BACKGROUND_Z	BACKGROUND_Y	NITER_MODEL_G	NITER_MODEL_R	NITER_MODEL_I	NITER_MODEL_Z	NITER_MODEL_Y	KRON_RADIUS_G	KRON_RADIUS_R	KRON_RADIUS_I	KRON_RADIUS_Z	KRON_RADIUS_Y	MAG_AUTO_G	MAG_AUTO_R	MAG_AUTO_I	MAG_AUTO_Z	MAG_AUTO_Y	MAGERR_AUTO_G	MAGERR_AUTO_R	MAGERR_AUTO_I	MAGERR_AUTO_Z	MAGERR_AUTO_Y	WAVG_MAG_PSF_G	WAVG_MAG_PSF_R	WAVG_MAG_PSF_I	WAVG_MAG_PSF_Z	WAVG_MAG_PSF_Y	WAVG_MAGERR_PSF_G	WAVG_MAGERR_PSF_R	WAVG_MAGERR_PSF_I	WAVG_MAGERR_PSF_Z	WAVG_MAGERR_PSF_Y	FLUX_AUTO_G	FLUX_AUTO_R	FLUX_AUTO_I	FLUX_AUTO_Z	FLUX_AUTO_Y	FLUXERR_AUTO_G	FLUXERR_AUTO_R	FLUXERR_AUTO_I	FLUXERR_AUTO_Z	FLUXERR_AUTO_Y	WAVG_FLUX_PSF_G	WAVG_FLUX_PSF_R	WAVG_FLUX_PSF_I	WAVG_FLUX_PSF_Z	WAVG_FLUX_PSF_Y	WAVG_FLUXERR_PSF_G	WAVG_FLUXERR_PSF_R	WAVG_FLUXERR_PSF_I	WAVG_FLUXERR_PSF_Z	WAVG_FLUXERR_PSF_Y	CLASS_STAR_G	CLASS_STAR_R	CLASS_STAR_I	CLASS_STAR_Z	CLASS_STAR_Y	SPREAD_MODEL_G	SPREAD_MODEL_R	SPREAD_MODEL_I	SPREAD_MODEL_Z	SPREAD_MODEL_Y	WAVG_SPREAD_MODEL_G	WAVG_SPREAD_MODEL_R	WAVG_SPREAD_MODEL_I	WAVG_SPREAD_MODEL_Z	WAVG_SPREAD_MODEL_Y	SPREADERR_MODEL_G	SPREADERR_MODEL_R	SPREADERR_MODEL_I	SPREADERR_MODEL_Z	SPREADERR_MODEL_Y	WAVG_SPREADERR_MODEL_G	WAVG_SPREADERR_MODEL_R	WAVG_SPREADERR_MODEL_I	WAVG_SPREADERR_MODEL_Z	WAVG_SPREADERR_MODEL_Y	Norder	Dir	Npix
npartitions=1
Order: 0, Pixel: 4	int64[pyarrow]	string[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	uint8[pyarrow]	uint64[pyarrow]	uint64[pyarrow]

The catalog has been loaded lazily, meaning no data has been read, only the catalog schema

lsdb Catalog gaia_dr3:

	solution_id	designation	source_id	random_index	ref_epoch	ra	ra_error	dec	dec_error	parallax	parallax_error	parallax_over_error	pm	pmra	pmra_error	pmdec	pmdec_error	ra_dec_corr	ra_parallax_corr	ra_pmra_corr	ra_pmdec_corr	dec_parallax_corr	dec_pmra_corr	dec_pmdec_corr	parallax_pmra_corr	parallax_pmdec_corr	pmra_pmdec_corr	astrometric_n_obs_al	astrometric_n_obs_ac	astrometric_n_good_obs_al	astrometric_n_bad_obs_al	astrometric_gof_al	astrometric_chi2_al	astrometric_excess_noise	astrometric_excess_noise_sig	astrometric_params_solved	astrometric_primary_flag	nu_eff_used_in_astrometry	pseudocolour	pseudocolour_error	ra_pseudocolour_corr	dec_pseudocolour_corr	parallax_pseudocolour_corr	pmra_pseudocolour_corr	pmdec_pseudocolour_corr	astrometric_matched_transits	visibility_periods_used	astrometric_sigma5d_max	matched_transits	new_matched_transits	matched_transits_removed	ipd_gof_harmonic_amplitude	ipd_gof_harmonic_phase	ipd_frac_multi_peak	ipd_frac_odd_win	ruwe	scan_direction_strength_k1	scan_direction_strength_k2	scan_direction_strength_k3	scan_direction_strength_k4	scan_direction_mean_k1	scan_direction_mean_k2	scan_direction_mean_k3	scan_direction_mean_k4	duplicated_source	phot_g_n_obs	phot_g_mean_flux	phot_g_mean_flux_error	phot_g_mean_flux_over_error	phot_g_mean_mag	phot_bp_n_obs	phot_bp_mean_flux	phot_bp_mean_flux_error	phot_bp_mean_flux_over_error	phot_bp_mean_mag	phot_rp_n_obs	phot_rp_mean_flux	phot_rp_mean_flux_error	phot_rp_mean_flux_over_error	phot_rp_mean_mag	phot_bp_rp_excess_factor	phot_bp_n_contaminated_transits	phot_bp_n_blended_transits	phot_rp_n_contaminated_transits	phot_rp_n_blended_transits	phot_proc_mode	bp_rp	bp_g	g_rp	radial_velocity	radial_velocity_error	rv_method_used	rv_nb_transits	rv_nb_deblended_transits	rv_visibility_periods_used	rv_expected_sig_to_noise	rv_renormalised_gof	rv_chisq_pvalue	rv_time_duration	rv_amplitude_robust	rv_template_teff	rv_template_logg	rv_template_fe_h	rv_atm_param_origin	vbroad	vbroad_error	vbroad_nb_transits	grvs_mag	grvs_mag_error	grvs_mag_nb_transits	rvs_spec_sig_to_noise	phot_variable_flag	l	b	ecl_lon	ecl_lat	in_qso_candidates	in_galaxy_candidates	non_single_star	has_xp_continuous	has_xp_sampled	has_rvs	has_epoch_photometry	has_epoch_rv	has_mcmc_gspphot	has_mcmc_msc	in_andromeda_survey	classprob_dsc_combmod_quasar	classprob_dsc_combmod_galaxy	classprob_dsc_combmod_star	teff_gspphot	teff_gspphot_lower	teff_gspphot_upper	logg_gspphot	logg_gspphot_lower	logg_gspphot_upper	mh_gspphot	mh_gspphot_lower	mh_gspphot_upper	distance_gspphot	distance_gspphot_lower	distance_gspphot_upper	azero_gspphot	azero_gspphot_lower	azero_gspphot_upper	ag_gspphot	ag_gspphot_lower	ag_gspphot_upper	ebpminrp_gspphot	ebpminrp_gspphot_lower	ebpminrp_gspphot_upper	libname_gspphot	Norder	Dir	Npix
npartitions=1
Order: 0, Pixel: 4	int64[pyarrow]	string[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int8[pyarrow]	bool[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	int8[pyarrow]	int8[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	bool[pyarrow]	int16[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int8[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int8[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	float[pyarrow]	string[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	bool[pyarrow]	bool[pyarrow]	int16[pyarrow]	bool[pyarrow]	bool[pyarrow]	bool[pyarrow]	bool[pyarrow]	bool[pyarrow]	bool[pyarrow]	bool[pyarrow]	bool[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	string[pyarrow]	uint8[pyarrow]	uint64[pyarrow]	uint64[pyarrow]

The catalog has been loaded lazily, meaning no data has been read, only the catalog schema

lsdb Catalog des_dr2_x_gaia_dr3:

	COADD_OBJECT_ID_des	TILENAME_des	HPIX_32_des	HPIX_64_des	HPIX_1024_des	HPIX_4096_des	HPIX_16384_des	RA_des	DEC_des	ALPHAWIN_J2000_des	DELTAWIN_J2000_des	GALACTIC_L_des	GALACTIC_B_des	XWIN_IMAGE_des	YWIN_IMAGE_des	A_IMAGE_des	ERRA_IMAGE_des	B_IMAGE_des	ERRB_IMAGE_des	THETA_J2000_des	ERRTHETA_IMAGE_des	KRON_RADIUS_des	EBV_SFD98_des	MAG_AUTO_G_DERED_des	MAG_AUTO_R_DERED_des	MAG_AUTO_I_DERED_des	MAG_AUTO_Z_DERED_des	MAG_AUTO_Y_DERED_des	WAVG_MAG_PSF_G_DERED_des	WAVG_MAG_PSF_R_DERED_des	WAVG_MAG_PSF_I_DERED_des	WAVG_MAG_PSF_Z_DERED_des	WAVG_MAG_PSF_Y_DERED_des	EXTENDED_CLASS_COADD_des	EXTENDED_CLASS_WAVG_des	FLAGS_G_des	IMAFLAGS_ISO_G_des	NEPOCHS_G_des	FLAGS_R_des	IMAFLAGS_ISO_R_des	NEPOCHS_R_des	FLAGS_I_des	IMAFLAGS_ISO_I_des	NEPOCHS_I_des	FLAGS_Z_des	IMAFLAGS_ISO_Z_des	NEPOCHS_Z_des	FLAGS_Y_des	IMAFLAGS_ISO_Y_des	NEPOCHS_Y_des	XWIN_IMAGE_G_des	XWIN_IMAGE_R_des	XWIN_IMAGE_I_des	XWIN_IMAGE_Z_des	XWIN_IMAGE_Y_des	YWIN_IMAGE_G_des	YWIN_IMAGE_R_des	YWIN_IMAGE_I_des	YWIN_IMAGE_Z_des	YWIN_IMAGE_Y_des	X2WIN_IMAGE_G_des	X2WIN_IMAGE_R_des	X2WIN_IMAGE_I_des	X2WIN_IMAGE_Z_des	X2WIN_IMAGE_Y_des	Y2WIN_IMAGE_G_des	Y2WIN_IMAGE_R_des	Y2WIN_IMAGE_I_des	Y2WIN_IMAGE_Z_des	Y2WIN_IMAGE_Y_des	XYWIN_IMAGE_G_des	XYWIN_IMAGE_R_des	XYWIN_IMAGE_I_des	XYWIN_IMAGE_Z_des	XYWIN_IMAGE_Y_des	ERRX2WIN_IMAGE_G_des	ERRX2WIN_IMAGE_R_des	ERRX2WIN_IMAGE_I_des	ERRX2WIN_IMAGE_Z_des	ERRX2WIN_IMAGE_Y_des	ERRY2WIN_IMAGE_G_des	ERRY2WIN_IMAGE_R_des	ERRY2WIN_IMAGE_I_des	ERRY2WIN_IMAGE_Z_des	ERRY2WIN_IMAGE_Y_des	ERRXYWIN_IMAGE_G_des	ERRXYWIN_IMAGE_R_des	ERRXYWIN_IMAGE_I_des	ERRXYWIN_IMAGE_Z_des	ERRXYWIN_IMAGE_Y_des	AWIN_IMAGE_G_des	AWIN_IMAGE_R_des	AWIN_IMAGE_I_des	AWIN_IMAGE_Z_des	AWIN_IMAGE_Y_des	BWIN_IMAGE_G_des	BWIN_IMAGE_R_des	BWIN_IMAGE_I_des	BWIN_IMAGE_Z_des	BWIN_IMAGE_Y_des	THETAWIN_IMAGE_G_des	THETAWIN_IMAGE_R_des	THETAWIN_IMAGE_I_des	THETAWIN_IMAGE_Z_des	THETAWIN_IMAGE_Y_des	ERRAWIN_IMAGE_G_des	ERRAWIN_IMAGE_R_des	ERRAWIN_IMAGE_I_des	ERRAWIN_IMAGE_Z_des	ERRAWIN_IMAGE_Y_des	ERRBWIN_IMAGE_G_des	ERRBWIN_IMAGE_R_des	ERRBWIN_IMAGE_I_des	ERRBWIN_IMAGE_Z_des	ERRBWIN_IMAGE_Y_des	ERRTHETAWIN_IMAGE_G_des	ERRTHETAWIN_IMAGE_R_des	ERRTHETAWIN_IMAGE_I_des	ERRTHETAWIN_IMAGE_Z_des	ERRTHETAWIN_IMAGE_Y_des	FLUX_RADIUS_G_des	FLUX_RADIUS_R_des	FLUX_RADIUS_I_des	FLUX_RADIUS_Z_des	FLUX_RADIUS_Y_des	FWHM_IMAGE_G_des	FWHM_IMAGE_R_des	FWHM_IMAGE_I_des	FWHM_IMAGE_Z_des	FWHM_IMAGE_Y_des	ISOAREA_IMAGE_G_des	ISOAREA_IMAGE_R_des	ISOAREA_IMAGE_I_des	ISOAREA_IMAGE_Z_des	ISOAREA_IMAGE_Y_des	BACKGROUND_G_des	BACKGROUND_R_des	BACKGROUND_I_des	BACKGROUND_Z_des	BACKGROUND_Y_des	NITER_MODEL_G_des	NITER_MODEL_R_des	NITER_MODEL_I_des	NITER_MODEL_Z_des	NITER_MODEL_Y_des	KRON_RADIUS_G_des	KRON_RADIUS_R_des	KRON_RADIUS_I_des	KRON_RADIUS_Z_des	KRON_RADIUS_Y_des	MAG_AUTO_G_des	MAG_AUTO_R_des	MAG_AUTO_I_des	MAG_AUTO_Z_des	MAG_AUTO_Y_des	MAGERR_AUTO_G_des	MAGERR_AUTO_R_des	MAGERR_AUTO_I_des	MAGERR_AUTO_Z_des	MAGERR_AUTO_Y_des	WAVG_MAG_PSF_G_des	WAVG_MAG_PSF_R_des	WAVG_MAG_PSF_I_des	WAVG_MAG_PSF_Z_des	WAVG_MAG_PSF_Y_des	WAVG_MAGERR_PSF_G_des	WAVG_MAGERR_PSF_R_des	WAVG_MAGERR_PSF_I_des	WAVG_MAGERR_PSF_Z_des	WAVG_MAGERR_PSF_Y_des	FLUX_AUTO_G_des	FLUX_AUTO_R_des	FLUX_AUTO_I_des	FLUX_AUTO_Z_des	FLUX_AUTO_Y_des	FLUXERR_AUTO_G_des	FLUXERR_AUTO_R_des	FLUXERR_AUTO_I_des	FLUXERR_AUTO_Z_des	FLUXERR_AUTO_Y_des	WAVG_FLUX_PSF_G_des	WAVG_FLUX_PSF_R_des	WAVG_FLUX_PSF_I_des	WAVG_FLUX_PSF_Z_des	WAVG_FLUX_PSF_Y_des	WAVG_FLUXERR_PSF_G_des	WAVG_FLUXERR_PSF_R_des	WAVG_FLUXERR_PSF_I_des	WAVG_FLUXERR_PSF_Z_des	WAVG_FLUXERR_PSF_Y_des	CLASS_STAR_G_des	CLASS_STAR_R_des	CLASS_STAR_I_des	CLASS_STAR_Z_des	CLASS_STAR_Y_des	SPREAD_MODEL_G_des	SPREAD_MODEL_R_des	SPREAD_MODEL_I_des	SPREAD_MODEL_Z_des	SPREAD_MODEL_Y_des	WAVG_SPREAD_MODEL_G_des	WAVG_SPREAD_MODEL_R_des	WAVG_SPREAD_MODEL_I_des	WAVG_SPREAD_MODEL_Z_des	WAVG_SPREAD_MODEL_Y_des	SPREADERR_MODEL_G_des	SPREADERR_MODEL_R_des	SPREADERR_MODEL_I_des	SPREADERR_MODEL_Z_des	SPREADERR_MODEL_Y_des	WAVG_SPREADERR_MODEL_G_des	WAVG_SPREADERR_MODEL_R_des	WAVG_SPREADERR_MODEL_I_des	WAVG_SPREADERR_MODEL_Z_des	WAVG_SPREADERR_MODEL_Y_des	Norder_des	Dir_des	Npix_des	solution_id_gaia	designation_gaia	source_id_gaia	random_index_gaia	ref_epoch_gaia	ra_gaia	ra_error_gaia	dec_gaia	dec_error_gaia	parallax_gaia	parallax_error_gaia	parallax_over_error_gaia	pm_gaia	pmra_gaia	pmra_error_gaia	pmdec_gaia	pmdec_error_gaia	ra_dec_corr_gaia	ra_parallax_corr_gaia	ra_pmra_corr_gaia	ra_pmdec_corr_gaia	dec_parallax_corr_gaia	dec_pmra_corr_gaia	dec_pmdec_corr_gaia	parallax_pmra_corr_gaia	parallax_pmdec_corr_gaia	pmra_pmdec_corr_gaia	astrometric_n_obs_al_gaia	astrometric_n_obs_ac_gaia	astrometric_n_good_obs_al_gaia	astrometric_n_bad_obs_al_gaia	astrometric_gof_al_gaia	astrometric_chi2_al_gaia	astrometric_excess_noise_gaia	astrometric_excess_noise_sig_gaia	astrometric_params_solved_gaia	astrometric_primary_flag_gaia	nu_eff_used_in_astrometry_gaia	pseudocolour_gaia	pseudocolour_error_gaia	ra_pseudocolour_corr_gaia	dec_pseudocolour_corr_gaia	parallax_pseudocolour_corr_gaia	pmra_pseudocolour_corr_gaia	pmdec_pseudocolour_corr_gaia	astrometric_matched_transits_gaia	visibility_periods_used_gaia	astrometric_sigma5d_max_gaia	matched_transits_gaia	new_matched_transits_gaia	matched_transits_removed_gaia	ipd_gof_harmonic_amplitude_gaia	ipd_gof_harmonic_phase_gaia	ipd_frac_multi_peak_gaia	ipd_frac_odd_win_gaia	ruwe_gaia	scan_direction_strength_k1_gaia	scan_direction_strength_k2_gaia	scan_direction_strength_k3_gaia	scan_direction_strength_k4_gaia	scan_direction_mean_k1_gaia	scan_direction_mean_k2_gaia	scan_direction_mean_k3_gaia	scan_direction_mean_k4_gaia	duplicated_source_gaia	phot_g_n_obs_gaia	phot_g_mean_flux_gaia	phot_g_mean_flux_error_gaia	phot_g_mean_flux_over_error_gaia	phot_g_mean_mag_gaia	phot_bp_n_obs_gaia	phot_bp_mean_flux_gaia	phot_bp_mean_flux_error_gaia	phot_bp_mean_flux_over_error_gaia	phot_bp_mean_mag_gaia	phot_rp_n_obs_gaia	phot_rp_mean_flux_gaia	phot_rp_mean_flux_error_gaia	phot_rp_mean_flux_over_error_gaia	phot_rp_mean_mag_gaia	phot_bp_rp_excess_factor_gaia	phot_bp_n_contaminated_transits_gaia	phot_bp_n_blended_transits_gaia	phot_rp_n_contaminated_transits_gaia	phot_rp_n_blended_transits_gaia	phot_proc_mode_gaia	bp_rp_gaia	bp_g_gaia	g_rp_gaia	radial_velocity_gaia	radial_velocity_error_gaia	rv_method_used_gaia	rv_nb_transits_gaia	rv_nb_deblended_transits_gaia	rv_visibility_periods_used_gaia	rv_expected_sig_to_noise_gaia	rv_renormalised_gof_gaia	rv_chisq_pvalue_gaia	rv_time_duration_gaia	rv_amplitude_robust_gaia	rv_template_teff_gaia	rv_template_logg_gaia	rv_template_fe_h_gaia	rv_atm_param_origin_gaia	vbroad_gaia	vbroad_error_gaia	vbroad_nb_transits_gaia	grvs_mag_gaia	grvs_mag_error_gaia	grvs_mag_nb_transits_gaia	rvs_spec_sig_to_noise_gaia	phot_variable_flag_gaia	l_gaia	b_gaia	ecl_lon_gaia	ecl_lat_gaia	in_qso_candidates_gaia	in_galaxy_candidates_gaia	non_single_star_gaia	has_xp_continuous_gaia	has_xp_sampled_gaia	has_rvs_gaia	has_epoch_photometry_gaia	has_epoch_rv_gaia	has_mcmc_gspphot_gaia	has_mcmc_msc_gaia	in_andromeda_survey_gaia	classprob_dsc_combmod_quasar_gaia	classprob_dsc_combmod_galaxy_gaia	classprob_dsc_combmod_star_gaia	teff_gspphot_gaia	teff_gspphot_lower_gaia	teff_gspphot_upper_gaia	logg_gspphot_gaia	logg_gspphot_lower_gaia	logg_gspphot_upper_gaia	mh_gspphot_gaia	mh_gspphot_lower_gaia	mh_gspphot_upper_gaia	distance_gspphot_gaia	distance_gspphot_lower_gaia	distance_gspphot_upper_gaia	azero_gspphot_gaia	azero_gspphot_lower_gaia	azero_gspphot_upper_gaia	ag_gspphot_gaia	ag_gspphot_lower_gaia	ag_gspphot_upper_gaia	ebpminrp_gspphot_gaia	ebpminrp_gspphot_lower_gaia	ebpminrp_gspphot_upper_gaia	libname_gspphot_gaia	Norder_gaia	Dir_gaia	Npix_gaia	_dist_arcsec
npartitions=1
Order: 0, Pixel: 4	int64[pyarrow]	string[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	int16[pyarrow]	int32[pyarrow]	int32[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	int32[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	uint8[pyarrow]	uint64[pyarrow]	uint64[pyarrow]	int64[pyarrow]	string[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int8[pyarrow]	bool[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	int8[pyarrow]	int8[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	bool[pyarrow]	int16[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	int8[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int8[pyarrow]	int16[pyarrow]	int16[pyarrow]	int16[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarr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The catalog has been loaded lazily, meaning no data has been read, only the catalog schema

# Run the pipeline with Dask client, it will take a while
xmatched.to_hats(XMATCH_DIR)

# Look into the data
lsdb.read_hats(XMATCH_DIR).head()

	COADD_OBJECT_ID_des	TILENAME_des	HPIX_32_des	HPIX_64_des	HPIX_1024_des	HPIX_4096_des	HPIX_16384_des	RA_des	DEC_des	ALPHAWIN_J2000_des	...	ag_gspphot_lower_gaia	ag_gspphot_upper_gaia	ebpminrp_gspphot_gaia	ebpminrp_gspphot_lower_gaia	ebpminrp_gspphot_upper_gaia	libname_gspphot_gaia	Norder_gaia	Dir_gaia	Npix_gaia	_dist_arcsec
_healpix_29
1369333388684088685	1033365960	DES0000+0209	4864	19459	4981605	79705693	1275291097	0.315738	1.807978	0.315738	...	0.1502	0.1595	0.0839	0.0812	0.0863	MARCS	0	0	4	0.217639
1369333392965728211	1033367823	DES0000+0209	4864	19459	4981605	79705693	1275291101	0.321042	1.812476	0.321042	...	0.1019	0.1442	0.0686	0.0556	0.0787	A	0	0	4	0.003322
1369333418327952083	1033367955	DES0000+0209	4864	19459	4981605	79705695	1275291124	0.310521	1.816329	0.310521	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0	0	4	0.007574
1369333563259788683	1033369594	DES0000+0209	4864	19459	4981606	79705703	1275291259	0.234549	1.80234	0.234549	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0	0	4	0.010084
1369333662580669435	1033369151	DES0000+0209	4864	19459	4981606	79705709	1275291352	0.224895	1.805816	0.224895	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0	0	4	0.005852

5 rows × 374 columns

Let’s zoom into a small area of the sky and plot the catalogs

We will select small “boxes” in the sky to load only small parts of the catalogs and plot them.

Here we do not load the saved cross-matched catalog, but virtually cross-match the catalogs on the fly each time we run .compute(). While it would be more performant to load the cross-matched catalog, the following cell demonstrates that you can work with the cross-matching results without saving them to disk.

ra_range = [0.0, 0.1]
dec_range = [2.45, 2.55]

des_box = des_catalog.box_search(ra=ra_range, dec=dec_range).compute()
gaia_box = gaia_catalog.box_search(ra=ra_range, dec=dec_range).compute()
xmatch_box = xmatched.box_search(ra=ra_range, dec=dec_range).compute()

ra_des = np.where(des_box["RA"] > 180, des_box["RA"] - 360, des_box["RA"])
ra_gaia = np.where(gaia_box["ra"] > 180, gaia_box["ra"] - 360, gaia_box["ra"])
ra_x_gaia = np.where(xmatch_box["ra_gaia"] > 180, xmatch_box["ra_gaia"] - 360, xmatch_box["ra_gaia"])

plt.figure(figsize=(5, 5))
plt.scatter(ra_gaia, gaia_box["dec"], s=50, alpha=1.0, color="green", label="Gaia")
plt.scatter(ra_x_gaia, xmatch_box["dec_gaia"], s=10, alpha=1.0, color="red", label="x-matched")
plt.scatter(ra_des, des_box["DEC"], s=10, alpha=0.4, marker="+", color="blue", label="DES")
plt.xlabel("RA")
plt.ylabel("Dec")
plt.legend()

<matplotlib.legend.Legend at 0x711826264620>

Plot the HR diagram

Now we may plot the color-magnitude diagram for all the DES stars using Gaia parallaxes. Instead of redoing the cross-match, we will load the saved cross-matched catalog with few columns only.

# Read the cross-matched catalog, just like we did before for Gaia and DES,
# but keeping few columns only.
xmatched_from_disk = lsdb.read_hats(
    XMATCH_DIR,
    columns=[
        "parallax_gaia",
        "parallax_over_error_gaia",
        "WAVG_MAG_PSF_G_des",
        "WAVG_MAG_PSF_R_des",
    ],
)

# Apply quality flags
filtered = xmatched.query(
    "parallax_over_error_gaia > 10.0 and WAVG_MAG_PSF_G_des > 0.0 and WAVG_MAG_PSF_R_des > 0.0"
)

# Get Dask data frame from the catalog object
ddf = filtered._ddf

# get 2D histogram of WAVG_MAG_PSF_R-WAVG_MAG_PSF_I vs WAVG_MAG_PSF_I, lazily
color = ddf["WAVG_MAG_PSF_G_des"] - ddf["WAVG_MAG_PSF_R_des"]
absolute_r = ddf["WAVG_MAG_PSF_R_des"] + 5 * np.log10(ddf["parallax_gaia"] / 100)

x_bins = np.linspace(-1, 2.5, 101)
y_bins = np.linspace(0, 20, 101)

hist2d = dask.array.histogram2d(color.to_dask_array(), absolute_r.to_dask_array(), bins=[x_bins, y_bins])

# Run the computation with Dask client, it will take a while
hist2d = hist2d[0].compute()

# Plot the 2D histogram
plt.imshow(
    hist2d.T,
    extent=(x_bins[0], x_bins[-1], y_bins[0], y_bins[-1]),
    aspect="auto",
    origin="lower",
    norm=LogNorm(vmin=1, vmax=hist2d.max()),
)
plt.gca().invert_yaxis()
plt.colorbar(label="Number of stars")
plt.xlabel("$g-r$")
plt.ylabel("$M_r$")
plt.title("Absolute magnitude — color diagram")

/home/delucchi/.virtualenvs/demo/lib/python3.12/site-packages/dask/dataframe/core.py:3769: UserWarning: Dask currently has limited support for converting pandas extension dtypes to arrays. Converting double[pyarrow] to object dtype.
  warnings.warn(
/home/delucchi/.virtualenvs/demo/lib/python3.12/site-packages/dask/dataframe/core.py:3769: UserWarning: Dask currently has limited support for converting pandas extension dtypes to arrays. Converting double[pyarrow] to object dtype.
  warnings.warn(

Text(0.5, 1.0, 'Absolute magnitude — color diagram')

And this is how it would look like for the entire Gaia + DES cross-matched catalog. This shows ~3.6 million DES stars with Gaia parallaxes left after filtering.