Advanced usage of MOCs to explore complex regions of interest#

Stefania Amodea¹, Matthieu Baumann¹, Thomas Boch¹, Caroline Bot¹, Katarina A. Lutz¹.

  1. Université de Strasbourg, CNRS, Observatoire Astronomique de Strasbourg, UMR 7550, F-67000, Strasbourg, France

Thomas Boch and Caroline Bot wrote the original version of this tutorial, available on the EURO-VO tutorials page. It was presented at the workshop “Detecting the unexpected, Discovery in the Era of Astronomically Big Data”. The version here is an adaptation to jupyter notebooks by the Strasbourg astronomical Data Center (CDS) team.


Introduction#

I have a set of images. I would like to select regions in my observations that are above a given threshold in another survey (e.g. at low extinction), retrieve objects from very large catalogs (e.g. Gaia + 2MASS) in these non-trivial shapes and not-necessarily-connected regions, and combine them to visualize some quantities (e.g. color color diagram).

# Standard Library
from pathlib import Path

# Astronomy tools
import astropy.units as u
from astropy.coordinates import Angle, SkyCoord, match_coordinates_sky
from astropy.io import fits

# Access astronomical databases
import pyvo
from astroquery.vizier import Vizier

# Moc and HEALPix tools
import cdshealpix
import mocpy

# For plots
import matplotlib.pyplot as plt

# Data handling
import numpy as np

Step 1: Finding the images#

We want to find all Short-Red images in the Macquarie/AAO/Strasbourg Hα Planetary Galactic catalog (MASH) using the VizieR associated data service.

Vizier

The VizieR service at CDS inventories astronomical catalogs published in the literature. Some of these catalogs contain data associated with publications and the tables therein. This data can be browsed and explored through the VizieR-associated data service, linked to the traditional VizieR table service. Here we look for images associated with the MASH catalog of planetary nebulae (Parker et. al. 2006-2008). The MASH fits files are cut-outs extracted from a larger Hα and Short Red survey to constitute a set of regions of interest around planetary nebulae.

To find VizieR-associated data, we use the Table Access Protocol (TAP) with the VizieR endpoint. Through the VizieR TAP endpoint, we can search for tables, their content, and information on associated data.

First, we search for the MASH catalog:

# give the address of the service, you can also directly visit the website
tap_vizier = pyvo.dal.TAPService("https://tapvizier.cds.unistra.fr/TAPVizieR/tap")

# a query that searches for all tables with the words MASH and Parker in their description
query = """
        SELECT  *  FROM tables 
        WHERE description LIKE '%MASH%Parker%'
        """

mash_catalogues = tap_vizier.search(query).to_table()
mash_catalogues
Table length=5
schema_nametable_nametable_typedescriptionutypenrows
objectobjectobjectobjectobjectobject
J_MNRASJ/MNRAS/412/223/table4tableThe nine MASH PNe detected and possibly detected in the PMN survey ( Bojicic I.S., Parker Q.A., Filipovic M.D., Frew D.J.)9
J_MNRASJ/MNRAS/412/223/table1tableMASH PNe detected in the NVSS ( Bojicic I.S., Parker Q.A., Filipovic M.D., Frew D.J.)201
V_combinedV/127A/mash2tableThe MASH-II Supplement (from paper II) ( Parker Q.A., Acker A., Frew D.J., Hartley M., Peyaud A.E.J., Phillipps S., Russeil D., Beaulieu S.F., Cohen M., Koppen J., Marcout J., Miszalski B., Morgan D.H., Morris R.A.H., Ochsenbein F., Pierce M.J.,)335
V_combinedV/127A/mash1tableThe MASH Catalog of Planetary Nebulae (paper I) ( Parker Q.A., Acker A., Frew D.J., Hartley M., Peyaud A.E.J., Phillipps S., Russeil D., Beaulieu S.F., Cohen M., Koppen J., Marcout J., Miszalski B., Morgan D.H., Morris R.A.H., Ochsenbein F., Pierce M.J.,)903
J_MNRASJ/MNRAS/412/223/mpgs2tableMASH PNe detected in the MPGS-2 (Cat. VIII/82) ( Bojicic I.S., Parker Q.A., Filipovic M.D., Frew D.J.)81

In this tutorial, we are interested in the tables belonging to the catalogs V/127A. This includes tables V/127A/mash1 and V/127A/mash2. To have a look at the content of these tables, we look at their first lines like this:

query = """
        SELECT TOP 5 * FROM \"V/127A/mash1\" 
        """
mash1_head = tap_vizier.search(query).to_table()
mash1_head
Table length=5
recnon_PNGPNGNameRAJ2000DEJ2000GLonGLatMajDiamMinDiamCSMorphTelObsDateHaExpHaFldCommentsimgImageHaImageSrAssocData
degdegdegdegarcsecarcsec'Y:M:D'
int32str1objectobjectfloat64float64float64float64float64float64objectobjectobjectfloat64objectobjectobjectobjectobjectobjectobject
29LG234.7-02.2PHR0724-2021111.05458333333331-20.36361111111111234.7045-2.2774134.554.0ASA2452672.0HA18201HA842Large, very faint diffuse arcuate nebula; has [NII]~2xH-alpha, nothing in blue1029img_haimg_srfits
2PG227.3-12.0PHR0633-180898.35374999999999-18.13972222222222227.3207-12.028917.015.0EaSA2452672.0HA18191HA926Very faint, partial arcuate nebula also observed M1 060100; [NII]~0.8xH-alpha, strong [SII], only weak H-beta in blue - inconclusive1002img_haimg_srfits
16TG227.2-03.4PHR0705-1419106.41041666666665-14.318055555555553227.2852-3.402915.015.0ESA2452668.0HA18244HA1017Small, circular PN around a faint central star; also observed M1 040100; [NII]~0.7 H-alpha, [OIII]>>H-beta1016img_haimg_srfits
5LG223.6-06.8PHR0646-1235101.60583333333332-12.598888888888887223.6338-6.803540.037.0ESA2453788.0HA18194HA1016Slightly oval very faint PN candidate - has [OIII] and H-alpha1005img_haimg_srfits
10PG224.3-05.5PHR0652-1240103.08458333333331-12.67611111111111224.3504-5.5463187.0180.0ISA2452670.0HA18244HA1017Faint, extended S-shaped emission nebula, possible evolved PN, also observed M1 080100; has [NII]~0.8Ha, [OIII], strong [SII], [OIII]>H-beta1010img_haimg_srfits

As you can see, the last column of this table is called AssocData and contains the entry fits. If you look at this table on the VizieR web interface, you can download the associated fits file. Within this notebook, we query the obscore database to get the URLs to the fits file. Using the astropy.io.fits module, we can open the fits files from their URLs.

obs_tap_vizier = pyvo.dal.TAPService(
    "https://cdsarc.cds.unistra.fr/saadavizier.tap/tap",
)
query = """
        SELECT TOP 5  *  FROM obscore 
        WHERE obs_collection='V/127A'   
        """
mash_fits = obs_tap_vizier.search(query).to_table()
mash_fits
Table length=5
access_estsizeaccess_formataccess_urlbib_referencecalib_leveldataproduct_typeem_bandem_maxem_minextensionfacility_namehas_wcsinstrument_nameobs_collectionobs_idobs_publisher_didoidsaadao_ucdpol_statess_decs_fovs_ras_regions_resolutiontarget_namet_exptimet_maxt_mint_resolution
kbytespectmeta.codemeta.codedegdegdegarcsec
int64objectobjectobjectint32objectobjectfloat64float64int32objectint32objectobjectobjectobjectobjectobjectobjectfloat64float64float64objectfloat64objectfloat64float64float64float64
610560application/fitshttps://cdsarc.cds.unistra.fr/saadavizier/download?oid=8649755497794109452006MNRAS.373...79P-1imageOptical1e-063e-07--UKST5SuperCOSMOS IV/127A1032_sr.fitsivo://CDS.VizieR/V/127A?res=1032_sr.fits864975549779410945obs.imageNotSet-13.7843898548012030.0713021682865721111.43275777455307Polygon ICRS 111.46664940645094 -13.81781914701963 111.39832407190251 -13.817324215795937 111.3988758446347 -13.750955924906409 111.46718176614709 -13.7514507063586090.948991575097523----51226.0--
740160application/fitshttps://cdsarc.cds.unistra.fr/saadavizier/download?oid=8649755497794109512006MNRAS.373...79P-1imageOptical1e-063e-07--UKST5SuperCOSMOS IV/127A1034_ha.fitsivo://CDS.VizieR/V/127A?res=1034_ha.fits864975549779410951obs.imageNotSet-17.120492976226730.07123944496920925111.78451610217952Polygon ICRS 111.8191479048229 -17.153711083791936 111.74974303867411 -17.15359964722061 111.74989666803143 -17.087268981666405 111.8192767884711 -17.0873803700202520.9485038734315203----52338.0--
596160application/fitshttps://cdsarc.cds.unistra.fr/saadavizier/download?oid=8649755497794109552006MNRAS.373...79P-1imageOptical1e-063e-07--UKST5SuperCOSMOS IV/127A1034_sr.fitsivo://CDS.VizieR/V/127A?res=1034_sr.fits864975549779410955obs.imageNotSet-17.1205339692782450.07124314880377468111.7845314034197Polygon ICRS 111.8191637002453 -17.153753988164176 111.74975722819107 -17.153640656320004 111.7499114759699 -17.08730806317958 111.81929320101966 -17.0874213466143270.9485285228101485----50871.0--
731520application/fitshttps://cdsarc.cds.unistra.fr/saadavizier/download?oid=8649755497794109592006MNRAS.373...79P-1imageOptical1e-063e-07--UKST5SuperCOSMOS IV/127A1036_ha.fitsivo://CDS.VizieR/V/127A?res=1036_ha.fits864975549779410959obs.imageNotSet-21.8620994279805560.07130134708963763112.69528726569132Polygon ICRS 112.73146814532774 -21.89490077932626 112.65997554014551 -21.895645624610193 112.659123005937 -21.829290184523508 112.73058240266795 -21.8285457137899180.9488088411876365----51163.0--
590400application/fitshttps://cdsarc.cds.unistra.fr/saadavizier/download?oid=8649755497794109602006MNRAS.373...79P-1imageOptical1e-063e-07--UKST5SuperCOSMOS IV/127A1005_sr.fitsivo://CDS.VizieR/V/127A?res=1005_sr.fits864975549779410960obs.imageNotSet-12.598781947184450.06695119600443825101.605805625331Polygon ICRS 101.64022494465036 -12.631531553985315 101.57226287333897 -12.63235954506209 101.57139509987323 -12.56602794013158 101.63933959085792 -12.5652001703247580.9485060738987898----51156.0--

As you can see, the result from this query provides information on the fits files associated with the MASH catalogs. In particular, the column access_url provides the location of the data. To get the first image we can do:

# download the first image
hdul_list = fits.open(mash_fits["access_url"][0])
# plot it in a quick preview
plt.imshow(hdul_list[0].data, cmap="gray", origin="lower")
<matplotlib.image.AxesImage at 0x7f75684b6490>
_images/ebd7ea76fc7a9a00b1388be6ba5c8a26701a5152cb96cf4e5a768f7705f5b913.png

This should be done for every image in the list. However, downloading all the data takes quite some time. For this tutorial, we prepared a subsample of 335 of these Short Red images that will run promptly but we encourage you to try accessing the full Short Red sample on your own later. The subsample is available in the Data Folder of this repository.

Step 2: Create a MOC of the MASH images#

The multi-order coverage (MOC) map of a set of images represents their sky coverage. MOCs can describe arbitrary zones in the sky which do not need to be connected. You’ll see that the union or intersection of two MOCs requires few time and computational effort. Catalogs can also be filtered by MOCs.

Here we want to use the fits files just downloaded to create a MOC map corresponding to the coverage of the MASH images.

Organising data#

# Where to find fits images downloaded from the archive above
datadir = Path("Data/MASH_Sample/")
datadir.mkdir(parents=True, exist_ok=True)

In most cases, we could ignore the next cell. However, some possible deprecated keywords in the fits header would hamper the MOC creation and would cause errors in the underlying astropy.wcs.WCS module. This is why we rewrite the headers of the fits files so that they only contain the useful keywords needed to define the coordinate frame correctly before using mocpy.

Create the MOC#

Here we can create the MOC of the MASH images with the MOCPy module. This can take a few seconds.

# Get a list of the fits files and create the MOC
mash_file_list = datadir.glob(
    "*_sr_modified.fits",
)  # glob allows to find all paths that end with _sr_modified.fits in datadir
moc_mash = mocpy.MOC.from_fits_images(mash_file_list, max_norder=15)
# this takes a bit of time because a lot of files are opened and processed to extract the moc
WARNING: FITSFixedWarning: RADECSYS= 'FK5 ' 
the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]

Plot the MOC#

We can chose between two of the MOCpy methods to plot the MOC

# A one-liner for a very fast visualization
moc_mash.display_preview()
_images/e3e5f1eef95431b28595ca1656ade2ce0b8be23045f680a77e7c50b055606d88.png
# With a bit more control over the output, the MOC.wcs method
fig = plt.figure(figsize=(10, 7))
wcs = moc_mash.wcs(fig)
ax = fig.add_subplot(projection=wcs)
ax.grid(True)  # noqa: FBT003
moc_mash.fill(ax, wcs, color="purple", alpha=0.5)
_images/1b70796068a9a0bbeb8aa9ae6381115bc45f66b2929544f06831f57faaea2a28.png

You can see how the MOC has arbitrary shapes and not all regions are connected.

And for more control over the plot parameters, there is also the mocpy.WCS method (!)

Step 3: Load an archival extinction map and create the MOC of the low extinction regions#

Different works (e.g. Schlegel et al. 1998, Schlafly & Finkbeiner 2011, Green et al. 2015…) have created extinction maps of the sky that are publicly available. Some of these maps are all-sky maps, while others have higher resolutions, or come from different methods… They can be found in HEALPix format (among others) on the Legacy Archive for Microwave Background Data Analysis (LAMBDA) website or on the Analysis Center for Extended Data (CADE) website.

For this tutorial, we will download the well-known all-sky extinction map from Schlegel et al. from the LAMBDA website and define the low extinction area for which \(0 < E(B-V) < 0.5\) as a MOC. It has an information page.

The map is available here: https://lambda.gsfc.nasa.gov/data/foregrounds/SFD/lambda_sfd_ebv.fits and we save it to disc.

hdul = fits.open(
    "https://lambda.gsfc.nasa.gov/data/foregrounds/SFD/lambda_sfd_ebv.fits",
)

We are only interested in regions with low extinction. So we aim to get a MOC of all regions where the extinction values from the Schlegel et al. map are between 0 and 0.5mag. The extinction map we got from the NASA webpage is in the HEALPix format. This is an efficient presentation of all-sky maps. The HEALPix tesselation is also used by MOCs. So to get the MOC from the extinction map, we do the following.

First, we check the coordinate system in the map header. We will need to convert to equatorial coordinates, change the projection of the map, and set the order (i.e. resolution) of the map.

hdr = hdul[0].header
hdr
SIMPLE  =                    T / file does conform to FITS standard             
BITPIX  =                   32 / number of bits per data pixel                  
NAXIS   =                    0 / number of data axes                            
EXTEND  =                    T / FITS dataset may contain extensions            
COMMENT   FITS (Flexible Image Transport System) format is defined in 'Astronomy
COMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H 
DATE    = '2003-02-05T00:00:00' /file creation date (YYYY-MM-DDThh:mm:ss UT)    
OBJECT  = 'ALL-SKY '           / Portion of sky given                           
COMMENT   This file contains an all-sky Galactic reddening map, E(B-V), based on
COMMENT   the derived reddening maps of Schlegel, Finkbeiner and Davis (1998).  
COMMENT   Software and data files downloaded from their website were used to    
COMMENT   interpolate their high resolution dust maps onto pixel centers        
COMMENT   appropriate for a HEALPix Nside=512 projection in Galactic            
COMMENT   coordinates. This file is distributed and maintained by LAMBDA.       
REFERENC= 'Legacy Archive for Microwave Background Data Analysis (LAMBDA)      '
REFERENC= '                http://lambda.gsfc.nasa.gov/                        '
REFERENC= 'Maps of Dust Infrared Emission for Use in Estimation of Reddening an'
REFERENC= ' Cosmic Microwave Background Radiation Foregrounds',                '
REFERENC= ' Schlegel, Finkbeiner & Davis 1998 ApJ 500, 525                     '
REFERENC= 'Berkeley mirror site for SFD98 data: http://astron.berkeley.edu/dust'
REFERENC= 'Princeton mirror site for SFD98 data:                               '
REFERENC= '                           http://astro/princeton.edu/~schlegel/dust'
REFERENC= 'HEALPix Home Page: http://www.eso.org/science/healpix/              '
RESOLUTN=                    9 / Resolution index                               
SKYCOORD= 'Galactic'           / Coordinate system                              
PIXTYPE = 'HEALPIX '           / Pixel algorithm                                
ORDERING= 'NESTED  '           / Ordering scheme                                
NSIDE   =                  512 / Resolution parameter                           
NPIX    =              3145728 / # of pixels                                    
FIRSTPIX=                    0 / First pixel (0 based)                          
LASTPIX =              3145727 / Last pixel (0 based)                           
print((hdul[1].data).shape)
hdul[1].data
(3145728,)
FITS_rec([( 9.625492, 1.), (46.090515, 1.), ( 8.18071 , 1.), ...,
          (15.149189, 1.), (14.107367, 1.), (15.463686, 1.)],
         dtype=(numpy.record, [('TEMPERATURE', '>f4'), ('N_OBS', '>f4')]))

The data field here has a specific shape. It contains tuples for which the first value is the extinction (named ‘TEMPERATURE’) and the second one is the number of observations of the value (you can check that it is 1 everywhere).

extinction_values = hdul[1].data["TEMPERATURE"]

Let’s extract the information about the number of sides and the order of the healpix map from the header of the fits file

nside = hdul[0].header["NSIDE"]
norder = hdul[0].header["RESOLUTN"]

The header allows to see that this map is in galactic coordinates. We will need to convert this into equatorial coordinates to compare with our other maps.

# Creation of an HEALpix grid at order 9 in nested ordering
healpix_index = np.arange(12 * 4**norder, dtype=np.uint64)
print(
    f"We can check the the NPIX value corresponds to the one in the header here: {len(healpix_index)}",
)
We can check the the NPIX value corresponds to the one in the header here: 3145728
# Get the coordinates of the centers of these healpix cells
center_coordinates_in_equatorial = cdshealpix.healpix_to_skycoord(
    healpix_index,
    depth=9,
)
center_coordinates_in_equatorial
<SkyCoord (ICRS): (ra, dec) in deg
    [( 45.        ,  0.0746039 ), ( 45.08789062,  0.14920793),
     ( 44.91210938,  0.14920793), ..., (315.08789062, -0.14920793),
     (314.91210938, -0.14920793), (315.        , -0.0746039 )]>
# Convert this into galactic coordinates
center_coordinates_in_galactic = center_coordinates_in_equatorial.galactic
center_coordinates_in_galactic
<SkyCoord (Galactic): (l, b) in deg
    [(176.8796283 , -48.85086427), (176.89078038, -48.7358142 ),
     (176.70525363, -48.86216423), ..., ( 48.82487228, -28.4122831 ),
     ( 48.7216889 , -28.26178141), ( 48.84578935, -28.29847774)]>
# Calculate the bilinear interpolation we must apply to each healpix cell to get the values in the other coordinate system
healpix, weights = cdshealpix.bilinear_interpolation(
    center_coordinates_in_galactic.l,
    center_coordinates_in_galactic.b,
    depth=norder,
)
# Apply the interpolation
ext_map_equatorial_nested = (extinction_values[healpix.data] * weights.data).sum(axis=1)
ext_map_equatorial_nested
array([0.08981742, 0.0991632 , 0.08249644, ..., 0.08323811, 0.08352184,
       0.0820533 ])

Next we declare which pixel we want to use, let’s take all pixels with an extinction lower than 0.5:

low_extinction_index = np.where(ext_map_equatorial_nested < 0.5)[0]
print(
    f"The low extinction criteria keeps {round((len(low_extinction_index)/ len(extinction_values)*100), 2)}% of the sky map",
)
The low extinction criteria keeps 86.74% of the sky map

And let’s create a MOC from this criteria

moc_low_extinction = mocpy.MOC.from_healpix_cells(
    low_extinction_index,
    np.full(
        (
            len(
                low_extinction_index,
            )
        ),
        norder,
    ),
    norder,
)

Step 4: Find out which regions are covered by the MASH short-red images in the low extinction regions defined above#

To find out the sky regions of the MASH sample that are at low extinction, we build the intersection of the two MOCs.

moc_intersection = moc_low_extinction.intersection(moc_mash)
# Once the intersection is bluit, we can for example print the sky fraction :
print(
    f"The intersection of the two MOCs covers {round(moc_intersection.sky_fraction * 100, 4)}% of the sky",
)
The intersection of the two MOCs covers 0.4778% of the sky

Now we can visualize the coverage of the two MOCs and their intersection. The grey area is where the extinction is low. The blue one is the MASH coverage. The tiny red dots show the MASH coverage in low extinction regions.

fig = plt.figure(111, figsize=(10, 7))

with mocpy.WCS(
    fig,
    fov=140 * u.deg,
    center=SkyCoord(200, -20, unit="deg", frame="icrs"),
    coordsys="icrs",
    rotation=Angle(0, u.degree),
    projection="AIT",
) as wcs:
    ax = fig.add_subplot(1, 1, 1, projection=wcs)

    moc_low_extinction.fill(
        ax=ax,
        wcs=wcs,
        alpha=0.5,
        fill=True,
        color="grey",
        label="low extinction",
    )
    moc_mash.fill(
        ax=ax,
        wcs=wcs,
        alpha=0.5,
        fill=True,
        color="dodgerblue",
        label="MASH coverage",
    )
    moc_intersection.fill(
        ax=ax,
        wcs=wcs,
        alpha=0.5,
        fill=True,
        color="crimson",
        label="MASH in low extinction regions",
    )
    # Sets labels
    ax.set_xlabel("RA")
    ax.set_ylabel("Dec")
    # Sets ticks
    lon, lat = ax.coords[0], ax.coords[1]
    lon.set_major_formatter("hh:mm:ss")
    lat.set_major_formatter("dd:mm")
    lon.set_ticklabel(exclude_overlapping=True)
    lat.set_ticklabel(exclude_overlapping=True)
plt.legend()
<matplotlib.legend.Legend at 0x7f75661e2dd0>
_images/6b36b7ed6e49cd9a006e837d4489b436a4ac36ec94575585b54f9cadcd43236f.png

Step 5: Query the 2MASS and Gaia Catalogues by MOC#

Without the usage of MOC, querying for sources in the low extinction regions covered by the MASH subsample would be tedious or even impossible. Indeed, one would need to load the whole catalog and make selections which would not be possible given the size of some catalogs. Alternatively, one would need to query the catalog field by field, which would take time and several queries. Instead, here we will use the power of MOC files to query large catalogs directly in the covered regions only. We will use coverages of the low extinction and MASH regions to query for sources from the Gaia and 2MASS surveys in these highly non-continuous and non-trivial shape areas.

First, let’s see which Gaia and 2MASS catalogs are available on VizieR. We could, as above, use the TAP endpoint of VizieR. But we show below the Vizier module in the astroquery package.

catalog_list_twomass = Vizier.find_catalogs("Cutri")
for k, v in catalog_list_twomass.items():
    print(k, ": ", v.description)
J/ApJ/874/82 :  Follow-up photometry & spectroscopy of PTF14jg (Hillenbrand+, 2019)
II/241 :  2MASS Catalog Incremental Data Release (IPAC/UMass, 2000)
J/ApJ/569/23 :  Optical polarisation of 2MASS QSOs (Smith+, 2002)
II/246 :  2MASS All-Sky Catalog of Point Sources (Cutri+ 2003)
J/AJ/154/53 :  WISE/NEOWISE observations of comets (Bauer+, 2017)
J/ApJ/743/156 :  NEOWISE observations of NEOs: preliminary results (Mainzer+, 2011)
II/365 :  The CatWISE2020 catalog (updated version 28-Jan-2021) (Marocco+, 2021)
II/126 :  IRAS Serendipitous Survey Catalog (IPAC 1986)
J/AJ/112/62 :  Quasar absorption-line systems (Tanner+ 1996)
J/AJ/156/60 :  Thermal model fits for short-arc NEOs with NEOWISE (Masiero+, 2018)
J/ApJ/814/117 :  NEOWISE Reactivation mission: 1st yr data (Nugent+, 2015)
J/AJ/126/63 :  Host galaxies of 2MASS-QSOs with z<=3 (Hutchings+, 2003)
J/ApJ/742/40 :  Jovian Trojans asteroids with WISE/NEOWISE (Grav+, 2011)
II/311 :  WISE All-Sky Data Release (Cutri+ 2012)
J/ApJ/805/90 :  WISE ELIRGs and comparison with QSOs (Tsai+, 2015)
J/ApJ/760/L12 :  WISE/NEOWISE NEOs preliminary thermal fits (Mainzer+, 2012)
J/ApJS/190/100 :  NIR proper motion survey using 2MASS (Kirkpatrick+, 2010)
J/ApJ/744/197 :  WISE/NEOWISE observations of Hilda asteroids (Grav+, 2012)
J/ApJS/224/36 :  The AllWISE motion survey (AllWISE2) (Kirkpatrick+, 2016)
J/ApJS/175/191 :  Variables from 2MASS calibration fields (Plavchan+, 2008)
J/ApJ/784/110 :  NEOWISE observations of 105 near-Earth objects (Mainzer+, 2014)
J/ApJ/719/550 :  Deep NIR imaging of {rho} Oph cloud core (Marsh+, 2010)
J/AJ/127/646 :  Unbiased census of AGN in 2MASS (Francis+, 2004)
J/ApJ/780/92 :  Light curves of the RR Lyr SDSS J015450.17+001500.7 (Szabo+, 2014)
J/ApJ/759/L8 :  WISE/NEOWISE observations of main belt asteroids (Masiero+, 2012)
J/ApJ/783/122 :  AllWISE motion survey (Kirkpatrick+, 2014)
J/ApJS/95/1 :  Atlas of Quasar Energy Distributions (Elvis+ 1994)
J/AJ/144/148 :  Infrared photometry of brown dwarf and Hyper-LIRG (Griffith+, 2012)
J/AJ/152/63 :  NEOWISE reactivation mission: 2nd yr data (Nugent+, 2016)
II/307 :  WISE Preliminary Data Release (Cutri+ 2011)
J/ApJ/560/566 :  K-band galaxy luminosity function from 2MASS (Kochanek+, 2001)
VII/233 :  The 2MASS Extended sources (IPAC/UMass, 2003-2006)
J/ApJS/234/23 :  The WISE AGN candidates catalogs (Assef+, 2018)
J/ApJS/199/26 :  The 2MASS Redshift Survey (2MRS) (Huchra+, 2012)
J/AJ/154/168 :  NEOWISE: thermal model fits for NEOs and MBAs (Masiero+, 2017)
J/ApJ/792/30 :  NEOWISE magnitudes for near-Earth objects (Mainzer+, 2014)
II/281 :  2MASS 6X Point Source Working Database / Catalog (Cutri+ 2006)
II/328 :  AllWISE Data Release (Cutri+ 2013)
J/ApJ/564/421 :  Spectra of T dwarfs. I. (Burgasser+, 2002)
J/ApJS/172/663 :  Infrared observations of the Pleiades (Stauffer+, 2007)
J/AJ/135/2245 :  Absolute spectrum of the Sun and Vega for 0.2-30um (Rieke+, 2008)
J/AJ/125/2521 :  2MASS6x survey of the Lockman Hole (Beichman+, 2003)
J/ApJ/741/68 :  Main Belt asteroids with WISE/NEOWISE. I. (Masiero+, 2011)
J/ApJ/635/214 :  Chandra X-ray sources and NIR identifications (Ebisawa+, 2005)
J/PASP/113/10 :  Sub-mJy radio sources complete sample (Masci+, 2001)
J/ApJ/713/330 :  Spitzer observations of major-merger galaxies (Xu+, 2010)
catalog_list_gaia = Vizier.find_catalogs("Gaia DR2", max_catalogs=1000)
for k, v in catalog_list_gaia.items():
    print(k, ": ", v.description)
I/324 :  The Initial Gaia Source List (IGSL) (Smart, 2013)
I/337 :  Gaia DR1 (Gaia Collaboration, 2016)
I/345 :  Gaia DR2 (Gaia Collaboration, 2018)
I/347 :  Distances to 1.33 billion stars in Gaia DR2 (Bailer-Jones+, 2018)
I/350 :  Gaia EDR3 (Gaia Collaboration, 2020)
I/352 :  Distances to 1.47 billion stars in Gaia EDR3 (Bailer-Jones+, 2021)
I/355 :  Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
I/356 :  Gaia DR3 Part 2. Extra-galactic (Gaia Collaboration, 2022)
I/357 :  Gaia DR3 Part 3. Non-single stars (Gaia Collaboration, 2022)
I/358 :  Gaia DR3 Part 4. Variability (Gaia Collaboration, 2022)
I/359 :  Gaia DR3 Part 5. Solar System (Gaia Collaboration, 2022)
I/360 :  Gaia DR3 Part 6. Performance verification (Gaia Collaboration, 2022)
I/361 :  Gaia Focused Product Release (Gaia FPR) (Gaia Collaboration, 2023)
IV/36 :  Gaia-IPHAS/KIS Value-Added Catalogues (Scaringi+, 2018)
VI/137 :  GaiaSimu Universe Model Snapshot (Robin+, 2012)
VI/145 :  ASC Gaia Attitude Star Catalog (Smart, 2015)
J/A+A/523/A48 :  Gaia photometry (Jordi+, 2010)
J/A+A/674/A25 :  Gaia DR3. spurious signals (Holl+, 2023)
V/149 :  LAMOST DR2 catalogs (Luo+, 2016)
IX/52 :  XXL Survey. DR2 (Chiappetti+, 2018)
J/A+A/634/A133 :  X-Shooter Spectral Library (XSL). DR2 (Gonneau+, 2020)
WARNING: UnitsWarning: Unit 'Sun' not supported by the VOUnit standard. Did you mean uN? [astropy.units.format.vounit]
WARNING: UnitsWarning: The unit 'erg' has been deprecated in the VOUnit standard. Suggested: cm**2.g.s**-2. [astropy.units.format.utils]

For 2MASS we will want to use II/246 :  2MASS All-Sky Catalog of Point Sources (Cutri+ 2003) and for Gaia I/345 :  Gaia DR2 (Gaia Collaboration, 2018). Before we query the full two tables we only look at a few sources for each table to understand which columns are available. The query below will give us 50 sources each – the default for the get_catalogs method.

test_twomass = Vizier.get_catalogs("II/246")
print(test_twomass)
test_twomass[0]
TableList with 1 tables:
	'0:II/246/out' with 15 column(s) and 50 row(s) 
Table length=50
RAJ2000DEJ2000_2MASSJmage_JmagHmage_HmagKmage_KmagQflgRflgBflgCflgXflgAflg
degdegmagmagmagmagmagmag
float64float64str17float32float32float32float32float32float32str3str3str3str3uint8uint8
44.9960550.00556502595905+000020016.3760.09715.7700.14015.2580.141ABB22211100000
45.0048570.01980603000116+000111312.5290.02411.9540.03011.8740.029AAA22211100000
45.0041930.02095603000100+000115414.8450.05614.2230.07714.0160.055AAA222111ccc00
44.9950740.03820402595881+000217516.7460.13415.8140.14016.1250.324BBD22211100000
44.9638510.04358702595132+000236916.4760.11316.0570.17515.564--BCU22011000000
45.0482810.04832903001158+000253913.3540.02512.8740.03012.6990.030AAA22211100000
45.0235640.06847203000565+000406414.7290.03814.1410.04314.0240.074AAA22211100000
44.9933070.07642302595839+000435114.9210.03614.5570.06314.4720.081AAA22211100000
44.9691320.08445802595259+000504016.0860.08615.3810.11115.3810.176ABC22211100000
44.9784740.09252202595483+000533016.5050.12216.1690.18215.950--BCU22011000000
44.9327980.06488102594387+000353515.7600.07115.0790.07514.8440.122AAB22211100000
44.9511590.10528702594827+000619015.2650.05014.5600.05014.5240.091AAA22211100000
44.9960080.08500402595904+000506015.0860.03714.9330.06814.8310.106AAA22211100000
45.0137760.08792703000330+000516516.5170.11115.8510.12815.3630.170BBC22211100000
44.9830640.09652102595593+000547415.5060.05515.0210.07014.5750.077AAA22211100000
45.0052380.10205303000125+000607316.5060.11415.9770.16916.0790.276BCD22211100000
45.0955020.08538603002292+000507316.5010.14915.767--15.259--CUU20010000000
45.0569710.11511903001367+000654416.4570.13315.9450.17215.992--BCU22011000000
45.1241190.13675203002978+000812314.9120.04814.3980.05714.1240.069AAA22211100000
45.1321080.13781403003170+000816111.3200.02311.0070.02510.9260.021AAA22211100000
45.1646610.15454903003951+000916314.0850.03513.7800.03413.6780.041AAA22211100000
45.1463260.16041703003511+000937515.2600.05714.7120.05414.2700.070AAA22211100000
45.1165870.14472403002798+000841014.0170.03213.6140.03413.5030.048AAA22211100000
45.1298060.14794703003115+000852616.4270.12716.1020.20315.451--BCU22011000000
45.0339970.12118703000815+000716216.7100.21215.6070.18815.488--CCU22011000000
45.0581130.12747803001394+000738910.9710.02210.5270.02510.3500.020AAA22211100000
45.0336800.12872503000808+000743412.9830.02512.5480.02812.3890.030AAA22211100000
45.0558850.14611903001341+000846013.9260.02613.5140.03513.3440.044AAA22211100000
45.0344680.14384903000827+000837816.070--15.3090.14315.4680.221UBD02201100000
45.0318130.14416103000763+000838916.7650.18615.7610.14716.277--CBU22011000000
45.0191460.16320403000459+000947516.2860.12115.7570.15314.150--BBU226220cc000
45.0198770.16377403000477+000949516.0610.12915.4740.13414.149--BBU226220cc000
45.0615110.17092603001476+001015316.6930.16716.1390.19116.258--CCU22011000000
45.0836890.16939703002008+001009815.5320.06614.9100.07614.5020.101AAA22211100000
45.0969260.18130503002326+001052616.9350.20916.3890.23115.7940.316CDD22211100000
45.0634620.19565703001523+001144314.6040.03214.3100.04814.0830.062AAA22211100000
44.8948430.11417302593476+000651013.1810.02412.8680.02212.7970.030AAA22211100000
44.9102720.11474902593846+000653015.9360.08215.1710.08114.9860.138AAB22211100000
44.9127840.12403502593906+000726515.0020.04814.3760.04814.2190.071AAA22211100000
44.9529500.13382902594870+000801715.3330.05414.6610.05214.4230.089AAA22211100000
44.9935020.14911502595844+000856816.6160.15815.8900.15515.4170.198CBC22211100000
44.9727900.15507702595346+000918213.6530.02613.1620.03312.8890.033AAA22211100000
44.9208400.14607602594100+000845815.6870.07015.3880.09514.9400.128AAB22211100000
44.9257870.14971302594218+000858916.6260.16116.1720.21415.833--CCU22011000000
44.9228980.15126202594149+000904515.4080.05314.8090.06914.4940.093AAA22211100000
44.9397490.15818802594553+000929416.3070.11215.6310.12615.348--BBU22011000000
44.9589720.15238902595015+000908615.6720.07015.1140.08414.9920.142AAB22211100000
44.9514070.15676202594833+000924315.4520.05414.9080.07214.9770.140AAB22211100000
44.9699440.16361302595278+000949015.4400.05814.7750.06314.3770.080AAA22211100000
44.8753960.11820602593009+000705514.9150.03214.6170.04614.5960.084AAA22211100000
test_gaia = Vizier.get_catalogs("I/345")
print(test_gaia)
test_gaia[0]
TableList with 20 tables:
	'0:I/345/gaia2' with 32 column(s) and 50 row(s) 
	'1:I/345/rvstdcat' with 30 column(s) and 50 row(s) 
	'2:I/345/rvstdmes' with 7 column(s) and 50 row(s) 
	'3:I/345/allwise' with 2 column(s) and 50 row(s) 
	'4:I/345/iers' with 2 column(s) and 50 row(s) 
	'5:I/345/cepheid' with 23 column(s) and 50 row(s) 
	'6:I/345/rrlyrae' with 21 column(s) and 50 row(s) 
	'7:I/345/lpv' with 11 column(s) and 50 row(s) 
	'8:I/345/varres' with 7 column(s) and 50 row(s) 
	'9:I/345/shortts' with 9 column(s) and 50 row(s) 
	'10:I/345/tsstat' with 13 column(s) and 50 row(s) 
	'11:I/345/numtrans' with 4 column(s) and 50 row(s) 
	'12:I/345/transits' with 20 column(s) and 50 row(s) 
	'13:I/345/rm' with 9 column(s) and 50 row(s) 
	'14:I/345/rmseg' with 16 column(s) and 50 row(s) 
	'15:I/345/rmout' with 2 column(s) and 50 row(s) 
	'16:I/345/ssoobj' with 6 column(s) and 50 row(s) 
	'17:I/345/ssoorb' with 19 column(s) and 50 row(s) 
	'18:I/345/ssores' with 10 column(s) and 50 row(s) 
	'19:I/345/ssoobs' with 7 column(s) and 50 row(s) 
Table length=50
RA_ICRSe_RA_ICRSDE_ICRSe_DE_ICRSSourcePlxe_PlxpmRAe_pmRApmDEe_pmDEDupFGe_FGGmage_GmagFBPe_FBPBPmage_BPmagFRPe_FRPRPmage_RPmagBP-RPRVe_RVTeffAGE_BP-RP_RadLum
degmasdegmasmasmasmas / yrmas / yrmas / yrmas / yrmagmagmagmagmagmagmagkm / skm / sKmagmagsolRadsolLum
float64float64float64float64int64float64float32float64float32float64float32uint8float32float32float64float64float32float32float64float64float32float32float64float64float64float64float32float64float32float32float32float64
46.888724679870.70441.952524477990.76356276837006579200.16520.9827-2.4291.910-2.5842.3530121.91.26620.47310.011382.5911.320.55910.1486102.510.8419.73530.11480.8238--------------
46.895436088580.11201.955097895920.10396277180603971840.63470.12455.6300.250-3.7570.236019322.59417.47340.0015851.814.2318.02560.018116369.31216.72710.00621.2985--------------
46.888933543510.03851.965571795450.03476277867797248000.56150.04334.8190.085-1.6400.08101.226e+046.30415.46720.0006644424.8715.82850.0042844111.5214.94590.00150.8826----5759.670.46930.23901.251.537
46.792164025120.69781.912069210550.79416278254345318403.73881.037910.8751.3712.6971.4410170.11.92820.11190.012341.7414.1621.30010.3685237.33.84618.82360.01762.4764--------------
46.810644407671.34101.921947833501.35846278554994837761.63161.6121-10.8142.7891.7662.4620206.22.5819.90280.013653.798.52221.02460.17202627.91918.71620.03282.3084--------------
46.783177822210.46761.922387923630.5522627889859081600-0.54270.68571.4400.901-1.6130.8970228.71.54719.79020.007386.939.37620.50340.11712139.3518.94100.04771.5624--------------
46.798381880223.87171.929308527843.5521627924218822272------------0227.42.94319.79620.0140273.814.0619.25790.0558784.213.7117.52580.01901.7321--------------
46.821287779290.05471.931708931370.05446279628740764164.90920.065622.0650.116-39.5250.108075938.14215.98730.0012198437.917.10770.0207927581.8314.84360.00962.2641----3731.060.21870.1355----
46.821107821950.17611.932220153130.17976279628734794241.71400.2177-2.2570.360-14.5070.334014355.46617.79640.0041--------------------------------
46.835502178710.03821.933406706650.03866279972338144002.17510.04538.0240.085-4.6690.08701.586e+0412.7615.18760.0009632817.8715.84810.00311.374e+0435.5514.41700.00281.4311----4526.240.15550.07500.670.168
46.831893378690.11901.937827290860.12536279929383009280.04090.14620.5590.248-1.9670.234016302.60217.65800.001773912.9218.17970.0190129915.7316.97800.01311.2017--------------
46.830944597660.12521.944102395740.13486279929383015680.17950.15794.6380.2561.5360.251014322.24417.79860.0017696.212.0218.24450.018710509.19317.20910.00951.0355--------------
46.790129785440.05441.931946467460.0535628134672769536-0.06660.06620.0100.112-2.0020.106069766.6616.07940.0010266920.2616.78540.0082625813.4315.27090.00231.5145----4425.00--------
46.784409322480.21041.942015089170.28836281303772546561.98000.2686-5.1840.405-9.8180.4530822.22.26618.40100.0030203.59.47319.57990.0505102212.117.23810.01292.3418--------------
46.769076609680.07281.940777486980.07616281690325080320.49200.0895-0.7050.151-7.0280.144039504.19816.69690.0012196126.1617.12020.0145291228.8216.10130.01071.0189----5188.860.30900.1670----
46.769547894820.73741.941855776790.87076281647380326400.64121.04541.0091.411-3.7971.4110161.51.36120.16800.009191.898.53720.44330.1009151.611.9819.30990.08581.1334--------------
46.767550486471.87431.943967727362.3932628164738056064------------079.031.620.94390.022012.627.00922.59890.6031117.212.4819.58950.11563.0094--------------
46.8039870105217.72521.960988372935.4931628336536755328------------089.311.87620.81110.0228--------------------------------
46.855266752380.41021.956333850360.48186284052551646720.96680.61201.7230.8257.5430.89202921.65519.52480.006294.6118.3320.41160.210437218.9118.33540.05522.0761--------------
46.856413623490.10391.957746584650.09916284095507105281.43630.131510.6680.212-11.6810.212031625.06216.93850.0017946.79.2217.91090.0106331418.915.96120.00621.9497----3776.251.47700.70900.800.117
46.828237394230.68981.957837509830.79416284396150433280.07640.95181.3191.4290.7531.4740171.71.38920.10120.008864.1413.3120.83350.2253211.620.0418.94800.10281.8856--------------
46.844819586850.49901.964015142180.5917628473974643584-0.59320.75081.4440.9060.3040.91302401.79119.73780.008198.5512.720.36730.139921514.3418.93070.07241.4366--------------
46.847441377510.82231.969665582380.95016284782695459840.73271.15002.1051.793-3.9241.8650142.41.29520.30460.009963.3415.1520.84720.2598198.115.0119.01960.08231.8276--------------
46.883219473332.30771.973976915172.3952628546989662976------------02754.45819.58990.0176377.631.5618.90870.090785818.217.42820.02301.4805--------------
46.867797506370.30521.982979107040.3620628611413603712-0.09500.41403.5860.656-13.9120.7140376.32.02719.24950.005870.4410.1520.73180.1565477.49.20718.06480.02092.6670--------------
46.821367093820.27161.977534325510.25476287187877154560.30530.33458.9250.511-7.1860.466020088.19417.43120.0044652.821.2418.31440.0353221619.2816.39800.00941.9164--------------
46.861632508091.60631.994571147602.4025628817573128960------------081.011.58320.91700.021291.0748.9320.45290.5833119.117.6419.57230.16080.8806--------------
46.866694191780.93681.995407438810.91786288175721710083.31261.113618.4431.988-34.7661.7620312.74.93119.45060.017145.029.13921.21790.2204530.58.98217.95020.01843.2677--------------
46.836554728110.29012.003221457600.30666288519317762560.46640.39270.5960.549-6.4880.5370570.62.32818.79760.0044308.718.1119.12740.0637417.625.3718.20990.06590.9175--------------
46.677179280340.34011.881343675820.31326289550109603842.16320.41076.2190.717-7.8890.5830612.12.19318.72130.0039146.320.4319.93840.1516807.616.3817.49390.02202.4446--------------
46.678774899290.07631.882962319140.06866289593064939521.97010.08868.0340.1712.1730.152049914.67616.44280.0010159721.1917.34340.0144509916.3915.49310.00351.8503----4078.000.68070.32670.570.081
46.679674573140.14301.887604681010.13026289550109625600.45660.16986.1760.308-4.3510.253017402.71617.58690.0017736.930.0918.18290.0443151512.9816.81120.00931.3717--------------
46.663298900830.26041.881448555620.23586289936662329600.73290.30831.1070.565-6.8180.461014894.77317.75640.0035487.634.1718.63130.0761169414.0116.68960.00901.9417--------------
46.672360353750.03961.900379365900.03646290237303074561.72320.04610.7160.090-10.0970.08301.536e+0557.2512.72220.00048.626e+04117.113.01190.00159.924e+04167.912.27020.00180.7417----6218.330.30730.15671.222.008
46.710111658110.10181.902578592080.09166290967454812160.35300.11860.9150.229-3.9950.204031154.54116.95470.0016166528.2217.29810.0184215026.5716.43100.01340.8671----5641.870.16300.0780----
46.696569251220.14001.902772461900.12816290924499197440.18640.1670-2.2290.303-0.5080.251017853.24317.55930.0020899.868.5917.96610.0828143517.2116.87000.01301.0961--------------
46.667393017801.43581.909894110921.3438629332969144320------------0125.51.86320.44200.016159.2910.3520.91890.1896177.88.14519.13740.04981.7815--------------
46.672261384281.01501.917027371660.95076293329682329604.01151.2070-5.8702.1372.2481.7900156.91.95320.19910.013576.934.60520.63620.065022210.4818.89590.05131.7403--------------
46.658473997300.12621.913574431130.13336294016875667200.55960.16848.0640.280-1.3520.240022893.44117.28920.0016100341.4417.84780.044818697.67316.58280.00451.2651--------------
46.725147733880.14901.922108865220.09766295090623060481.17550.13658.1400.38410.4300.263035004.56316.82830.0014140424.2217.48270.0187310415.5516.03210.00541.4506----4465.270.14300.07820.600.130
46.702156419280.86701.921812283380.79306295391266639360.57401.08748.6951.724-7.3871.3510204.41.91219.91210.010255.815.78220.98450.1125284.317.0118.62760.06502.3569--------------
46.716364763350.50451.928708554060.31236295734862635520.07660.54274.7611.170-4.9821.00206173.36918.71270.005930520.4919.14070.0730498.413.1718.01800.02871.1227--------------
46.695891323742.12961.939297080591.4561629779644835200------------0120.61.53920.48510.013937.688.4321.41100.2429159.711.5519.25380.07852.1572--------------
46.682132600081.74181.933678634571.49076298182999865602.20152.022515.4933.6492.5333.9390112.91.51820.55620.014644.499.27821.23070.2264149.610.5819.32450.07681.9062--------------
46.705371447831.53211.950050050213.2442629882724966784------------0101.21.64920.67480.017727.410.753821.75660.0299132.90.874319.45350.00712.3032--------------
46.730261738391.00561.958312834311.1484629917084710528-1.07681.441914.4692.531-8.0663.1780111.11.54620.57370.015133.579.03221.53660.2921161.111.7119.24380.07892.2928--------------
46.724504269200.71761.964002549540.87436299213785701121.51771.0542-7.5032.077-9.0662.7100141.81.46820.30890.0112--------------------------------
46.724484884850.38251.964280763010.45696299170836551681.16940.5520-5.3460.761-7.8330.7540330.31.4419.39110.0047119.512.8320.15810.1166502.417.0518.00930.03692.1489--------------
46.715094844351.07111.962879859661.0777629985803131520-1.51661.19962.4502.902-2.3093.1990141.91.49720.30830.011583.7613.5820.54380.1761101.510.9419.74560.11700.7982--------------
46.620366811420.52421.910342186170.4184630020162856704-0.54330.58141.2871.072-2.6110.8440356.12.13619.30940.0065163.813.8819.81570.0920261.714.518.71740.06011.0982--------------

As you will see below, we only need coordinates, 2MASS photometry in the H and K band, and Gaia photometry in the Gaia G band. So we’ll query the tables II/246/out for 2MASS and I/345/gaia2 for Gaia DR2:

twomass = moc_intersection.query_vizier_table("II/246/out", max_rows=20000)
twomass
Table length=20000
_2MASSRAJ2000DEJ2000errHalfMajerrHalfMinerrPosAngJmagHmagKmage_Jmage_Hmage_KmagQflRflXMeasureJD
degdegarcsecarcsecdegmagmagmagmagmagmagd
str17float64float64float32float32float32float32float32float32float32float32float32str3int16uint8float64
06561231-2715514104.051312-27.2642960.110.1145.015.81615.53315.2740.0680.1040.192AAC22202451203.5942
06561317-2715210104.05491-27.2558440.060.0690.013.89113.29213.0590.0260.0330.034AAA22202451203.5942
06561481-2716053104.061712-27.2681620.060.0690.013.85713.33613.20.0260.0220.039AAA22202451203.5942
06561479-2715417104.061646-27.2615850.060.0690.013.4313.23413.2660.0230.0220.041AAA22202451203.5942
06561641-2715166104.068408-27.2546290.060.0645.013.84313.62513.5430.0290.0380.045AAA22202451203.5942
06561656-2715126104.069033-27.2535270.160.1575.014.94715.88314.747--0.137--UBU2002451203.5942
06561660-2715246104.069205-27.2568440.060.0690.015.0414.63114.4570.0520.0680.092AAA22202451203.5942
06561532-2714537104.063862-27.2482530.080.08135.015.69815.215.020.0530.070.143AAB22202451203.5942
06561543-2714325104.064318-27.2423630.060.0645.014.62714.23114.0750.0370.0420.068AAA22202451203.5942
06561711-2714541104.071307-27.2483830.060.0690.013.6713.30713.1630.0260.0250.029AAA22202451203.5942
06562032-2715025104.084702-27.2507060.070.064.014.72414.33114.4570.0290.0410.101AAA22202451203.5942
06562075-2714021104.086461-27.2339230.130.1345.016.48415.72816.2890.1050.107--AAU22002451203.5942
06561726-2714384104.071925-27.2440240.060.0690.013.48113.13913.0140.0230.0270.033AAA22202451203.5942
06561759-2713598104.073293-27.233280.060.0690.014.76314.17713.8720.0320.0390.056AAA22202451203.5942
06561894-2713384104.078949-27.2273390.250.2381.016.52816.10815.4030.1180.1490.229BBD22202451203.5942
06561879-2714289104.078292-27.2413770.070.0745.015.64114.95114.8280.0540.0570.113AAB22202451203.5942
06561060-2715111104.044183-27.2530840.20.1868.016.70316.29916.2930.1290.16--BCU22002451203.5942
06561225-2714413104.05108-27.2448080.220.270.016.70716.41416.0160.120.179--BCU22002451203.5942
06561125-2714498104.04688-27.2471870.060.0690.010.3239.8349.6970.0230.0220.023AAA22202451203.5942
06561135-2714113104.047301-27.2364880.230.2175.016.79116.41914.3720.1310.195--BCU22002451203.5942
06560523-2714195104.021804-27.2387520.290.27135.017.28916.16315.7690.2050.1740.259DCD22202451203.5942
06560974-2713092104.040599-27.2192340.060.0690.014.28714.01913.9580.0260.0250.065AAA22202451203.5942
06561099-2714107104.045795-27.2363090.060.0690.014.1113.32613.160.0330.0330.03AAA22202451203.5942
06560717-2714071104.029884-27.2353250.150.1482.016.64715.91514.9480.1230.122--BBU22002451203.5942
06561342-2712466104.055943-27.2129480.060.0690.013.5113.22213.1580.0280.0250.029AAA22202451203.5942
06561218-2713314104.050776-27.2253950.270.2393.016.61715.98615.5060.1140.140.201BBD22202451203.5942
06561408-2712226104.058682-27.2062840.160.1575.016.81317.09616.8740.136----BUU20002451203.5942
06561494-2712042104.062284-27.2011720.180.1845.016.86915.92815.7760.161----CUU20002451203.5942
06561443-2712057104.060132-27.2015970.180.167.016.96716.50516.4480.147----CUU20002451203.5942
06561453-2713473104.060581-27.2298090.190.1945.016.73116.23615.50.1360.176--BCU22002451203.5942
06561833-2713238104.076384-27.2232910.060.0690.014.20713.96813.810.0260.0380.042AAA22202451203.5942
06561512-2712552104.063005-27.2153470.070.0745.015.59414.99614.7710.0530.0550.105AAB22202451203.5942
06561592-2714018104.066348-27.2338580.230.2145.016.78216.52216.7670.1420.211--BDU22002451203.5942
06561538-2712393104.064098-27.2109220.110.1145.016.0615.42215.1690.0790.08--AAU22002451203.5942
06561585-2712419104.066047-27.2116390.080.0845.015.49315.26815.2690.0510.0750.184AAC22202451203.5942
06561553-2712203104.064732-27.2056640.060.0690.014.29914.09213.90.0240.0310.06AAA22202451203.5942
06561657-2712502104.069083-27.2139550.170.15103.016.45616.09916.9430.1070.151--ABU22002451203.5942
06561926-2712497104.080262-27.2138060.20.1912.016.59716.30816.9320.1280.178--BCU22002451203.5942
................................................
06564615-2603043104.192326-26.0512080.060.0645.014.27113.99913.810.0390.0550.055AAA22202451296.4891
06564793-2602151104.199743-26.0375310.210.2145.016.29415.83416.8220.1360.195--BCU22002451296.4891
06564755-2603050104.198164-26.0513920.080.0890.015.64615.19414.8890.0760.1110.142ABB22202451296.4891
06565210-2602344104.217123-26.0428940.060.0690.012.11911.90411.860.0240.0230.024AAA22202451296.4891
06565221-2601084104.217559-26.0190030.070.0686.014.6314.37514.2760.0430.0490.082AAA22202451296.4892
06565248-2600433104.218697-26.0120470.080.0845.015.27615.13515.2070.0660.1090.174ABC22202451296.4892
06565239-2602257104.218325-26.0404950.180.1845.016.3215.60516.7560.1560.164--CCU22002451296.4891
06565253-2600349104.218898-26.0097030.090.083.015.33714.99915.1010.0650.1190.171ABC22202451296.4892
06565259-2601557104.219136-26.0321640.240.23.016.51615.6515.220.170.1730.193CCC22202451296.4892
06565291-2601264104.220475-26.0240150.060.0690.014.51114.0913.8780.0410.0310.064AAA22202451296.4892
06565329-2602237104.222078-26.0399190.260.2516.016.58615.84815.330.1870.210.216CCC22202451296.4891
06564879-2601431104.203331-26.0286620.260.254.016.41416.07915.950.1520.255--CDU22002451296.4892
06565135-2602016104.213961-26.0338040.070.0745.015.32514.92214.8110.0610.0890.129AAB22202451296.4892
06565082-2601367104.211777-26.0268650.160.15173.016.33916.21116.8080.136----BUU20002451296.4892
06564847-2601561104.201987-26.0322530.330.285.016.41815.91715.6050.1460.2330.265CDD22202451296.4892
06565344-2601095104.222672-26.0193290.290.284.016.52515.84115.4130.1640.2040.241CCD22202451296.4892
06565559-2601476104.231634-26.0298940.140.14135.016.16815.70115.0450.124----BUU20002451296.4892
06565181-2602205104.215894-26.0390360.290.2589.016.04316.05215.4360.1180.250.238BDD22202451296.4891
06564064-2600480104.169345-26.0133550.060.0645.013.91713.57613.4450.0340.0420.05AAA22202451296.4892
06563772-2601504104.157168-26.0306910.060.0690.013.0412.74712.7310.0260.0290.03AAA22202451296.4892
06564366-2601334104.181941-26.0259570.40.3187.016.516.24615.650.1710.2820.285CDD22202451296.4892
06564480-2601185104.186708-26.0218140.060.0645.014.32514.03914.0330.0320.0250.075AAA22202451296.4892
06564158-2602443104.173252-26.0456520.090.0990.015.32615.15415.0780.0690.1190.168ABC22202451296.4891
06563927-2601318104.163652-26.0255180.060.0645.013.96413.53213.3660.0320.0220.044AAA22202451296.4892
06564043-2601234104.168487-26.0231720.080.0790.015.66615.25514.6810.0830.1180.115ABB22202451296.4892
06564038-2601038104.168278-26.0177330.070.0745.015.17714.86814.9440.0510.0960.142AAB22202451296.4892
06564040-2601515104.168335-26.0309790.210.1882.016.17615.64415.080.1240.1790.168BCC22202451296.4892
06564539-2601032104.18915-26.0175690.280.27176.016.24715.53115.520.1140.1610.247BCD22202451296.4892
06564602-2600142104.191783-26.0039650.070.0745.015.01214.61514.5410.0520.0720.103AAA22202451296.4892
06564554-2600421104.189787-26.0116980.060.0645.014.96814.48214.0390.0480.0580.074AAA22202451296.4892
06564981-2600284104.207569-26.0078950.080.0845.015.82815.26314.80.0830.1330.143ABB22202451296.4892
06564783-2600242104.199297-26.0067460.120.1110.015.92815.54315.2420.0980.1490.184ACC22202451296.4892
06564644-2600212104.193517-26.0059030.070.0690.015.13214.39514.2240.0560.0740.084AAA22202451296.4892
06564711-2559578104.196329-25.9994010.280.2681.016.15215.52315.4840.1080.1470.255BCD22202451296.4892
06564864-2559555104.202699-25.998770.170.1641.016.35716.13616.9150.133----BUU20002451296.4892
06564676-2559428104.19487-25.9952390.070.060.013.57913.19813.1320.0320.0310.034AAA22202451296.4892
06564535-2559589104.188986-25.9997010.090.090.015.80215.26615.1440.0820.1270.174ABC22202451296.4892
06565753-2601231104.239722-26.0230870.060.0645.013.02612.93212.9440.0260.0290.033AAA22202451296.4892
06565979-2601016104.249134-26.0171150.170.16135.016.58915.93416.9260.1370.143--BBU22002451208.533
gaia = moc_intersection.query_vizier_table("I/345/gaia2", max_rows=20000)
gaia
Table length=20000
ra_epoch2000dec_epoch2000errHalfMajerrHalfMinerrPosAngsource_idrara_errordecdec_errorparallaxparallax_errorpmrapmra_errorpmdecpmdec_errorduplicated_sourcephot_g_mean_fluxphot_g_mean_flux_errorphot_g_mean_magphot_bp_mean_fluxphot_bp_mean_flux_errorphot_bp_mean_magphot_rp_mean_fluxphot_rp_mean_flux_errorphot_rp_mean_magbp_rpradial_velocityradial_velocity_errorrv_nb_transitsteff_vala_g_vale_bp_min_rp_valradius_vallum_val
degdegarcsecarcsecdegdegmasdegmasmasmasmas / yrmas / yrmas / yrmas / yre-/se-/smage-/se-/smage-/se-/smagmagkm / skm / sKmagmagRsunLsun
float64float64float32float32float32int64float64float64float64float64float64float64float64float64float64float64boolfloat64float64float64float64float64float64float64float64float64float64float64float64int16float64float32float32float64float64
104.0642397349738-27.24822453635080.0090.0020.02920584465727934848104.064239734972.314-27.248224536358.5789------------False80.46911.2134320.924294------------------0----------
104.0613350962615-27.24571205150620.020.0170.02920584465727934720104.061338655040.4795-27.245726105290.72662.79250.93190.7351.066-3.2641.314False110.2320.81178320.58259429.7616.1909721.667269127.1175.5034819.501412.165859----0----------
104.0616994142013-27.2681625010780.0010.0010.02920584362651704192104.061764721910.0195-27.268269830640.02821.58990.033113.4830.044-24.9280.058False12121.44.7641915.4794865099.412.522816.0825910058.714.842614.7555641.327026----04984.00.3090.14950.610.21
104.0638238272635-27.24825868065470.0020.0020.02920584461429778816104.063823293820.0521-27.248233159280.07650.65440.0872-0.110.1215.9280.159False2056.521.7954417.405537875.6866.9603817.9955181741.367.6776716.6596981.335819----0----------
104.0534300495002-27.25004261560940.0140.0110.02920584427070040064104.053426774620.3271-27.250036587040.53020.89470.6557-0.6760.7321.40.92False162.7740.81227420.15940593.946911.489120.419182132.2836.6203219.4581620.96102----0----------
104.0751267092824-27.24971400405210.0070.0060.02920584392710301952104.075123822640.1708-27.249696906410.2387-0.21910.2883-0.5960.383.9710.463False354.0830.96809619.315603194.9844.128619.626392251.525.0944818.7604870.865906----0----------
104.0742449208677-27.26440276444640.0090.0080.02920584392710299648104.074237336050.2424-27.264392085150.30220.10110.3706-1.5660.5312.480.558False249.170.92132519.697124112.1264.9964820.227123190.765.8883819.0607011.166422----0----------
104.0773944731844-27.26335045444130.0080.0070.02920584392710299520104.077393478670.2151-27.263336001180.2962-0.39640.3634-0.2050.4743.3570.546False270.9330.89897719.60621116.8015.9762120.18277216.3875.208318.9238451.258925----0----------
104.0570207631639-27.26466321109670.0120.010.02920584358350560512104.057018127720.2794-27.264641980720.37390.10620.4371-0.5440.6294.9310.787False224.0610.80597819.8124588.33154.7991120.4861185.4745.7490619.0912151.394884----0----------
104.067647998646-27.26548195443460.0110.0090.02920584358350560384104.06764329260.2658-27.265479225090.3692-0.65210.4458-0.9720.5910.6340.712False197.5160.77497119.94935889.66436.0520820.46984147.0164.9236119.3435061.126333----0----------
104.0625613443571-27.27794991392450.0050.0040.02920584358350560128104.06254643040.1315-27.277936846830.17140.55210.2058-3.0790.2863.0350.329False570.9421.2996418.796886282.4034.7461319.224215415.6345.6527918.2151431.009071----0----------
104.0524334477795-27.25288255078960.0160.0130.02920584431371234688104.052437061120.3667-27.252866595560.60650.76460.72550.7460.8333.7061.035False148.7670.91423820.25709746.53556.2879421.181927179.5235.840719.1266212.055306----0----------
104.0517864137349-27.26649893280450.0040.0030.02920584431368206208104.051786957320.0912-27.266487457840.13180.10510.15890.1120.2122.6650.267False924.0771.3283318.274096496.6885.4371418.611177629.1286.6456317.7650720.846106----0----------
104.0739324439192-27.26292476919020.0150.0130.02920584397008462464104.073926936560.3872-27.262963147730.5461-0.01570.6756-1.1370.852-8.9140.973False123.5270.70426320.45895830.1114.0824321.654573154.136.1270419.2922042.36237----0----------
104.0642873821425-27.24236461295040.0010.0010.02920584465730915200104.064264770030.0212-27.242376755320.02960.85570.0337-4.6690.047-2.820.058False9587.883.8037415.7340594941.8211.989416.116676699.0910.766915.196880.91979----05420.50.12520.0570.830.533
104.0712892584117-27.24838483492060.0010.0010.02920584465730915456104.071296843220.0153-27.248386193220.02220.45090.0251.5660.034-0.3150.044False18483.05.2167915.0214358930.6212.259415.47418513774.611.376214.414221.059965----05456.5----2.153.689
104.0684067796523-27.25464493550250.0010.0010.02920584397011440128104.068399478110.0148-27.254617165350.02170.33480.025-1.5080.0346.450.042False20987.16.3841214.88348511622.421.730215.18814613850.619.178314.4082450.779901----06071.84----2.447.297
104.0512108539907-27.25019319518520.0290.0210.02920584431368429056104.051212049130.6006-27.2501843561.06641.75141.21030.2471.3612.0531.856False94.87720.80138920.74546254.28027.7350121.01478481.26377.7626519.9871791.027605----0----------
104.0527566427982-27.26632544366330.0090.0090.02920584431368206080104.052756061530.2352-27.266313742140.2950.26030.3544-0.120.5512.7180.594False278.1381.0274819.577713129.9616.4284220.066856240.147.1896718.810761.256096----0----------
104.0569364956352-27.25343241359420.0020.00290.02920584431368428544104.056936495641.5651-27.253432413591.5649------------False64.60021.0656821.16278------------------0----------
104.0577788453587-27.24390386566420.0370.0280.02920584465728845952104.057792201230.847-27.243884514871.56051.63721.86782.7581.8094.4942.403False77.33621.0224420.9674134.21825.7837821.51574766.43548.8662420.2059211.309826----0----------
104.0625240059008-27.25085168505740.0040.0020.02920584465728845184104.06252400591.864-27.250851685063.8963------------False80.76491.1110920.9203142.48775.281221.2807367.50396.1065520.1885991.092131----0----------
104.0690267560499-27.25348126924290.0060.0050.02920584392712980352104.069031018820.1284-27.253464777560.1836-0.24490.22470.880.3173.830.381False608.5621.3104718.727604274.9457.0549219.253273561.6788.3421717.8882031.36507----0----------
104.0513121237182-27.26428585598670.0020.0010.02920584431371181056104.05130548980.0424-27.26427331770.05850.10920.0674-1.370.0972.9120.12False3201.762.2526116.9248941755.267.9596917.2405362139.618.1207816.4360870.804449----05904.33--------
104.0553448156352-27.2581125390250.0080.0070.02920584431371180544104.055323922470.1939-27.258097254180.280.95080.3348-4.3140.4353.550.528False302.5960.93236119.48620887.03995.2025820.502092315.2176.3591118.5153941.986698----0----------
104.061620479337-27.26158148072650.0010.00.02920584362651703552104.061601342210.0127-27.261547034780.01940.58150.0219-3.9510.0298.00.04True38338.08.2773214.22929123016.721.549214.4462822897.420.795813.8624550.583824----07123.00.69850.351.384.412
104.0548954733497-27.25584632592910.0020.0010.02920584431371180032104.054857255220.0416-27.255808758180.06053.1710.0701-7.8910.0938.7250.125False3933.042.9022516.701544757.8118.4754418.1524855000.213.836915.5144522.638033----03850.87--------
104.0694340110457-27.25767121193350.0020.0020.02920584397011440896104.069448403190.0433-27.257667132420.06040.19990.07042.9720.0970.9470.117False2738.991.9156117.0943891434.317.0391517.4597781970.627.9725116.5254140.934364----0----------
104.0691908712154-27.25682961987570.0010.0010.02920584397011440768104.06920537090.0312-27.256826403850.04320.36910.0512.9940.0710.7470.084False5024.62.940516.4356122434.7611.480916.8852483781.88.8371215.8176721.067576----05528.971.2120.612----
104.0516229058904-27.26547440885670.0070.0060.02920584427074472960104.051619941760.166-27.265469873880.22040.09850.2568-0.6120.381.0530.443False413.8271.0933419.146318213.8235.5174419.52625289.6466.4182418.6072520.918999----0----------
104.0456279910352-27.25534588865840.0270.0240.02920584431368205440104.0456327650.7158-27.255343500251.16540.84411.70190.9861.5550.5551.717True98.63621.0113220.70327639.57546.477921.357824100.0459.0979819.761431.596394----0----------
104.0573071065338-27.25803046208290.0140.0110.02920584431368203776104.057318592170.3295-27.258015665050.45451.90670.55262.3710.7293.4370.88False149.6560.78343620.25063346.69634.7945421.17818171.7837.3998419.174472.00371----0----------
104.0680470357636-27.26375058256830.0450.03690.02920584397008464768104.068047546851.4129-27.263732958811.24121.28191.43680.1062.9034.0932.318False89.73681.0089420.8059431.85966.2552321.59328758.99334.9963320.3349131.258373----0----------
104.0616652942046-27.25324647157150.040.0260.02920584465727938816104.061675684490.7841-27.253231620621.1781-0.73621.45182.1451.7043.4492.55False82.77731.0812620.89358738.26854.6613821.39428378.06345.0918520.0308021.363482----0----------
104.0662806585689-27.25466817360010.030.0230.02920584465727937536104.066284853410.6252-27.254662600930.91081.23731.08330.8661.4511.2941.909True178.2052.4901520.06106497.0517.1471320.383888219.5225.9642818.9082241.475664----0----------
104.0788155655205-27.22727984143670.0040.0040.02920584598868738304104.078819701160.1197-27.227286363570.1490.59070.17590.8540.263-1.5150.275False681.8221.1883118.604189264.3536.1570619.295929617.4057.1753917.7854941.510435----0----------
104.088664953225-27.22829895832980.0080.0070.02920584598868738048104.088670626190.1944-27.228297361890.26160.4580.31771.1720.4240.3710.502False316.5190.89084119.437366131.639.3351820.053001280.3386.2086218.6427151.410286----0----------
104.0853780961902-27.23015827313150.0130.0110.02920584598868737664104.085378034750.3299-27.230148436310.4265-0.22190.5139-0.0130.7262.2850.826False175.190.83406120.07959684.97997.7522820.528097151.738.9259619.3092441.218853----0----------
.........................................................................................................
103.6980191636504-26.69554537375160.0130.0110.02920662595476101504103.698025239540.3128-26.695530350340.45560.74540.56981.2610.7063.4890.851False163.7820.73191820.15270272.10566.5549720.706465157.4114.9204819.2693351.43713----0----------
103.6746416153805-26.69107062805170.0040.0030.02920662629835864704103.674637195930.0939-26.691053823720.13120.11450.1554-0.9170.2053.9030.276False809.8641.2502718.417336407.8666.1780418.825096603.636.2325517.8099921.015104----0----------
103.6818307323895-26.6977640073040.0020.0010.02920662629835835264103.681828480530.0334-26.697753485390.04650.06760.0538-0.4670.0712.4440.098False4659.182.5183616.5175912956.529.0378816.6744372550.046.6492916.2455540.428883----07440.460.6780.3765----
103.6895322687124-26.69185951620090.0020.0020.02920662668492552832103.689532268711.7844-26.69185951621.8236------------False69.70311.0224221.080235------------------0----------
103.7083903844813-26.69128667335540.0010.0010.02920662595476120576103.708383280310.0264-26.6912636960.03720.48320.0434-1.4740.0575.3370.077False7221.163.6077816.0418473818.9710.827316.396524989.1412.594215.5168560.879664----05943.330.3260.17271.041.211
103.6847552008415-26.69793206187530.0240.0170.02920662565411348224103.684752993220.5015-26.697930196930.8085-2.40130.9869-0.4581.0780.4331.571False95.24220.79819220.74129138.65634.2350721.38333795.47556.5723419.8121911.571146----0----------
103.6804167212213-26.68990737678290.0480.0420.02920662634133582976103.680422278981.2405-26.689909532281.5957-2.8682.38551.1532.739-0.5013.081False80.9461.3797220.917877131.63655.965920.052952114.97519.235819.6104130.442539----0----------
103.6729444953412-26.6897421606950.0160.0130.02920662634130822144103.672935211850.3692-26.689725745740.5309-0.00260.6628-1.9260.8333.8131.007False155.4070.75989920.20969267.79296.2873920.773428173.3357.1922719.1647031.608725----0----------
103.6930010144782-26.70322471977410.0220.0190.02920662565411346176103.692993139750.5775-26.703219682830.7472-0.19380.9341-1.6341.2191.171.429False95.57760.77157920.73747449.8986.5126721.1061879.80497.3293220.0068451.099335----0----------
103.6938936643781-26.68139907622610.020.0170.02920662668490514816103.693906708480.54-26.681340294450.68971.30210.88012.7071.12713.6531.321False111.9530.8277820.56577150.49417.4612321.093285143.5945.9729819.3690811.724203----0----------
103.7075147825871-26.69041559905940.0050.0040.02920662599771038208103.707514143740.1178-26.690409939880.16980.27060.2027-0.1330.2561.3140.34False586.6621.3629218.767395328.1787.9653119.061113440.7277.7102918.1514950.909618----0----------
103.6968408120309-26.70223904305570.0410.0270.02920662565411337856103.696825304950.8676-26.702241440821.63850.87712.0106-3.2181.729-0.5572.618False77.01560.87931620.97191827.95525.1797821.73523385.04644.6490819.937781.797453----0----------
103.6790946300216-26.69073738277320.0010.00.02920662634135133312103.679090639380.0127-26.690730027690.01730.89770.0197-0.8280.0271.7080.036False117095.034.323613.01701666021.165.594513.30218174663.166.076612.5791550.723026----06375.19----1.945.627
103.6851908449474-26.698903251670.0030.0030.02920662561116355072103.685211171020.0745-26.698879797550.10160.48310.11914.2180.1625.4470.216False1208.171.4990217.983042507.4896.9180518.587822995.4747.5411217.2668461.320976----0----------
103.6880062644454-26.70708653926150.0110.0080.02920662565411365376103.687993469070.2503-26.707075430120.34160.43130.4156-2.6550.5462.580.679False224.1740.74918119.81190391.55037.0432720.447239230.1385.2196318.8569491.59029----0----------
103.6772278913576-26.70028080921640.0120.010.02920662629835826048103.677228776980.298-26.700270900260.402-0.01660.49810.1840.6662.3010.766False196.7590.81468419.9535386.98396.3187520.50279175.0316.0104819.1541331.348658----0----------
103.6798534900421-26.70145612234880.0260.0250.02920662634133582720103.67985089180.6922-26.70145227940.9982.58481.265-0.5391.6370.8931.693False83.53430.76570720.88370340.44824.5254221.33413983.19155.4294319.9617211.372417----0----------
103.704266301682-26.69384921248990.0050.0030.02920662599773842688103.704266301682.763-26.693849212495.2851------------False60.11111.28521.240978------------------0----------
103.6697589867552-26.69517581975380.0010.0010.02920662634135140096103.669748147030.0295-26.69516238590.04030.3710.0467-2.2490.0633.120.085False6028.793.1658216.2377913102.5110.33716.6221074220.8212.483315.6984290.923677----05468.00.73580.371.481.774
103.68602363422-26.69444805923820.0030.0010.02920662634133440640103.686023634221.2184-26.694448059242.9684------------False74.31730.94905321.010641------------------0----------
103.6744209115503-26.69889091284540.0070.0050.02920662629835831168103.674416481490.1581-26.698885825970.22990.29410.2771-0.9190.3421.1810.472False397.5271.0167419.189949189.3476.8230419.658241289.8035.622618.6066631.051579----0----------
103.6926283315679-26.67689978371570.0050.0040.02920662664196999680103.692654981670.1139-26.676964690440.15461.90710.18715.5310.25-15.0750.305False694.91.2494518.58356161.4888.2450619.83104856.0358.0081517.430692.400349----0----------
103.6905363986536-26.70901596634160.0010.00.02920662565415663488103.690539234820.0148-26.709018557660.02060.54570.02390.5880.031-0.6020.043True33838.59.3635514.36483919052.421.016514.65151221805.818.920313.915490.736022----06223.91.36850.67131.814.411
103.6750143118302-26.6939367202770.0010.00.02920662634135136896103.6749972730.0139-26.693860214080.01991.33830.0229-3.5360.02917.7690.042False29583.98.0941714.51072514956.621.99814.91430521018.914.295313.9553950.95891----05420.330.1360.0660.930.673
103.6907087973092-26.69422317275460.0020.0010.02920662599775392384103.690726750920.0387-26.694210814260.0540.63910.06283.7250.0832.870.114False3875.072.2955216.7176691803.817.6085217.2109132963.1610.4116.0825311.128382----04980.00.28580.1450.870.416
103.6860670864347-26.69179954588570.0010.0010.02920662634135131264103.686060841650.0189-26.691784030780.02640.35010.0305-1.2960.043.6040.056False12932.64.9009315.4091496859.3810.80315.7606758886.9114.699614.8900420.870633----05733.66----2.074.179
103.6842737979447-26.70175033289470.0370.03490.02920662565413339904103.684280083291.2459-26.701749928071.3821-2.24391.73721.3042.3650.0942.175False79.56940.72890220.936546.52089.3006221.18227269.24219.3035320.1609941.021278----0----------
103.6820034780249-26.69377611442160.0220.0210.02920662634130817280103.681998157470.6459-26.693806949570.7993-0.88711.0528-1.1041.328-7.1621.417False102.5420.90593520.66111633.41793.9270121.541441129.8026.8725119.4787142.062727----0----------
103.7023633842059-26.6937781772540.010.0080.02920662595476109056103.702362597940.2279-26.693777676250.32590.70250.4055-0.1630.5040.1160.646False239.3630.95138419.740725130.0157.977820.066406185.2546.9600419.09250.973906----0----------
103.67736401159-26.68775432568180.0170.0140.02920662634130810880103.677360981570.3959-26.687732320110.57481.24920.7108-0.6290.9145.1111.077False128.4380.72035220.41663650.1198.0615121.101381155.455.9756719.2829421.81844----0----------
103.6534419859618-26.69791735460720.0110.0080.02920662732915053568103.653444123030.2405-26.69791218280.3592-0.24470.43370.4430.5271.2010.703False243.0330.8250819.724205122.1366.6141220.134275190.8785.073619.0600341.074242----0----------
103.6448602407325-26.68027591077450.020.0160.02920662805934420608103.644838417750.4784-26.680270198840.7468-0.4160.9312-4.5291.0411.3271.287False111.5820.89551620.56938457.48156.858220.95256895.24564.8175519.8148081.13776----0----------
103.6457054696464-26.69816993998630.0010.0010.02920662698555162368103.645686963190.0173-26.698161998350.02451.98230.0283-3.840.0381.8450.054False245087.031.134412.215064130678.0131.72112.560881163903.073.742111.7254560.83542471.721.12135776.330.8460.40651.572.463
103.6371030479543-26.70443158087540.0050.0040.02920662698555289472103.63709768910.1128-26.704426727340.15770.07610.1887-1.1120.2441.1270.321False618.6661.1957918.709726310.827.2285219.120115450.35.584918.1281660.991949----0----------
103.6456888116766-26.6934091252090.0120.010.02920662737210100992103.645688641960.3058-26.693399339440.42870.39290.5375-0.0350.6762.2730.788False182.5390.82820220.03497992.88265.1225420.431551160.2855.168819.249691.181862----0----------
103.6501059275592-26.69961486064570.010.0070.02920662698555304576103.650097762680.2151-26.699602838060.3260.42730.394-1.6940.4722.7920.621False249.1580.84363319.697176130.4778.0607720.062551218.4377.5064918.9136031.148949----0----------
103.6595666536828-26.69731612764130.0190.0160.02920662737210084608103.659544077340.4646-26.69729218080.71371.09840.8643-4.6851.015.5621.241False123.5890.80416120.45841442.83285.5390121.271946143.4626.2576119.370081.901865----0----------
103.6457432485343-26.70088499707630.0180.0130.02920662702850376192103.645738748720.3872-26.700867258670.5581-1.24080.6952-0.9340.8584.121.153False122.4590.70861720.46839377.66568.6669720.625816151.4227.9114319.311451.314367----0----------
103.6428952792929-26.69975849017980.0290.0210.02920662702850377344103.642895108250.6431-26.699745151281.05580.8841.2836-0.0351.3863.0981.845False85.14740.86489720.86293865.01857.2779620.818796100.9698.5174119.7514461.06735----0----------

Step 6: Cross-match Gaia and 2MASS sources in all fields#

We now want to find sources in the selected region (observed in the MASH regions of interest and at low extinction) that are common to the 2MASS and Gaia catalogs. To do so, we will perform a cross-match of the Gaia and 2MASS catalogs. Alternatively, we could use the CDS XMatch service via the corresponding astroquery module.

To do so, let’s first inspect the match_coordinates_sky function from astropy.coordinates.

help(match_coordinates_sky)
Help on function match_coordinates_sky in module astropy.coordinates.matching:

match_coordinates_sky(matchcoord, catalogcoord, nthneighbor=1, storekdtree='kdtree_sky')
    Finds the nearest on-sky matches of a coordinate or coordinates in
    a set of catalog coordinates.
    
    This finds the on-sky closest neighbor, which is only different from the
    3-dimensional match if ``distance`` is set in either ``matchcoord``
    or ``catalogcoord``.
    
    Parameters
    ----------
    matchcoord : `~astropy.coordinates.BaseCoordinateFrame` or `~astropy.coordinates.SkyCoord`
        The coordinate(s) to match to the catalog.
    catalogcoord : `~astropy.coordinates.BaseCoordinateFrame` or `~astropy.coordinates.SkyCoord`
        The base catalog in which to search for matches. Typically this will
        be a coordinate object that is an array (i.e.,
        ``catalogcoord.isscalar == False``)
    nthneighbor : int, optional
        Which closest neighbor to search for.  Typically ``1`` is desired here,
        as that is correct for matching one set of coordinates to another.
        The next likely use case is ``2``, for matching a coordinate catalog
        against *itself* (``1`` is inappropriate because each point will find
        itself as the closest match).
    storekdtree : bool or str, optional
        If a string, will store the KD-Tree used for the computation
        in the ``catalogcoord`` in ``catalogcoord.cache`` with the
        provided name.  This dramatically speeds up subsequent calls with the
        same catalog. If False, the KD-Tree is discarded after use.
    
    Returns
    -------
    idx : int array
        Indices into ``catalogcoord`` to get the matched points for each
        ``matchcoord``. Shape matches ``matchcoord``.
    sep2d : `~astropy.coordinates.Angle`
        The on-sky separation between the closest match for each
        ``matchcoord`` and the ``matchcoord``. Shape matches ``matchcoord``.
    dist3d : `~astropy.units.Quantity` ['length']
        The 3D distance between the closest match for each ``matchcoord`` and
        the ``matchcoord``. Shape matches ``matchcoord``.  If either
        ``matchcoord`` or ``catalogcoord`` don't have a distance, this is the 3D
        distance on the unit sphere, rather than a true distance.
    
    Notes
    -----
    This function requires `SciPy <https://www.scipy.org/>`_ to be installed
    or it will fail.
# We generate the coordinates in the appropriate format
twomass_coord = SkyCoord(ra=twomass["RAJ2000"], dec=twomass["DEJ2000"], unit=u.deg)
gaia_coord = SkyCoord(ra=gaia["ra_epoch2000"], dec=gaia["dec_epoch2000"], unit=u.deg)

index, separation_2d, _ = match_coordinates_sky(twomass_coord, gaia_coord)
# Decide the maximum separation between objects to be considered acceptable matches
max_separation = 1.0 * u.arcsec
# Apply constraint on the two catalogs
sep_constraint = separation_2d < max_separation
twomass_matches = twomass[sep_constraint]
gaia_matches = gaia[index[sep_constraint]]
# Select only interesting columns from twomass_matches
match_catalog = twomass_matches["_2MASS", "RAJ2000", "DEJ2000", "Hmag", "Kmag"]
# Add column G magnitude from gaia
match_catalog["Gmag"] = gaia_matches["phot_g_mean_mag"]
match_catalog
Table length=6376
_2MASSRAJ2000DEJ2000HmagKmagGmag
degdegmagmagmag
str17float64float64float32float32float64
06561231-2715514104.051312-27.26429615.53315.27416.924894
06561317-2715210104.05491-27.25584413.29213.05916.701544
06561481-2716053104.061712-27.26816213.33613.215.479486
06561479-2715417104.061646-27.26158513.23413.26614.229291
06561641-2715166104.068408-27.25462913.62513.54314.883485
06561656-2715126104.069033-27.25352715.88314.74718.727604
06561660-2715246104.069205-27.25684414.63114.45716.435612
06561532-2714537104.063862-27.24825315.215.0217.405537
06561543-2714325104.064318-27.24236314.23114.07515.734059
06561711-2714541104.071307-27.24838313.30713.16315.021435
06562032-2715025104.084702-27.25070614.33114.45715.809618
06562075-2714021104.086461-27.23392315.72816.28917.67484
06561726-2714384104.071925-27.24402413.13913.01414.705641
06561759-2713598104.073293-27.2332814.17713.87218.109772
06561894-2713384104.078949-27.22733916.10815.40318.604189
06561879-2714289104.078292-27.24137714.95114.82818.446154
06561060-2715111104.044183-27.25308416.29916.29317.89479
06561225-2714413104.05108-27.24480816.41416.01617.921455
06561125-2714498104.04688-27.2471879.8349.69711.854339
06561135-2714113104.047301-27.23648816.41914.37219.787733
06560974-2713092104.040599-27.21923414.01913.95815.416584
06561099-2714107104.045795-27.23630913.32613.1615.873589
06560717-2714071104.029884-27.23532515.91514.94818.844616
06561342-2712466104.055943-27.21294813.22213.15814.7209
06561218-2713314104.050776-27.22539515.98615.50618.21587
06561408-2712226104.058682-27.20628417.09616.87418.727905
06561494-2712042104.062284-27.20117215.92815.77618.452906
06561443-2712057104.060132-27.20159716.50516.44818.147007
06561453-2713473104.060581-27.22980916.23615.518.611937
06561833-2713238104.076384-27.22329113.96813.8115.312183
06561512-2712552104.063005-27.21534714.99614.77117.754915
06561592-2714018104.066348-27.23385816.52216.76718.27725
06561538-2712393104.064098-27.21092215.42215.16918.392714
06561585-2712419104.066047-27.21163915.26815.26916.786901
06561553-2712203104.064732-27.20566414.09213.915.185103
06561657-2712502104.069083-27.21395516.09916.94317.4553
06561926-2712497104.080262-27.21380616.30816.93217.962248
06562408-2712536104.100347-27.21489314.38514.38115.704457
..................
06545575-2640333103.732294-26.67593813.93413.78215.741429
06545577-2640583103.732402-26.68287715.49515.04918.186043
06545507-2640505103.729462-26.68071611.22811.06713.227829
06545831-2640090103.742994-26.66916716.35515.68518.128725
06545780-2640467103.740844-26.67964415.92115.06818.661951
06545765-2640113103.740249-26.66982313.78913.65716.623756
06550010-2640487103.750443-26.68021616.04615.73918.310627
06550006-2640340103.750286-26.67611315.13615.03916.940012
06550048-2641143103.752022-26.68731916.38315.69517.837942
06550040-2639348103.751686-26.65967815.41515.52716.990917
06550025-2639509103.75105-26.6641615.52215.29216.93812
06550504-2640232103.771012-26.67311715.64915.17418.920807
06550151-2641142103.756294-26.68728614.13914.12416.556156
06550292-2640074103.762204-26.6687315.59515.39517.554699
06550363-2640572103.765125-26.68255613.99513.87515.622164
06543817-2642130103.659062-26.70361316.00515.01619.36678
06543882-2642562103.66175-26.7156314.39114.23615.837558
06543881-2642487103.661746-26.71354114.47814.34915.86585
06543912-2643298103.663013-26.72495315.04514.93918.58063
06543942-2643080103.664291-26.71891615.41915.30716.771603
06544053-2643307103.668888-26.72522214.55214.37215.731012
06544016-2643313103.66737-26.7253815.50315.11117.339947
06544029-2642277103.667897-26.70770514.16914.22216.248013
06544164-2643157103.67353-26.72104315.46815.01316.942991
06544205-2643260103.675222-26.72389815.13415.05617.244776
06544330-2642414103.680432-26.71151715.8315.69116.959612
06544298-2641266103.67909-26.69073911.83711.80113.017016
06544364-2641518103.681834-26.69773915.81715.60816.517591
06544444-2641560103.685202-26.69889815.80115.70317.983042
06544200-2641381103.675002-26.69393912.98112.96114.510725
06544465-2641304103.686061-26.6917913.98313.89615.409149
06544074-2641426103.669757-26.69518314.71314.58816.237791
06544573-2642324103.690544-26.70901713.16213.10614.364839
06545001-2641286103.708399-26.69128814.66914.66216.041847
06544577-2641391103.690725-26.69420214.81114.7816.717669
06544623-2640367103.692647-26.67686815.17114.32718.58356
06543496-2641534103.645686-26.69817210.89510.812.215064
06560422-2639092104.017586-26.65256516.29515.7619.62056
06585741-2635059104.739224-26.58499315.50315.55818.368963

Step 7: Build a color-color diagram#

We now use the data we got from the cross-match to get a 2MASS/Gaia color-color diagram for all the sources in the low extinction sky regions covered by the MASH survey:

fig, ax = plt.subplots(figsize=(10, 8))
ax.plot(
    match_catalog["Hmag"] - match_catalog["Kmag"],
    match_catalog["Gmag"] - match_catalog["Hmag"],
    linestyle="",
    marker=".",
)
ax.set_xlabel("H - Ks [mag]", fontsize=16)
ax.set_ylabel("G - H [mag]", fontsize=16)
plt.show()
_images/0a0e01f3d8a607288293c50a2a2b0d7a9ad9883535b2a0a4b90338be41d8202f.png