The MASH planeraty nebulae catalog: Advanced usage of HiPS and MOCs to explore complex regions of interest

Stefania Amodea¹, Matthieu Baumann¹, Thomas Boch¹, Caroline Bot¹, Katarina A. Lutz¹, Manon Marchand¹.

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

This tutorial demonstrates an advanced usage of Hierarchical Progressive Surveys (HiPS) and Multi-Order Coverage (MOC) maps. Wis this tutorial, you will:

1. learn how to find fits files

2. build a map of the coverage of all the fits files

3. select only the parts of this map that correspond to low-extinction regions

4. retrieve objects from large catalogs inside the obtained map – that has a non-trivial shape and non-necessarily-connected regions

5. combine catalogs to visualize a 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.

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_name	table_name	table_type	description	utype	nrows
object	object	object	object	object	object
J_MNRAS	J/MNRAS/412/223/table4	table	The 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_MNRAS	J/MNRAS/412/223/table1	table	MASH PNe detected in the NVSS ( Bojicic I.S., Parker Q.A., Filipovic M.D., Frew D.J.)		201
V_combined	V/127A/mash2	table	The 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_combined	V/127A/mash1	table	The 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_MNRAS	J/MNRAS/412/223/mpgs2	table	MASH 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

recno	n_PNG	PNG	Name	RAJ2000	DEJ2000	GLon	GLat	MajDiam	MinDiam	CS	Morph	Tel	ObsDate	HaExp	HaFld	Comments	img	ImageHa	ImageSr	AssocData
				deg	deg	deg	deg	arcsec	arcsec				'Y:M:D'
int32	str1	object	object	float64	float64	float64	float64	float64	float64	object	object	object	float64	object	object	object	object	object	object	object
29	L	G234.7-02.2	PHR0724-2021	111.05458333333331	-20.36361111111111	234.7045	-2.2774	134.5	54.0		A	SA	2452672.0	HA18201	HA842	Large, very faint diffuse arcuate nebula; has [NII]~2xH-alpha, nothing in blue	1029	img_ha	img_sr	fits
2	P	G227.3-12.0	PHR0633-1808	98.35374999999999	-18.13972222222222	227.3207	-12.0289	17.0	15.0		Ea	SA	2452672.0	HA18191	HA926	Very faint, partial arcuate nebula also observed M1 060100; [NII]~0.8xH-alpha, strong [SII], only weak H-beta in blue - inconclusive	1002	img_ha	img_sr	fits
16	T	G227.2-03.4	PHR0705-1419	106.41041666666665	-14.318055555555553	227.2852	-3.4029	15.0	15.0		E	SA	2452668.0	HA18244	HA1017	Small, circular PN around a faint central star; also observed M1 040100; [NII]~0.7 H-alpha, [OIII]>>H-beta	1016	img_ha	img_sr	fits
5	L	G223.6-06.8	PHR0646-1235	101.60583333333332	-12.598888888888887	223.6338	-6.8035	40.0	37.0		E	SA	2453788.0	HA18194	HA1016	Slightly oval very faint PN candidate - has [OIII] and H-alpha	1005	img_ha	img_sr	fits
10	P	G224.3-05.5	PHR0652-1240	103.08458333333331	-12.67611111111111	224.3504	-5.5463	187.0	180.0		I	SA	2452670.0	HA18244	HA1017	Faint, extended S-shaped emission nebula, possible evolved PN, also observed M1 080100; has [NII]~0.8Ha, [OIII], strong [SII], [OIII]>H-beta	1010	img_ha	img_sr	fits

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_estsize	access_format	access_url	bib_reference	calib_level	dataproduct_type	em_band	em_max	em_min	extension	facility_name	has_wcs	instrument_name	obs_collection	obs_id	obs_publisher_did	oidsaada	o_ucd	pol_states	s_dec	s_fov	s_ra	s_region	s_resolution	target_name	t_exptime	t_max	t_min	t_resolution
kbyte						spect			meta.code		meta.code								deg	deg	deg		arcsec
int64	object	object	object	int32	object	object	float64	float64	int32	object	int32	object	object	object	object	object	object	object	float64	float64	float64	object	float64	object	float64	float64	float64	float64
610560	application/fits	https://cdsarc.cds.unistra.fr/saadavizier/download?oid=864975549779410945	2006MNRAS.373...79P	-1	image	Optical	1e-06	3e-07	--	UKST	5	SuperCOSMOS I	V/127A	1032_sr.fits	ivo://CDS.VizieR/V/127A?res=1032_sr.fits	864975549779410945	obs.image	NotSet	-13.784389854801203	0.0713021682865721	111.43275777455307	Polygon ICRS 111.46664940645094 -13.81781914701963 111.39832407190251 -13.817324215795937 111.3988758446347 -13.750955924906409 111.46718176614709 -13.751450706358609	0.948991575097523		--	--	51226.0	--
740160	application/fits	https://cdsarc.cds.unistra.fr/saadavizier/download?oid=864975549779410951	2006MNRAS.373...79P	-1	image	Optical	1e-06	3e-07	--	UKST	5	SuperCOSMOS I	V/127A	1034_ha.fits	ivo://CDS.VizieR/V/127A?res=1034_ha.fits	864975549779410951	obs.image	NotSet	-17.12049297622673	0.07123944496920925	111.78451610217952	Polygon ICRS 111.8191479048229 -17.153711083791936 111.74974303867411 -17.15359964722061 111.74989666803143 -17.087268981666405 111.8192767884711 -17.087380370020252	0.9485038734315203		--	--	52338.0	--
596160	application/fits	https://cdsarc.cds.unistra.fr/saadavizier/download?oid=864975549779410955	2006MNRAS.373...79P	-1	image	Optical	1e-06	3e-07	--	UKST	5	SuperCOSMOS I	V/127A	1034_sr.fits	ivo://CDS.VizieR/V/127A?res=1034_sr.fits	864975549779410955	obs.image	NotSet	-17.120533969278245	0.07124314880377468	111.7845314034197	Polygon ICRS 111.8191637002453 -17.153753988164176 111.74975722819107 -17.153640656320004 111.7499114759699 -17.08730806317958 111.81929320101966 -17.087421346614327	0.9485285228101485		--	--	50871.0	--
731520	application/fits	https://cdsarc.cds.unistra.fr/saadavizier/download?oid=864975549779410959	2006MNRAS.373...79P	-1	image	Optical	1e-06	3e-07	--	UKST	5	SuperCOSMOS I	V/127A	1036_ha.fits	ivo://CDS.VizieR/V/127A?res=1036_ha.fits	864975549779410959	obs.image	NotSet	-21.862099427980556	0.07130134708963763	112.69528726569132	Polygon ICRS 112.73146814532774 -21.89490077932626 112.65997554014551 -21.895645624610193 112.659123005937 -21.829290184523508 112.73058240266795 -21.828545713789918	0.9488088411876365		--	--	51163.0	--
590400	application/fits	https://cdsarc.cds.unistra.fr/saadavizier/download?oid=864975549779410960	2006MNRAS.373...79P	-1	image	Optical	1e-06	3e-07	--	UKST	5	SuperCOSMOS I	V/127A	1005_sr.fits	ivo://CDS.VizieR/V/127A?res=1005_sr.fits	864975549779410960	obs.image	NotSet	-12.59878194718445	0.06695119600443825	101.605805625331	Polygon ICRS 101.64022494465036 -12.631531553985315 101.57226287333897 -12.63235954506209 101.57139509987323 -12.56602794013158 101.63933959085792 -12.565200170324758	0.9485060738987898		--	--	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 could do:

image = fits.open(mash_fits['access_url'][0])

and then work on the image, plot it or save it to our machine. 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 either at http://astro.u-strasbg.fr/~bot/BochBot.tar.gz or 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)
# Make new directory where to save MOCs
outdir = Path("Data/Result_moc/")
outdir.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)
moc_mash.save(str(outdir / "mash_moc.fits"), overwrite=True)
# 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 vizualisation
moc_mash.display_preview()

# 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)
moc_mash.border(ax, wcs, color="purple")

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.

ext_map = fits.open(
    "https://lambda.gsfc.nasa.gov/data/foregrounds/SFD/lambda_sfd_ebv.fits",
)
ext_map.writeto(outdir / "Schlegel_extinction_map.fits", overwrite=True)
ext_map.close()

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.

# open the downloaded FITS file
hdul = fits.open(outdir / "Schlegel_extinction_map.fits")
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, 8))

with mocpy.WCS(
    fig,
    fov=200 * 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)
    lon.set_ticks(spacing=2 * u.hourangle)
plt.legend(bbox_to_anchor=(1, 1))

<matplotlib.legend.Legend at 0x7f422696bfd0>

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

RAJ2000	DEJ2000	_2MASS	Jmag	e_Jmag	Hmag	e_Hmag	Kmag	e_Kmag	Qflg	Rflg	Bflg	Cflg	Xflg	Aflg
deg	deg		mag	mag	mag	mag	mag	mag
float64	float64	str17	float32	float32	float32	float32	float32	float32	str3	str3	str3	str3	uint8	uint8
44.996055	0.005565	02595905+0000200	16.376	0.097	15.770	0.140	15.258	0.141	ABB	222	111	000	0	0
45.004857	0.019806	03000116+0001113	12.529	0.024	11.954	0.030	11.874	0.029	AAA	222	111	000	0	0
45.004193	0.020956	03000100+0001154	14.845	0.056	14.223	0.077	14.016	0.055	AAA	222	111	ccc	0	0
44.995074	0.038204	02595881+0002175	16.746	0.134	15.814	0.140	16.125	0.324	BBD	222	111	000	0	0
44.963851	0.043587	02595132+0002369	16.476	0.113	16.057	0.175	15.564	--	BCU	220	110	000	0	0
45.048281	0.048329	03001158+0002539	13.354	0.025	12.874	0.030	12.699	0.030	AAA	222	111	000	0	0
45.023564	0.068472	03000565+0004064	14.729	0.038	14.141	0.043	14.024	0.074	AAA	222	111	000	0	0
44.993307	0.076423	02595839+0004351	14.921	0.036	14.557	0.063	14.472	0.081	AAA	222	111	000	0	0
44.969132	0.084458	02595259+0005040	16.086	0.086	15.381	0.111	15.381	0.176	ABC	222	111	000	0	0
44.978474	0.092522	02595483+0005330	16.505	0.122	16.169	0.182	15.950	--	BCU	220	110	000	0	0
44.932798	0.064881	02594387+0003535	15.760	0.071	15.079	0.075	14.844	0.122	AAB	222	111	000	0	0
44.951159	0.105287	02594827+0006190	15.265	0.050	14.560	0.050	14.524	0.091	AAA	222	111	000	0	0
44.996008	0.085004	02595904+0005060	15.086	0.037	14.933	0.068	14.831	0.106	AAA	222	111	000	0	0
45.013776	0.087927	03000330+0005165	16.517	0.111	15.851	0.128	15.363	0.170	BBC	222	111	000	0	0
44.983064	0.096521	02595593+0005474	15.506	0.055	15.021	0.070	14.575	0.077	AAA	222	111	000	0	0
45.005238	0.102053	03000125+0006073	16.506	0.114	15.977	0.169	16.079	0.276	BCD	222	111	000	0	0
45.095502	0.085386	03002292+0005073	16.501	0.149	15.767	--	15.259	--	CUU	200	100	000	0	0
45.056971	0.115119	03001367+0006544	16.457	0.133	15.945	0.172	15.992	--	BCU	220	110	000	0	0
45.124119	0.136752	03002978+0008123	14.912	0.048	14.398	0.057	14.124	0.069	AAA	222	111	000	0	0
45.132108	0.137814	03003170+0008161	11.320	0.023	11.007	0.025	10.926	0.021	AAA	222	111	000	0	0
45.164661	0.154549	03003951+0009163	14.085	0.035	13.780	0.034	13.678	0.041	AAA	222	111	000	0	0
45.146326	0.160417	03003511+0009375	15.260	0.057	14.712	0.054	14.270	0.070	AAA	222	111	000	0	0
45.116587	0.144724	03002798+0008410	14.017	0.032	13.614	0.034	13.503	0.048	AAA	222	111	000	0	0
45.129806	0.147947	03003115+0008526	16.427	0.127	16.102	0.203	15.451	--	BCU	220	110	000	0	0
45.033997	0.121187	03000815+0007162	16.710	0.212	15.607	0.188	15.488	--	CCU	220	110	000	0	0
45.058113	0.127478	03001394+0007389	10.971	0.022	10.527	0.025	10.350	0.020	AAA	222	111	000	0	0
45.033680	0.128725	03000808+0007434	12.983	0.025	12.548	0.028	12.389	0.030	AAA	222	111	000	0	0
45.055885	0.146119	03001341+0008460	13.926	0.026	13.514	0.035	13.344	0.044	AAA	222	111	000	0	0
45.034468	0.143849	03000827+0008378	16.070	--	15.309	0.143	15.468	0.221	UBD	022	011	000	0	0
45.031813	0.144161	03000763+0008389	16.765	0.186	15.761	0.147	16.277	--	CBU	220	110	000	0	0
45.019146	0.163204	03000459+0009475	16.286	0.121	15.757	0.153	14.150	--	BBU	226	220	cc0	0	0
45.019877	0.163774	03000477+0009495	16.061	0.129	15.474	0.134	14.149	--	BBU	226	220	cc0	0	0
45.061511	0.170926	03001476+0010153	16.693	0.167	16.139	0.191	16.258	--	CCU	220	110	000	0	0
45.083689	0.169397	03002008+0010098	15.532	0.066	14.910	0.076	14.502	0.101	AAA	222	111	000	0	0
45.096926	0.181305	03002326+0010526	16.935	0.209	16.389	0.231	15.794	0.316	CDD	222	111	000	0	0
45.063462	0.195657	03001523+0011443	14.604	0.032	14.310	0.048	14.083	0.062	AAA	222	111	000	0	0
44.894843	0.114173	02593476+0006510	13.181	0.024	12.868	0.022	12.797	0.030	AAA	222	111	000	0	0
44.910272	0.114749	02593846+0006530	15.936	0.082	15.171	0.081	14.986	0.138	AAB	222	111	000	0	0
44.912784	0.124035	02593906+0007265	15.002	0.048	14.376	0.048	14.219	0.071	AAA	222	111	000	0	0
44.952950	0.133829	02594870+0008017	15.333	0.054	14.661	0.052	14.423	0.089	AAA	222	111	000	0	0
44.993502	0.149115	02595844+0008568	16.616	0.158	15.890	0.155	15.417	0.198	CBC	222	111	000	0	0
44.972790	0.155077	02595346+0009182	13.653	0.026	13.162	0.033	12.889	0.033	AAA	222	111	000	0	0
44.920840	0.146076	02594100+0008458	15.687	0.070	15.388	0.095	14.940	0.128	AAB	222	111	000	0	0
44.925787	0.149713	02594218+0008589	16.626	0.161	16.172	0.214	15.833	--	CCU	220	110	000	0	0
44.922898	0.151262	02594149+0009045	15.408	0.053	14.809	0.069	14.494	0.093	AAA	222	111	000	0	0
44.939749	0.158188	02594553+0009294	16.307	0.112	15.631	0.126	15.348	--	BBU	220	110	000	0	0
44.958972	0.152389	02595015+0009086	15.672	0.070	15.114	0.084	14.992	0.142	AAB	222	111	000	0	0
44.951407	0.156762	02594833+0009243	15.452	0.054	14.908	0.072	14.977	0.140	AAB	222	111	000	0	0
44.969944	0.163613	02595278+0009490	15.440	0.058	14.775	0.063	14.377	0.080	AAA	222	111	000	0	0
44.875396	0.118206	02593009+0007055	14.915	0.032	14.617	0.046	14.596	0.084	AAA	222	111	000	0	0

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_ICRS	e_RA_ICRS	DE_ICRS	e_DE_ICRS	Source	Plx	e_Plx	pmRA	e_pmRA	pmDE	e_pmDE	Dup	FG	e_FG	Gmag	e_Gmag	FBP	e_FBP	BPmag	e_BPmag	FRP	e_FRP	RPmag	e_RPmag	BP-RP	RV	e_RV	Teff	AG	E_BP-RP_	Rad	Lum
deg	mas	deg	mas		mas	mas	mas / yr	mas / yr	mas / yr	mas / yr				mag	mag			mag	mag			mag	mag	mag	km / s	km / s	K	mag	mag	solRad	solLum
float64	float64	float64	float64	int64	float64	float32	float64	float32	float64	float32	uint8	float32	float32	float64	float64	float32	float32	float64	float64	float32	float32	float64	float64	float64	float64	float32	float64	float32	float32	float32	float64
46.88872467987	0.7044	1.95252447799	0.7635	627683700657920	0.1652	0.9827	-2.429	1.910	-2.584	2.353	0	121.9	1.266	20.4731	0.0113	82.59	11.3	20.5591	0.1486	102.5	10.84	19.7353	0.1148	0.8238	--	--	--	--	--	--	--
46.89543608858	0.1120	1.95509789592	0.1039	627718060397184	0.6347	0.1245	5.630	0.250	-3.757	0.236	0	1932	2.594	17.4734	0.0015	851.8	14.23	18.0256	0.0181	1636	9.312	16.7271	0.0062	1.2985	--	--	--	--	--	--	--
46.88893354351	0.0385	1.96557179545	0.0347	627786779724800	0.5615	0.0433	4.819	0.085	-1.640	0.081	0	1.226e+04	6.304	15.4672	0.0006	6444	24.87	15.8285	0.0042	8441	11.52	14.9459	0.0015	0.8826	--	--	5759.67	0.4693	0.2390	1.25	1.537
46.79216402512	0.6978	1.91206921055	0.7941	627825434531840	3.7388	1.0379	10.875	1.371	2.697	1.441	0	170.1	1.928	20.1119	0.0123	41.74	14.16	21.3001	0.3685	237.3	3.846	18.8236	0.0176	2.4764	--	--	--	--	--	--	--
46.81064440767	1.3410	1.92194783350	1.3584	627855499483776	1.6316	1.6121	-10.814	2.789	1.766	2.462	0	206.2	2.58	19.9028	0.0136	53.79	8.522	21.0246	0.1720	262	7.919	18.7162	0.0328	2.3084	--	--	--	--	--	--	--
46.78317782221	0.4676	1.92238792363	0.5522	627889859081600	-0.5427	0.6857	1.440	0.901	-1.613	0.897	0	228.7	1.547	19.7902	0.0073	86.93	9.376	20.5034	0.1171	213	9.35	18.9410	0.0477	1.5624	--	--	--	--	--	--	--
46.79838188022	3.8717	1.92930852784	3.5521	627924218822272	--	--	--	--	--	--	0	227.4	2.943	19.7962	0.0140	273.8	14.06	19.2579	0.0558	784.2	13.71	17.5258	0.0190	1.7321	--	--	--	--	--	--	--
46.82128777929	0.0547	1.93170893137	0.0544	627962874076416	4.9092	0.0656	22.065	0.116	-39.525	0.108	0	7593	8.142	15.9873	0.0012	1984	37.9	17.1077	0.0207	9275	81.83	14.8436	0.0096	2.2641	--	--	3731.06	0.2187	0.1355	--	--
46.82110782195	0.1761	1.93222015313	0.1797	627962873479424	1.7140	0.2177	-2.257	0.360	-14.507	0.334	0	1435	5.466	17.7964	0.0041	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--
46.83550217871	0.0382	1.93340670665	0.0386	627997233814400	2.1751	0.0453	8.024	0.085	-4.669	0.087	0	1.586e+04	12.76	15.1876	0.0009	6328	17.87	15.8481	0.0031	1.374e+04	35.55	14.4170	0.0028	1.4311	--	--	4526.24	0.1555	0.0750	0.67	0.168
46.83189337869	0.1190	1.93782729086	0.1253	627992938300928	0.0409	0.1462	0.559	0.248	-1.967	0.234	0	1630	2.602	17.6580	0.0017	739	12.92	18.1797	0.0190	1299	15.73	16.9780	0.0131	1.2017	--	--	--	--	--	--	--
46.83094459766	0.1252	1.94410239574	0.1348	627992938301568	0.1795	0.1579	4.638	0.256	1.536	0.251	0	1432	2.244	17.7986	0.0017	696.2	12.02	18.2445	0.0187	1050	9.193	17.2091	0.0095	1.0355	--	--	--	--	--	--	--
46.79012978544	0.0544	1.93194646746	0.0535	628134672769536	-0.0666	0.0662	0.010	0.112	-2.002	0.106	0	6976	6.66	16.0794	0.0010	2669	20.26	16.7854	0.0082	6258	13.43	15.2709	0.0023	1.5145	--	--	4425.00	--	--	--	--
46.78440932248	0.2104	1.94201508917	0.2883	628130377254656	1.9800	0.2686	-5.184	0.405	-9.818	0.453	0	822.2	2.266	18.4010	0.0030	203.5	9.473	19.5799	0.0505	1022	12.1	17.2381	0.0129	2.3418	--	--	--	--	--	--	--
46.76907660968	0.0728	1.94077748698	0.0761	628169032508032	0.4920	0.0895	-0.705	0.151	-7.028	0.144	0	3950	4.198	16.6969	0.0012	1961	26.16	17.1202	0.0145	2912	28.82	16.1013	0.0107	1.0189	--	--	5188.86	0.3090	0.1670	--	--
46.76954789482	0.7374	1.94185577679	0.8707	628164738032640	0.6412	1.0454	1.009	1.411	-3.797	1.411	0	161.5	1.361	20.1680	0.0091	91.89	8.537	20.4433	0.1009	151.6	11.98	19.3099	0.0858	1.1334	--	--	--	--	--	--	--
46.76755048647	1.8743	1.94396772736	2.3932	628164738056064	--	--	--	--	--	--	0	79.03	1.6	20.9439	0.0220	12.62	7.009	22.5989	0.6031	117.2	12.48	19.5895	0.1156	3.0094	--	--	--	--	--	--	--
46.80398701052	17.7252	1.96098837293	5.4931	628336536755328	--	--	--	--	--	--	0	89.31	1.876	20.8111	0.0228	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--
46.85526675238	0.4102	1.95633385036	0.4818	628405255164672	0.9668	0.6120	1.723	0.825	7.543	0.892	0	292	1.655	19.5248	0.0062	94.61	18.33	20.4116	0.2104	372	18.91	18.3354	0.0552	2.0761	--	--	--	--	--	--	--
46.85641362349	0.1039	1.95774658465	0.0991	628409550710528	1.4363	0.1315	10.668	0.212	-11.681	0.212	0	3162	5.062	16.9385	0.0017	946.7	9.22	17.9109	0.0106	3314	18.9	15.9612	0.0062	1.9497	--	--	3776.25	1.4770	0.7090	0.80	0.117
46.82823739423	0.6898	1.95783750983	0.7941	628439615043328	0.0764	0.9518	1.319	1.429	0.753	1.474	0	171.7	1.389	20.1012	0.0088	64.14	13.31	20.8335	0.2253	211.6	20.04	18.9480	0.1028	1.8856	--	--	--	--	--	--	--
46.84481958685	0.4990	1.96401514218	0.5917	628473974643584	-0.5932	0.7508	1.444	0.906	0.304	0.913	0	240	1.791	19.7378	0.0081	98.55	12.7	20.3673	0.1399	215	14.34	18.9307	0.0724	1.4366	--	--	--	--	--	--	--
46.84744137751	0.8223	1.96966558238	0.9501	628478269545984	0.7327	1.1500	2.105	1.793	-3.924	1.865	0	142.4	1.295	20.3046	0.0099	63.34	15.15	20.8472	0.2598	198.1	15.01	19.0196	0.0823	1.8276	--	--	--	--	--	--	--
46.88321947333	2.3077	1.97397691517	2.3952	628546989662976	--	--	--	--	--	--	0	275	4.458	19.5899	0.0176	377.6	31.56	18.9087	0.0907	858	18.2	17.4282	0.0230	1.4805	--	--	--	--	--	--	--
46.86779750637	0.3052	1.98297910704	0.3620	628611413603712	-0.0950	0.4140	3.586	0.656	-13.912	0.714	0	376.3	2.027	19.2495	0.0058	70.44	10.15	20.7318	0.1565	477.4	9.207	18.0648	0.0209	2.6670	--	--	--	--	--	--	--
46.82136709382	0.2716	1.97753432551	0.2547	628718787715456	0.3053	0.3345	8.925	0.511	-7.186	0.466	0	2008	8.194	17.4312	0.0044	652.8	21.24	18.3144	0.0353	2216	19.28	16.3980	0.0094	1.9164	--	--	--	--	--	--	--
46.86163250809	1.6063	1.99457114760	2.4025	628817573128960	--	--	--	--	--	--	0	81.01	1.583	20.9170	0.0212	91.07	48.93	20.4529	0.5833	119.1	17.64	19.5723	0.1608	0.8806	--	--	--	--	--	--	--
46.86669419178	0.9368	1.99540743881	0.9178	628817572171008	3.3126	1.1136	18.443	1.988	-34.766	1.762	0	312.7	4.931	19.4506	0.0171	45.02	9.139	21.2179	0.2204	530.5	8.982	17.9502	0.0184	3.2677	--	--	--	--	--	--	--
46.83655472811	0.2901	2.00322145760	0.3066	628851931776256	0.4664	0.3927	0.596	0.549	-6.488	0.537	0	570.6	2.328	18.7976	0.0044	308.7	18.11	19.1274	0.0637	417.6	25.37	18.2099	0.0659	0.9175	--	--	--	--	--	--	--
46.67717928034	0.3401	1.88134367582	0.3132	628955010960384	2.1632	0.4107	6.219	0.717	-7.889	0.583	0	612.1	2.193	18.7213	0.0039	146.3	20.43	19.9384	0.1516	807.6	16.38	17.4939	0.0220	2.4446	--	--	--	--	--	--	--
46.67877489929	0.0763	1.88296231914	0.0686	628959306493952	1.9701	0.0886	8.034	0.171	2.173	0.152	0	4991	4.676	16.4428	0.0010	1597	21.19	17.3434	0.0144	5099	16.39	15.4931	0.0035	1.8503	--	--	4078.00	0.6807	0.3267	0.57	0.081
46.67967457314	0.1430	1.88760468101	0.1302	628955010962560	0.4566	0.1698	6.176	0.308	-4.351	0.253	0	1740	2.716	17.5869	0.0017	736.9	30.09	18.1829	0.0443	1515	12.98	16.8112	0.0093	1.3717	--	--	--	--	--	--	--
46.66329890083	0.2604	1.88144855562	0.2358	628993666232960	0.7329	0.3083	1.107	0.565	-6.818	0.461	0	1489	4.773	17.7564	0.0035	487.6	34.17	18.6313	0.0761	1694	14.01	16.6896	0.0090	1.9417	--	--	--	--	--	--	--
46.67236035375	0.0396	1.90037936590	0.0364	629023730307456	1.7232	0.0461	0.716	0.090	-10.097	0.083	0	1.536e+05	57.25	12.7222	0.0004	8.626e+04	117.1	13.0119	0.0015	9.924e+04	167.9	12.2702	0.0018	0.7417	--	--	6218.33	0.3073	0.1567	1.22	2.008
46.71011165811	0.1018	1.90257859208	0.0916	629096745481216	0.3530	0.1186	0.915	0.229	-3.995	0.204	0	3115	4.541	16.9547	0.0016	1665	28.22	17.2981	0.0184	2150	26.57	16.4310	0.0134	0.8671	--	--	5641.87	0.1630	0.0780	--	--
46.69656925122	0.1400	1.90277246190	0.1281	629092449919744	0.1864	0.1670	-2.229	0.303	-0.508	0.251	0	1785	3.243	17.5593	0.0020	899.8	68.59	17.9661	0.0828	1435	17.21	16.8700	0.0130	1.0961	--	--	--	--	--	--	--
46.66739301780	1.4358	1.90989411092	1.3438	629332969144320	--	--	--	--	--	--	0	125.5	1.863	20.4420	0.0161	59.29	10.35	20.9189	0.1896	177.8	8.145	19.1374	0.0498	1.7815	--	--	--	--	--	--	--
46.67226138428	1.0150	1.91702737166	0.9507	629332968232960	4.0115	1.2070	-5.870	2.137	2.248	1.790	0	156.9	1.953	20.1991	0.0135	76.93	4.605	20.6362	0.0650	222	10.48	18.8959	0.0513	1.7403	--	--	--	--	--	--	--
46.65847399730	0.1262	1.91357443113	0.1333	629401687566720	0.5596	0.1684	8.064	0.280	-1.352	0.240	0	2289	3.441	17.2892	0.0016	1003	41.44	17.8478	0.0448	1869	7.673	16.5828	0.0045	1.2651	--	--	--	--	--	--	--
46.72514773388	0.1490	1.92210886522	0.0976	629509062306048	1.1755	0.1365	8.140	0.384	10.430	0.263	0	3500	4.563	16.8283	0.0014	1404	24.22	17.4827	0.0187	3104	15.55	16.0321	0.0054	1.4506	--	--	4465.27	0.1430	0.0782	0.60	0.130
46.70215641928	0.8670	1.92181228338	0.7930	629539126663936	0.5740	1.0874	8.695	1.724	-7.387	1.351	0	204.4	1.912	19.9121	0.0102	55.81	5.782	20.9845	0.1125	284.3	17.01	18.6276	0.0650	2.3569	--	--	--	--	--	--	--
46.71636476335	0.5045	1.92870855406	0.3123	629573486263552	0.0766	0.5427	4.761	1.170	-4.982	1.002	0	617	3.369	18.7127	0.0059	305	20.49	19.1407	0.0730	498.4	13.17	18.0180	0.0287	1.1227	--	--	--	--	--	--	--
46.69589132374	2.1296	1.93929708059	1.4561	629779644835200	--	--	--	--	--	--	0	120.6	1.539	20.4851	0.0139	37.68	8.43	21.4110	0.2429	159.7	11.55	19.2538	0.0785	2.1572	--	--	--	--	--	--	--
46.68213260008	1.7418	1.93367863457	1.4907	629818299986560	2.2015	2.0225	15.493	3.649	2.533	3.939	0	112.9	1.518	20.5562	0.0146	44.49	9.278	21.2307	0.2264	149.6	10.58	19.3245	0.0768	1.9062	--	--	--	--	--	--	--
46.70537144783	1.5321	1.95005005021	3.2442	629882724966784	--	--	--	--	--	--	0	101.2	1.649	20.6748	0.0177	27.41	0.7538	21.7566	0.0299	132.9	0.8743	19.4535	0.0071	2.3032	--	--	--	--	--	--	--
46.73026173839	1.0056	1.95831283431	1.1484	629917084710528	-1.0768	1.4419	14.469	2.531	-8.066	3.178	0	111.1	1.546	20.5737	0.0151	33.57	9.032	21.5366	0.2921	161.1	11.71	19.2438	0.0789	2.2928	--	--	--	--	--	--	--
46.72450426920	0.7176	1.96400254954	0.8743	629921378570112	1.5177	1.0542	-7.503	2.077	-9.066	2.710	0	141.8	1.468	20.3089	0.0112	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--	--
46.72448488485	0.3825	1.96428076301	0.4569	629917083655168	1.1694	0.5520	-5.346	0.761	-7.833	0.754	0	330.3	1.44	19.3911	0.0047	119.5	12.83	20.1581	0.1166	502.4	17.05	18.0093	0.0369	2.1489	--	--	--	--	--	--	--
46.71509484435	1.0711	1.96287985966	1.0777	629985803131520	-1.5166	1.1996	2.450	2.902	-2.309	3.199	0	141.9	1.497	20.3083	0.0115	83.76	13.58	20.5438	0.1761	101.5	10.94	19.7456	0.1170	0.7982	--	--	--	--	--	--	--
46.62036681142	0.5242	1.91034218617	0.4184	630020162856704	-0.5433	0.5814	1.287	1.072	-2.611	0.844	0	356.1	2.136	19.3094	0.0065	163.8	13.88	19.8157	0.0920	261.7	14.5	18.7174	0.0601	1.0982	--	--	--	--	--	--	--

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

_2MASS	RAJ2000	DEJ2000	errHalfMaj	errHalfMin	errPosAng	Jmag	Hmag	Kmag	e_Jmag	e_Hmag	e_Kmag	Qfl	Rfl	X	MeasureJD
	deg	deg	arcsec	arcsec	deg	mag	mag	mag	mag	mag	mag				d
str17	float64	float64	float32	float32	float32	float32	float32	float32	float32	float32	float32	str3	int16	uint8	float64
06561231-2715514	104.051312	-27.264296	0.11	0.11	45.0	15.816	15.533	15.274	0.068	0.104	0.192	AAC	222	0	2451203.5942
06561317-2715210	104.05491	-27.255844	0.06	0.06	90.0	13.891	13.292	13.059	0.026	0.033	0.034	AAA	222	0	2451203.5942
06561481-2716053	104.061712	-27.268162	0.06	0.06	90.0	13.857	13.336	13.2	0.026	0.022	0.039	AAA	222	0	2451203.5942
06561479-2715417	104.061646	-27.261585	0.06	0.06	90.0	13.43	13.234	13.266	0.023	0.022	0.041	AAA	222	0	2451203.5942
06561641-2715166	104.068408	-27.254629	0.06	0.06	45.0	13.843	13.625	13.543	0.029	0.038	0.045	AAA	222	0	2451203.5942
06561656-2715126	104.069033	-27.253527	0.16	0.15	75.0	14.947	15.883	14.747	--	0.137	--	UBU	20	0	2451203.5942
06561660-2715246	104.069205	-27.256844	0.06	0.06	90.0	15.04	14.631	14.457	0.052	0.068	0.092	AAA	222	0	2451203.5942
06561532-2714537	104.063862	-27.248253	0.08	0.08	135.0	15.698	15.2	15.02	0.053	0.07	0.143	AAB	222	0	2451203.5942
06561543-2714325	104.064318	-27.242363	0.06	0.06	45.0	14.627	14.231	14.075	0.037	0.042	0.068	AAA	222	0	2451203.5942
06561711-2714541	104.071307	-27.248383	0.06	0.06	90.0	13.67	13.307	13.163	0.026	0.025	0.029	AAA	222	0	2451203.5942
06562032-2715025	104.084702	-27.250706	0.07	0.06	4.0	14.724	14.331	14.457	0.029	0.041	0.101	AAA	222	0	2451203.5942
06562075-2714021	104.086461	-27.233923	0.13	0.13	45.0	16.484	15.728	16.289	0.105	0.107	--	AAU	220	0	2451203.5942
06561726-2714384	104.071925	-27.244024	0.06	0.06	90.0	13.481	13.139	13.014	0.023	0.027	0.033	AAA	222	0	2451203.5942
06561759-2713598	104.073293	-27.23328	0.06	0.06	90.0	14.763	14.177	13.872	0.032	0.039	0.056	AAA	222	0	2451203.5942
06561894-2713384	104.078949	-27.227339	0.25	0.23	81.0	16.528	16.108	15.403	0.118	0.149	0.229	BBD	222	0	2451203.5942
06561879-2714289	104.078292	-27.241377	0.07	0.07	45.0	15.641	14.951	14.828	0.054	0.057	0.113	AAB	222	0	2451203.5942
06561060-2715111	104.044183	-27.253084	0.2	0.18	68.0	16.703	16.299	16.293	0.129	0.16	--	BCU	220	0	2451203.5942
06561225-2714413	104.05108	-27.244808	0.22	0.2	70.0	16.707	16.414	16.016	0.12	0.179	--	BCU	220	0	2451203.5942
06561125-2714498	104.04688	-27.247187	0.06	0.06	90.0	10.323	9.834	9.697	0.023	0.022	0.023	AAA	222	0	2451203.5942
06561135-2714113	104.047301	-27.236488	0.23	0.21	75.0	16.791	16.419	14.372	0.131	0.195	--	BCU	220	0	2451203.5942
06560523-2714195	104.021804	-27.238752	0.29	0.27	135.0	17.289	16.163	15.769	0.205	0.174	0.259	DCD	222	0	2451203.5942
06560974-2713092	104.040599	-27.219234	0.06	0.06	90.0	14.287	14.019	13.958	0.026	0.025	0.065	AAA	222	0	2451203.5942
06561099-2714107	104.045795	-27.236309	0.06	0.06	90.0	14.11	13.326	13.16	0.033	0.033	0.03	AAA	222	0	2451203.5942
06560717-2714071	104.029884	-27.235325	0.15	0.14	82.0	16.647	15.915	14.948	0.123	0.122	--	BBU	220	0	2451203.5942
06561342-2712466	104.055943	-27.212948	0.06	0.06	90.0	13.51	13.222	13.158	0.028	0.025	0.029	AAA	222	0	2451203.5942
06561218-2713314	104.050776	-27.225395	0.27	0.23	93.0	16.617	15.986	15.506	0.114	0.14	0.201	BBD	222	0	2451203.5942
06561408-2712226	104.058682	-27.206284	0.16	0.15	75.0	16.813	17.096	16.874	0.136	--	--	BUU	200	0	2451203.5942
06561494-2712042	104.062284	-27.201172	0.18	0.18	45.0	16.869	15.928	15.776	0.161	--	--	CUU	200	0	2451203.5942
06561443-2712057	104.060132	-27.201597	0.18	0.16	7.0	16.967	16.505	16.448	0.147	--	--	CUU	200	0	2451203.5942
06561453-2713473	104.060581	-27.229809	0.19	0.19	45.0	16.731	16.236	15.5	0.136	0.176	--	BCU	220	0	2451203.5942
06561833-2713238	104.076384	-27.223291	0.06	0.06	90.0	14.207	13.968	13.81	0.026	0.038	0.042	AAA	222	0	2451203.5942
06561512-2712552	104.063005	-27.215347	0.07	0.07	45.0	15.594	14.996	14.771	0.053	0.055	0.105	AAB	222	0	2451203.5942
06561592-2714018	104.066348	-27.233858	0.23	0.21	45.0	16.782	16.522	16.767	0.142	0.211	--	BDU	220	0	2451203.5942
06561538-2712393	104.064098	-27.210922	0.11	0.11	45.0	16.06	15.422	15.169	0.079	0.08	--	AAU	220	0	2451203.5942
06561585-2712419	104.066047	-27.211639	0.08	0.08	45.0	15.493	15.268	15.269	0.051	0.075	0.184	AAC	222	0	2451203.5942
06561553-2712203	104.064732	-27.205664	0.06	0.06	90.0	14.299	14.092	13.9	0.024	0.031	0.06	AAA	222	0	2451203.5942
06561657-2712502	104.069083	-27.213955	0.17	0.15	103.0	16.456	16.099	16.943	0.107	0.151	--	ABU	220	0	2451203.5942
06561926-2712497	104.080262	-27.213806	0.2	0.19	12.0	16.597	16.308	16.932	0.128	0.178	--	BCU	220	0	2451203.5942
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
06564615-2603043	104.192326	-26.051208	0.06	0.06	45.0	14.271	13.999	13.81	0.039	0.055	0.055	AAA	222	0	2451296.4891
06564793-2602151	104.199743	-26.037531	0.21	0.21	45.0	16.294	15.834	16.822	0.136	0.195	--	BCU	220	0	2451296.4891
06564755-2603050	104.198164	-26.051392	0.08	0.08	90.0	15.646	15.194	14.889	0.076	0.111	0.142	ABB	222	0	2451296.4891
06565210-2602344	104.217123	-26.042894	0.06	0.06	90.0	12.119	11.904	11.86	0.024	0.023	0.024	AAA	222	0	2451296.4891
06565221-2601084	104.217559	-26.019003	0.07	0.06	86.0	14.63	14.375	14.276	0.043	0.049	0.082	AAA	222	0	2451296.4892
06565248-2600433	104.218697	-26.012047	0.08	0.08	45.0	15.276	15.135	15.207	0.066	0.109	0.174	ABC	222	0	2451296.4892
06565239-2602257	104.218325	-26.040495	0.18	0.18	45.0	16.32	15.605	16.756	0.156	0.164	--	CCU	220	0	2451296.4891
06565253-2600349	104.218898	-26.009703	0.09	0.08	3.0	15.337	14.999	15.101	0.065	0.119	0.171	ABC	222	0	2451296.4892
06565259-2601557	104.219136	-26.032164	0.24	0.2	3.0	16.516	15.65	15.22	0.17	0.173	0.193	CCC	222	0	2451296.4892
06565291-2601264	104.220475	-26.024015	0.06	0.06	90.0	14.511	14.09	13.878	0.041	0.031	0.064	AAA	222	0	2451296.4892
06565329-2602237	104.222078	-26.039919	0.26	0.25	16.0	16.586	15.848	15.33	0.187	0.21	0.216	CCC	222	0	2451296.4891
06564879-2601431	104.203331	-26.028662	0.26	0.25	4.0	16.414	16.079	15.95	0.152	0.255	--	CDU	220	0	2451296.4892
06565135-2602016	104.213961	-26.033804	0.07	0.07	45.0	15.325	14.922	14.811	0.061	0.089	0.129	AAB	222	0	2451296.4892
06565082-2601367	104.211777	-26.026865	0.16	0.15	173.0	16.339	16.211	16.808	0.136	--	--	BUU	200	0	2451296.4892
06564847-2601561	104.201987	-26.032253	0.33	0.28	5.0	16.418	15.917	15.605	0.146	0.233	0.265	CDD	222	0	2451296.4892
06565344-2601095	104.222672	-26.019329	0.29	0.28	4.0	16.525	15.841	15.413	0.164	0.204	0.241	CCD	222	0	2451296.4892
06565559-2601476	104.231634	-26.029894	0.14	0.14	135.0	16.168	15.701	15.045	0.124	--	--	BUU	200	0	2451296.4892
06565181-2602205	104.215894	-26.039036	0.29	0.25	89.0	16.043	16.052	15.436	0.118	0.25	0.238	BDD	222	0	2451296.4891
06564064-2600480	104.169345	-26.013355	0.06	0.06	45.0	13.917	13.576	13.445	0.034	0.042	0.05	AAA	222	0	2451296.4892
06563772-2601504	104.157168	-26.030691	0.06	0.06	90.0	13.04	12.747	12.731	0.026	0.029	0.03	AAA	222	0	2451296.4892
06564366-2601334	104.181941	-26.025957	0.4	0.31	87.0	16.5	16.246	15.65	0.171	0.282	0.285	CDD	222	0	2451296.4892
06564480-2601185	104.186708	-26.021814	0.06	0.06	45.0	14.325	14.039	14.033	0.032	0.025	0.075	AAA	222	0	2451296.4892
06564158-2602443	104.173252	-26.045652	0.09	0.09	90.0	15.326	15.154	15.078	0.069	0.119	0.168	ABC	222	0	2451296.4891
06563927-2601318	104.163652	-26.025518	0.06	0.06	45.0	13.964	13.532	13.366	0.032	0.022	0.044	AAA	222	0	2451296.4892
06564043-2601234	104.168487	-26.023172	0.08	0.07	90.0	15.666	15.255	14.681	0.083	0.118	0.115	ABB	222	0	2451296.4892
06564038-2601038	104.168278	-26.017733	0.07	0.07	45.0	15.177	14.868	14.944	0.051	0.096	0.142	AAB	222	0	2451296.4892
06564040-2601515	104.168335	-26.030979	0.21	0.18	82.0	16.176	15.644	15.08	0.124	0.179	0.168	BCC	222	0	2451296.4892
06564539-2601032	104.18915	-26.017569	0.28	0.27	176.0	16.247	15.531	15.52	0.114	0.161	0.247	BCD	222	0	2451296.4892
06564602-2600142	104.191783	-26.003965	0.07	0.07	45.0	15.012	14.615	14.541	0.052	0.072	0.103	AAA	222	0	2451296.4892
06564554-2600421	104.189787	-26.011698	0.06	0.06	45.0	14.968	14.482	14.039	0.048	0.058	0.074	AAA	222	0	2451296.4892
06564981-2600284	104.207569	-26.007895	0.08	0.08	45.0	15.828	15.263	14.8	0.083	0.133	0.143	ABB	222	0	2451296.4892
06564783-2600242	104.199297	-26.006746	0.12	0.11	10.0	15.928	15.543	15.242	0.098	0.149	0.184	ACC	222	0	2451296.4892
06564644-2600212	104.193517	-26.005903	0.07	0.06	90.0	15.132	14.395	14.224	0.056	0.074	0.084	AAA	222	0	2451296.4892
06564711-2559578	104.196329	-25.999401	0.28	0.26	81.0	16.152	15.523	15.484	0.108	0.147	0.255	BCD	222	0	2451296.4892
06564864-2559555	104.202699	-25.99877	0.17	0.16	41.0	16.357	16.136	16.915	0.133	--	--	BUU	200	0	2451296.4892
06564676-2559428	104.19487	-25.995239	0.07	0.06	0.0	13.579	13.198	13.132	0.032	0.031	0.034	AAA	222	0	2451296.4892
06564535-2559589	104.188986	-25.999701	0.09	0.09	0.0	15.802	15.266	15.144	0.082	0.127	0.174	ABC	222	0	2451296.4892
06565753-2601231	104.239722	-26.023087	0.06	0.06	45.0	13.026	12.932	12.944	0.026	0.029	0.033	AAA	222	0	2451296.4892
06565979-2601016	104.249134	-26.017115	0.17	0.16	135.0	16.589	15.934	16.926	0.137	0.143	--	BBU	220	0	2451208.533

gaia = moc_intersection.query_vizier_table("I/345/gaia2", max_rows=20000)
gaia

Table length=20000

ra_epoch2000	dec_epoch2000	errHalfMaj	errHalfMin	errPosAng	source_id	ra	ra_error	dec	dec_error	parallax	parallax_error	pmra	pmra_error	pmdec	pmdec_error	duplicated_source	phot_g_mean_flux	phot_g_mean_flux_error	phot_g_mean_mag	phot_bp_mean_flux	phot_bp_mean_flux_error	phot_bp_mean_mag	phot_rp_mean_flux	phot_rp_mean_flux_error	phot_rp_mean_mag	bp_rp	radial_velocity	radial_velocity_error	rv_nb_transits	teff_val	a_g_val	e_bp_min_rp_val	radius_val	lum_val
deg	deg	arcsec	arcsec	deg		deg	mas	deg	mas	mas	mas	mas / yr	mas / yr	mas / yr	mas / yr		e-/s	e-/s	mag	e-/s	e-/s	mag	e-/s	e-/s	mag	mag	km / s	km / s		K	mag	mag	Rsun	Lsun
float64	float64	float32	float32	float32	int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	bool	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int16	float64	float32	float32	float64	float64
104.0642397349738	-27.2482245363508	0.009	0.002	0.0	2920584465727934848	104.06423973497	2.314	-27.24822453635	8.5789	--	--	--	--	--	--	False	80.4691	1.21343	20.924294	--	--	--	--	--	--	--	--	--	0	--	--	--	--	--
104.0613350962615	-27.2457120515062	0.02	0.017	0.0	2920584465727934720	104.06133865504	0.4795	-27.24572610529	0.7266	2.7925	0.9319	0.735	1.066	-3.264	1.314	False	110.232	0.811783	20.582594	29.761	6.19097	21.667269	127.117	5.50348	19.50141	2.165859	--	--	0	--	--	--	--	--
104.0616994142013	-27.268162501078	0.001	0.001	0.0	2920584362651704192	104.06176472191	0.0195	-27.26826983064	0.0282	1.5899	0.0331	13.483	0.044	-24.928	0.058	False	12121.4	4.76419	15.479486	5099.4	12.5228	16.08259	10058.7	14.8426	14.755564	1.327026	--	--	0	4984.0	0.309	0.1495	0.61	0.21
104.0638238272635	-27.2482586806547	0.002	0.002	0.0	2920584461429778816	104.06382329382	0.0521	-27.24823315928	0.0765	0.6544	0.0872	-0.11	0.121	5.928	0.159	False	2056.52	1.79544	17.405537	875.686	6.96038	17.995518	1741.36	7.67767	16.659698	1.335819	--	--	0	--	--	--	--	--
104.0534300495002	-27.2500426156094	0.014	0.011	0.0	2920584427070040064	104.05342677462	0.3271	-27.25003658704	0.5302	0.8947	0.6557	-0.676	0.732	1.4	0.92	False	162.774	0.812274	20.159405	93.9469	11.4891	20.419182	132.283	6.62032	19.458162	0.96102	--	--	0	--	--	--	--	--
104.0751267092824	-27.2497140040521	0.007	0.006	0.0	2920584392710301952	104.07512382264	0.1708	-27.24969690641	0.2387	-0.2191	0.2883	-0.596	0.38	3.971	0.463	False	354.083	0.968096	19.315603	194.984	4.1286	19.626392	251.52	5.09448	18.760487	0.865906	--	--	0	--	--	--	--	--
104.0742449208677	-27.2644027644464	0.009	0.008	0.0	2920584392710299648	104.07423733605	0.2424	-27.26439208515	0.3022	0.1011	0.3706	-1.566	0.531	2.48	0.558	False	249.17	0.921325	19.697124	112.126	4.99648	20.227123	190.76	5.88838	19.060701	1.166422	--	--	0	--	--	--	--	--
104.0773944731844	-27.2633504544413	0.008	0.007	0.0	2920584392710299520	104.07739347867	0.2151	-27.26333600118	0.2962	-0.3964	0.3634	-0.205	0.474	3.357	0.546	False	270.933	0.898977	19.60621	116.801	5.97621	20.18277	216.387	5.2083	18.923845	1.258925	--	--	0	--	--	--	--	--
104.0570207631639	-27.2646632110967	0.012	0.01	0.0	2920584358350560512	104.05701812772	0.2794	-27.26464198072	0.3739	0.1062	0.4371	-0.544	0.629	4.931	0.787	False	224.061	0.805978	19.81245	88.3315	4.79911	20.4861	185.474	5.74906	19.091215	1.394884	--	--	0	--	--	--	--	--
104.067647998646	-27.2654819544346	0.011	0.009	0.0	2920584358350560384	104.0676432926	0.2658	-27.26547922509	0.3692	-0.6521	0.4458	-0.972	0.591	0.634	0.712	False	197.516	0.774971	19.949358	89.6643	6.05208	20.46984	147.016	4.92361	19.343506	1.126333	--	--	0	--	--	--	--	--
104.0625613443571	-27.2779499139245	0.005	0.004	0.0	2920584358350560128	104.0625464304	0.1315	-27.27793684683	0.1714	0.5521	0.2058	-3.079	0.286	3.035	0.329	False	570.942	1.29964	18.796886	282.403	4.74613	19.224215	415.634	5.65279	18.215143	1.009071	--	--	0	--	--	--	--	--
104.0524334477795	-27.2528825507896	0.016	0.013	0.0	2920584431371234688	104.05243706112	0.3667	-27.25286659556	0.6065	0.7646	0.7255	0.746	0.833	3.706	1.035	False	148.767	0.914238	20.257097	46.5355	6.28794	21.181927	179.523	5.8407	19.126621	2.055306	--	--	0	--	--	--	--	--
104.0517864137349	-27.2664989328045	0.004	0.003	0.0	2920584431368206208	104.05178695732	0.0912	-27.26648745784	0.1318	0.1051	0.1589	0.112	0.212	2.665	0.267	False	924.077	1.32833	18.274096	496.688	5.43714	18.611177	629.128	6.64563	17.765072	0.846106	--	--	0	--	--	--	--	--
104.0739324439192	-27.2629247691902	0.015	0.013	0.0	2920584397008462464	104.07392693656	0.3872	-27.26296314773	0.5461	-0.0157	0.6756	-1.137	0.852	-8.914	0.973	False	123.527	0.704263	20.458958	30.111	4.08243	21.654573	154.13	6.12704	19.292204	2.36237	--	--	0	--	--	--	--	--
104.0642873821425	-27.2423646129504	0.001	0.001	0.0	2920584465730915200	104.06426477003	0.0212	-27.24237675532	0.0296	0.8557	0.0337	-4.669	0.047	-2.82	0.058	False	9587.88	3.80374	15.734059	4941.82	11.9894	16.11667	6699.09	10.7669	15.19688	0.91979	--	--	0	5420.5	0.1252	0.057	0.83	0.533
104.0712892584117	-27.2483848349206	0.001	0.001	0.0	2920584465730915456	104.07129684322	0.0153	-27.24838619322	0.0222	0.4509	0.025	1.566	0.034	-0.315	0.044	False	18483.0	5.21679	15.021435	8930.62	12.2594	15.474185	13774.6	11.3762	14.41422	1.059965	--	--	0	5456.5	--	--	2.15	3.689
104.0684067796523	-27.2546449355025	0.001	0.001	0.0	2920584397011440128	104.06839947811	0.0148	-27.25461716535	0.0217	0.3348	0.025	-1.508	0.034	6.45	0.042	False	20987.1	6.38412	14.883485	11622.4	21.7302	15.188146	13850.6	19.1783	14.408245	0.779901	--	--	0	6071.84	--	--	2.44	7.297
104.0512108539907	-27.2501931951852	0.029	0.021	0.0	2920584431368429056	104.05121204913	0.6006	-27.250184356	1.0664	1.7514	1.2103	0.247	1.361	2.053	1.856	False	94.8772	0.801389	20.745462	54.2802	7.73501	21.014784	81.2637	7.76265	19.987179	1.027605	--	--	0	--	--	--	--	--
104.0527566427982	-27.2663254436633	0.009	0.009	0.0	2920584431368206080	104.05275606153	0.2352	-27.26631374214	0.295	0.2603	0.3544	-0.12	0.551	2.718	0.594	False	278.138	1.02748	19.577713	129.961	6.42842	20.066856	240.14	7.18967	18.81076	1.256096	--	--	0	--	--	--	--	--
104.0569364956352	-27.2534324135942	0.002	0.002	90.0	2920584431368428544	104.05693649564	1.5651	-27.25343241359	1.5649	--	--	--	--	--	--	False	64.6002	1.06568	21.16278	--	--	--	--	--	--	--	--	--	0	--	--	--	--	--
104.0577788453587	-27.2439038656642	0.037	0.028	0.0	2920584465728845952	104.05779220123	0.847	-27.24388451487	1.5605	1.6372	1.8678	2.758	1.809	4.494	2.403	False	77.3362	1.02244	20.96741	34.2182	5.78378	21.515747	66.4354	8.86624	20.205921	1.309826	--	--	0	--	--	--	--	--
104.0625240059008	-27.2508516850574	0.004	0.002	0.0	2920584465728845184	104.0625240059	1.864	-27.25085168506	3.8963	--	--	--	--	--	--	False	80.7649	1.11109	20.92031	42.4877	5.2812	21.28073	67.5039	6.10655	20.188599	1.092131	--	--	0	--	--	--	--	--
104.0690267560499	-27.2534812692429	0.006	0.005	0.0	2920584392712980352	104.06903101882	0.1284	-27.25346477756	0.1836	-0.2449	0.2247	0.88	0.317	3.83	0.381	False	608.562	1.31047	18.727604	274.945	7.05492	19.253273	561.678	8.34217	17.888203	1.36507	--	--	0	--	--	--	--	--
104.0513121237182	-27.2642858559867	0.002	0.001	0.0	2920584431371181056	104.0513054898	0.0424	-27.2642733177	0.0585	0.1092	0.0674	-1.37	0.097	2.912	0.12	False	3201.76	2.25261	16.924894	1755.26	7.95969	17.240536	2139.61	8.12078	16.436087	0.804449	--	--	0	5904.33	--	--	--	--
104.0553448156352	-27.258112539025	0.008	0.007	0.0	2920584431371180544	104.05532392247	0.1939	-27.25809725418	0.28	0.9508	0.3348	-4.314	0.435	3.55	0.528	False	302.596	0.932361	19.486208	87.0399	5.20258	20.502092	315.217	6.35911	18.515394	1.986698	--	--	0	--	--	--	--	--
104.061620479337	-27.2615814807265	0.001	0.0	0.0	2920584362651703552	104.06160134221	0.0127	-27.26154703478	0.0194	0.5815	0.0219	-3.951	0.029	8.0	0.04	True	38338.0	8.27732	14.229291	23016.7	21.5492	14.44628	22897.4	20.7958	13.862455	0.583824	--	--	0	7123.0	0.6985	0.35	1.38	4.412
104.0548954733497	-27.2558463259291	0.002	0.001	0.0	2920584431371180032	104.05485725522	0.0416	-27.25580875818	0.0605	3.171	0.0701	-7.891	0.093	8.725	0.125	False	3933.04	2.90225	16.701544	757.811	8.47544	18.152485	5000.2	13.8369	15.514452	2.638033	--	--	0	3850.87	--	--	--	--
104.0694340110457	-27.2576712119335	0.002	0.002	0.0	2920584397011440896	104.06944840319	0.0433	-27.25766713242	0.0604	0.1999	0.0704	2.972	0.097	0.947	0.117	False	2738.99	1.91561	17.094389	1434.31	7.03915	17.459778	1970.62	7.97251	16.525414	0.934364	--	--	0	--	--	--	--	--
104.0691908712154	-27.2568296198757	0.001	0.001	0.0	2920584397011440768	104.0692053709	0.0312	-27.25682640385	0.0432	0.3691	0.051	2.994	0.071	0.747	0.084	False	5024.6	2.9405	16.435612	2434.76	11.4809	16.885248	3781.8	8.83712	15.817672	1.067576	--	--	0	5528.97	1.212	0.612	--	--
104.0516229058904	-27.2654744088567	0.007	0.006	0.0	2920584427074472960	104.05161994176	0.166	-27.26546987388	0.2204	0.0985	0.2568	-0.612	0.38	1.053	0.443	False	413.827	1.09334	19.146318	213.823	5.51744	19.52625	289.646	6.41824	18.607252	0.918999	--	--	0	--	--	--	--	--
104.0456279910352	-27.2553458886584	0.027	0.024	0.0	2920584431368205440	104.045632765	0.7158	-27.25534350025	1.1654	0.8441	1.7019	0.986	1.555	0.555	1.717	True	98.6362	1.01132	20.703276	39.5754	6.4779	21.357824	100.045	9.09798	19.76143	1.596394	--	--	0	--	--	--	--	--
104.0573071065338	-27.2580304620829	0.014	0.011	0.0	2920584431368203776	104.05731859217	0.3295	-27.25801566505	0.4545	1.9067	0.5526	2.371	0.729	3.437	0.88	False	149.656	0.783436	20.250633	46.6963	4.79454	21.17818	171.783	7.39984	19.17447	2.00371	--	--	0	--	--	--	--	--
104.0680470357636	-27.2637505825683	0.045	0.036	90.0	2920584397008464768	104.06804754685	1.4129	-27.26373295881	1.2412	1.2819	1.4368	0.106	2.903	4.093	2.318	False	89.7368	1.00894	20.80594	31.8596	6.25523	21.593287	58.9933	4.99633	20.334913	1.258373	--	--	0	--	--	--	--	--
104.0616652942046	-27.2532464715715	0.04	0.026	0.0	2920584465727938816	104.06167568449	0.7841	-27.25323162062	1.1781	-0.7362	1.4518	2.145	1.704	3.449	2.55	False	82.7773	1.08126	20.893587	38.2685	4.66138	21.394283	78.0634	5.09185	20.030802	1.363482	--	--	0	--	--	--	--	--
104.0662806585689	-27.2546681736001	0.03	0.023	0.0	2920584465727937536	104.06628485341	0.6252	-27.25466260093	0.9108	1.2373	1.0833	0.866	1.451	1.294	1.909	True	178.205	2.49015	20.061064	97.051	7.14713	20.383888	219.522	5.96428	18.908224	1.475664	--	--	0	--	--	--	--	--
104.0788155655205	-27.2272798414367	0.004	0.004	0.0	2920584598868738304	104.07881970116	0.1197	-27.22728636357	0.149	0.5907	0.1759	0.854	0.263	-1.515	0.275	False	681.822	1.18831	18.604189	264.353	6.15706	19.295929	617.405	7.17539	17.785494	1.510435	--	--	0	--	--	--	--	--
104.088664953225	-27.2282989583298	0.008	0.007	0.0	2920584598868738048	104.08867062619	0.1944	-27.22829736189	0.2616	0.458	0.3177	1.172	0.424	0.371	0.502	False	316.519	0.890841	19.437366	131.63	9.33518	20.053001	280.338	6.20862	18.642715	1.410286	--	--	0	--	--	--	--	--
104.0853780961902	-27.2301582731315	0.013	0.011	0.0	2920584598868737664	104.08537803475	0.3299	-27.23014843631	0.4265	-0.2219	0.5139	-0.013	0.726	2.285	0.826	False	175.19	0.834061	20.079596	84.9799	7.75228	20.528097	151.73	8.92596	19.309244	1.218853	--	--	0	--	--	--	--	--
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
103.6980191636504	-26.6955453737516	0.013	0.011	0.0	2920662595476101504	103.69802523954	0.3128	-26.69553035034	0.4556	0.7454	0.5698	1.261	0.706	3.489	0.851	False	163.782	0.731918	20.152702	72.1056	6.55497	20.706465	157.411	4.92048	19.269335	1.43713	--	--	0	--	--	--	--	--
103.6746416153805	-26.6910706280517	0.004	0.003	0.0	2920662629835864704	103.67463719593	0.0939	-26.69105382372	0.1312	0.1145	0.1554	-0.917	0.205	3.903	0.276	False	809.864	1.25027	18.417336	407.866	6.17804	18.825096	603.63	6.23255	17.809992	1.015104	--	--	0	--	--	--	--	--
103.6818307323895	-26.697764007304	0.002	0.001	0.0	2920662629835835264	103.68182848053	0.0334	-26.69775348539	0.0465	0.0676	0.0538	-0.467	0.071	2.444	0.098	False	4659.18	2.51836	16.517591	2956.52	9.03788	16.674437	2550.04	6.64929	16.245554	0.428883	--	--	0	7440.46	0.678	0.3765	--	--
103.6895322687124	-26.6918595162009	0.002	0.002	0.0	2920662668492552832	103.68953226871	1.7844	-26.6918595162	1.8236	--	--	--	--	--	--	False	69.7031	1.02242	21.080235	--	--	--	--	--	--	--	--	--	0	--	--	--	--	--
103.7083903844813	-26.6912866733554	0.001	0.001	0.0	2920662595476120576	103.70838328031	0.0264	-26.691263696	0.0372	0.4832	0.0434	-1.474	0.057	5.337	0.077	False	7221.16	3.60778	16.041847	3818.97	10.8273	16.39652	4989.14	12.5942	15.516856	0.879664	--	--	0	5943.33	0.326	0.1727	1.04	1.211
103.6847552008415	-26.6979320618753	0.024	0.017	0.0	2920662565411348224	103.68475299322	0.5015	-26.69793019693	0.8085	-2.4013	0.9869	-0.458	1.078	0.433	1.571	False	95.2422	0.798192	20.741291	38.6563	4.23507	21.383337	95.4755	6.57234	19.812191	1.571146	--	--	0	--	--	--	--	--
103.6804167212213	-26.6899073767829	0.048	0.042	0.0	2920662634133582976	103.68042227898	1.2405	-26.68990953228	1.5957	-2.868	2.3855	1.153	2.739	-0.501	3.081	False	80.946	1.37972	20.917877	131.636	55.9659	20.052952	114.975	19.2358	19.610413	0.442539	--	--	0	--	--	--	--	--
103.6729444953412	-26.689742160695	0.016	0.013	0.0	2920662634130822144	103.67293521185	0.3692	-26.68972574574	0.5309	-0.0026	0.6628	-1.926	0.833	3.813	1.007	False	155.407	0.759899	20.209692	67.7929	6.28739	20.773428	173.335	7.19227	19.164703	1.608725	--	--	0	--	--	--	--	--
103.6930010144782	-26.7032247197741	0.022	0.019	0.0	2920662565411346176	103.69299313975	0.5775	-26.70321968283	0.7472	-0.1938	0.9341	-1.634	1.219	1.17	1.429	False	95.5776	0.771579	20.737474	49.898	6.51267	21.10618	79.8049	7.32932	20.006845	1.099335	--	--	0	--	--	--	--	--
103.6938936643781	-26.6813990762261	0.02	0.017	0.0	2920662668490514816	103.69390670848	0.54	-26.68134029445	0.6897	1.3021	0.8801	2.707	1.127	13.653	1.321	False	111.953	0.82778	20.565771	50.4941	7.46123	21.093285	143.594	5.97298	19.369081	1.724203	--	--	0	--	--	--	--	--
103.7075147825871	-26.6904155990594	0.005	0.004	0.0	2920662599771038208	103.70751414374	0.1178	-26.69040993988	0.1698	0.2706	0.2027	-0.133	0.256	1.314	0.34	False	586.662	1.36292	18.767395	328.178	7.96531	19.061113	440.727	7.71029	18.151495	0.909618	--	--	0	--	--	--	--	--
103.6968408120309	-26.7022390430557	0.041	0.027	0.0	2920662565411337856	103.69682530495	0.8676	-26.70224144082	1.6385	0.8771	2.0106	-3.218	1.729	-0.557	2.618	False	77.0156	0.879316	20.971918	27.9552	5.17978	21.735233	85.0464	4.64908	19.93778	1.797453	--	--	0	--	--	--	--	--
103.6790946300216	-26.6907373827732	0.001	0.0	0.0	2920662634135133312	103.67909063938	0.0127	-26.69073002769	0.0173	0.8977	0.0197	-0.828	0.027	1.708	0.036	False	117095.0	34.3236	13.017016	66021.1	65.5945	13.302181	74663.1	66.0766	12.579155	0.723026	--	--	0	6375.19	--	--	1.94	5.627
103.6851908449474	-26.69890325167	0.003	0.003	0.0	2920662561116355072	103.68521117102	0.0745	-26.69887979755	0.1016	0.4831	0.1191	4.218	0.162	5.447	0.216	False	1208.17	1.49902	17.983042	507.489	6.91805	18.587822	995.474	7.54112	17.266846	1.320976	--	--	0	--	--	--	--	--
103.6880062644454	-26.7070865392615	0.011	0.008	0.0	2920662565411365376	103.68799346907	0.2503	-26.70707543012	0.3416	0.4313	0.4156	-2.655	0.546	2.58	0.679	False	224.174	0.749181	19.811903	91.5503	7.04327	20.447239	230.138	5.21963	18.856949	1.59029	--	--	0	--	--	--	--	--
103.6772278913576	-26.7002808092164	0.012	0.01	0.0	2920662629835826048	103.67722877698	0.298	-26.70027090026	0.402	-0.0166	0.4981	0.184	0.666	2.301	0.766	False	196.759	0.814684	19.95353	86.9839	6.31875	20.50279	175.031	6.01048	19.154133	1.348658	--	--	0	--	--	--	--	--
103.6798534900421	-26.7014561223488	0.026	0.025	0.0	2920662634133582720	103.6798508918	0.6922	-26.7014522794	0.998	2.5848	1.265	-0.539	1.637	0.893	1.693	False	83.5343	0.765707	20.883703	40.4482	4.52542	21.334139	83.1915	5.42943	19.961721	1.372417	--	--	0	--	--	--	--	--
103.704266301682	-26.6938492124899	0.005	0.003	0.0	2920662599773842688	103.70426630168	2.763	-26.69384921249	5.2851	--	--	--	--	--	--	False	60.1111	1.285	21.240978	--	--	--	--	--	--	--	--	--	0	--	--	--	--	--
103.6697589867552	-26.6951758197538	0.001	0.001	0.0	2920662634135140096	103.66974814703	0.0295	-26.6951623859	0.0403	0.371	0.0467	-2.249	0.063	3.12	0.085	False	6028.79	3.16582	16.237791	3102.51	10.337	16.622107	4220.82	12.4833	15.698429	0.923677	--	--	0	5468.0	0.7358	0.37	1.48	1.774
103.68602363422	-26.6944480592382	0.003	0.001	0.0	2920662634133440640	103.68602363422	1.2184	-26.69444805924	2.9684	--	--	--	--	--	--	False	74.3173	0.949053	21.010641	--	--	--	--	--	--	--	--	--	0	--	--	--	--	--
103.6744209115503	-26.6988909128454	0.007	0.005	0.0	2920662629835831168	103.67441648149	0.1581	-26.69888582597	0.2299	0.2941	0.2771	-0.919	0.342	1.181	0.472	False	397.527	1.01674	19.189949	189.347	6.82304	19.658241	289.803	5.6226	18.606663	1.051579	--	--	0	--	--	--	--	--
103.6926283315679	-26.6768997837157	0.005	0.004	0.0	2920662664196999680	103.69265498167	0.1139	-26.67696469044	0.1546	1.9071	0.1871	5.531	0.25	-15.075	0.305	False	694.9	1.24945	18.58356	161.488	8.24506	19.83104	856.035	8.00815	17.43069	2.400349	--	--	0	--	--	--	--	--
103.6905363986536	-26.7090159663416	0.001	0.0	0.0	2920662565415663488	103.69053923482	0.0148	-26.70901855766	0.0206	0.5457	0.0239	0.588	0.031	-0.602	0.043	True	33838.5	9.36355	14.364839	19052.4	21.0165	14.651512	21805.8	18.9203	13.91549	0.736022	--	--	0	6223.9	1.3685	0.6713	1.81	4.411
103.6750143118302	-26.693936720277	0.001	0.0	0.0	2920662634135136896	103.674997273	0.0139	-26.69386021408	0.0199	1.3383	0.0229	-3.536	0.029	17.769	0.042	False	29583.9	8.09417	14.510725	14956.6	21.998	14.914305	21018.9	14.2953	13.955395	0.95891	--	--	0	5420.33	0.136	0.066	0.93	0.673
103.6907087973092	-26.6942231727546	0.002	0.001	0.0	2920662599775392384	103.69072675092	0.0387	-26.69421081426	0.054	0.6391	0.0628	3.725	0.083	2.87	0.114	False	3875.07	2.29552	16.717669	1803.81	7.60852	17.210913	2963.16	10.41	16.082531	1.128382	--	--	0	4980.0	0.2858	0.145	0.87	0.416
103.6860670864347	-26.6917995458857	0.001	0.001	0.0	2920662634135131264	103.68606084165	0.0189	-26.69178403078	0.0264	0.3501	0.0305	-1.296	0.04	3.604	0.056	False	12932.6	4.90093	15.409149	6859.38	10.803	15.760675	8886.91	14.6996	14.890042	0.870633	--	--	0	5733.66	--	--	2.07	4.179
103.6842737979447	-26.7017503328947	0.037	0.034	90.0	2920662565413339904	103.68428008329	1.2459	-26.70174992807	1.3821	-2.2439	1.7372	1.304	2.365	0.094	2.175	False	79.5694	0.728902	20.9365	46.5208	9.30062	21.182272	69.2421	9.30353	20.160994	1.021278	--	--	0	--	--	--	--	--
103.6820034780249	-26.6937761144216	0.022	0.021	0.0	2920662634130817280	103.68199815747	0.6459	-26.69380694957	0.7993	-0.8871	1.0528	-1.104	1.328	-7.162	1.417	False	102.542	0.905935	20.661116	33.4179	3.92701	21.541441	129.802	6.87251	19.478714	2.062727	--	--	0	--	--	--	--	--
103.7023633842059	-26.693778177254	0.01	0.008	0.0	2920662595476109056	103.70236259794	0.2279	-26.69377767625	0.3259	0.7025	0.4055	-0.163	0.504	0.116	0.646	False	239.363	0.951384	19.740725	130.015	7.9778	20.066406	185.254	6.96004	19.0925	0.973906	--	--	0	--	--	--	--	--
103.67736401159	-26.6877543256818	0.017	0.014	0.0	2920662634130810880	103.67736098157	0.3959	-26.68773232011	0.5748	1.2492	0.7108	-0.629	0.914	5.111	1.077	False	128.438	0.720352	20.416636	50.119	8.06151	21.101381	155.45	5.97567	19.282942	1.81844	--	--	0	--	--	--	--	--
103.6534419859618	-26.6979173546072	0.011	0.008	0.0	2920662732915053568	103.65344412303	0.2405	-26.6979121828	0.3592	-0.2447	0.4337	0.443	0.527	1.201	0.703	False	243.033	0.82508	19.724205	122.136	6.61412	20.134275	190.878	5.0736	19.060034	1.074242	--	--	0	--	--	--	--	--
103.6448602407325	-26.6802759107745	0.02	0.016	0.0	2920662805934420608	103.64483841775	0.4784	-26.68027019884	0.7468	-0.416	0.9312	-4.529	1.041	1.327	1.287	False	111.582	0.895516	20.569384	57.4815	6.8582	20.952568	95.2456	4.81755	19.814808	1.13776	--	--	0	--	--	--	--	--
103.6457054696464	-26.6981699399863	0.001	0.001	0.0	2920662698555162368	103.64568696319	0.0173	-26.69816199835	0.0245	1.9823	0.0283	-3.84	0.038	1.845	0.054	False	245087.0	31.1344	12.215064	130678.0	131.721	12.560881	163903.0	73.7421	11.725456	0.835424	71.72	1.12	13	5776.33	0.846	0.4065	1.57	2.463
103.6371030479543	-26.7044315808754	0.005	0.004	0.0	2920662698555289472	103.6370976891	0.1128	-26.70442672734	0.1577	0.0761	0.1887	-1.112	0.244	1.127	0.321	False	618.666	1.19579	18.709726	310.82	7.22852	19.120115	450.3	5.5849	18.128166	0.991949	--	--	0	--	--	--	--	--
103.6456888116766	-26.693409125209	0.012	0.01	0.0	2920662737210100992	103.64568864196	0.3058	-26.69339933944	0.4287	0.3929	0.5375	-0.035	0.676	2.273	0.788	False	182.539	0.828202	20.034979	92.8826	5.12254	20.431551	160.285	5.1688	19.24969	1.181862	--	--	0	--	--	--	--	--
103.6501059275592	-26.6996148606457	0.01	0.007	0.0	2920662698555304576	103.65009776268	0.2151	-26.69960283806	0.326	0.4273	0.394	-1.694	0.472	2.792	0.621	False	249.158	0.843633	19.697176	130.477	8.06077	20.062551	218.437	7.50649	18.913603	1.148949	--	--	0	--	--	--	--	--
103.6595666536828	-26.6973161276413	0.019	0.016	0.0	2920662737210084608	103.65954407734	0.4646	-26.6972921808	0.7137	1.0984	0.8643	-4.685	1.01	5.562	1.241	False	123.589	0.804161	20.458414	42.8328	5.53901	21.271946	143.462	6.25761	19.37008	1.901865	--	--	0	--	--	--	--	--
103.6457432485343	-26.7008849970763	0.018	0.013	0.0	2920662702850376192	103.64573874872	0.3872	-26.70086725867	0.5581	-1.2408	0.6952	-0.934	0.858	4.12	1.153	False	122.459	0.708617	20.468393	77.6656	8.66697	20.625816	151.422	7.91143	19.31145	1.314367	--	--	0	--	--	--	--	--
103.6428952792929	-26.6997584901798	0.029	0.021	0.0	2920662702850377344	103.64289510825	0.6431	-26.69974515128	1.0558	0.884	1.2836	-0.035	1.386	3.098	1.845	False	85.1474	0.864897	20.862938	65.0185	7.27796	20.818796	100.969	8.51741	19.751446	1.06735	--	--	0	--	--	--	--	--

Step 6: Cross-match Gaia and WISE 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 Wide Infrared Survey Explorer (WISE) and Gaia catalogs. To do so, we will perform a cross-match of the Gaia and WISE 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

_2MASS	RAJ2000	DEJ2000	Hmag	Kmag	Gmag
	deg	deg	mag	mag	mag
str17	float64	float64	float32	float32	float64
06561231-2715514	104.051312	-27.264296	15.533	15.274	16.924894
06561317-2715210	104.05491	-27.255844	13.292	13.059	16.701544
06561481-2716053	104.061712	-27.268162	13.336	13.2	15.479486
06561479-2715417	104.061646	-27.261585	13.234	13.266	14.229291
06561641-2715166	104.068408	-27.254629	13.625	13.543	14.883485
06561656-2715126	104.069033	-27.253527	15.883	14.747	18.727604
06561660-2715246	104.069205	-27.256844	14.631	14.457	16.435612
06561532-2714537	104.063862	-27.248253	15.2	15.02	17.405537
06561543-2714325	104.064318	-27.242363	14.231	14.075	15.734059
06561711-2714541	104.071307	-27.248383	13.307	13.163	15.021435
06562032-2715025	104.084702	-27.250706	14.331	14.457	15.809618
06562075-2714021	104.086461	-27.233923	15.728	16.289	17.67484
06561726-2714384	104.071925	-27.244024	13.139	13.014	14.705641
06561759-2713598	104.073293	-27.23328	14.177	13.872	18.109772
06561894-2713384	104.078949	-27.227339	16.108	15.403	18.604189
06561879-2714289	104.078292	-27.241377	14.951	14.828	18.446154
06561060-2715111	104.044183	-27.253084	16.299	16.293	17.89479
06561225-2714413	104.05108	-27.244808	16.414	16.016	17.921455
06561125-2714498	104.04688	-27.247187	9.834	9.697	11.854339
06561135-2714113	104.047301	-27.236488	16.419	14.372	19.787733
06560974-2713092	104.040599	-27.219234	14.019	13.958	15.416584
06561099-2714107	104.045795	-27.236309	13.326	13.16	15.873589
06560717-2714071	104.029884	-27.235325	15.915	14.948	18.844616
06561342-2712466	104.055943	-27.212948	13.222	13.158	14.7209
06561218-2713314	104.050776	-27.225395	15.986	15.506	18.21587
06561408-2712226	104.058682	-27.206284	17.096	16.874	18.727905
06561494-2712042	104.062284	-27.201172	15.928	15.776	18.452906
06561443-2712057	104.060132	-27.201597	16.505	16.448	18.147007
06561453-2713473	104.060581	-27.229809	16.236	15.5	18.611937
06561833-2713238	104.076384	-27.223291	13.968	13.81	15.312183
06561512-2712552	104.063005	-27.215347	14.996	14.771	17.754915
06561592-2714018	104.066348	-27.233858	16.522	16.767	18.27725
06561538-2712393	104.064098	-27.210922	15.422	15.169	18.392714
06561585-2712419	104.066047	-27.211639	15.268	15.269	16.786901
06561553-2712203	104.064732	-27.205664	14.092	13.9	15.185103
06561657-2712502	104.069083	-27.213955	16.099	16.943	17.4553
06561926-2712497	104.080262	-27.213806	16.308	16.932	17.962248
06562408-2712536	104.100347	-27.214893	14.385	14.381	15.704457
...	...	...	...	...	...
06545575-2640333	103.732294	-26.675938	13.934	13.782	15.741429
06545577-2640583	103.732402	-26.682877	15.495	15.049	18.186043
06545507-2640505	103.729462	-26.680716	11.228	11.067	13.227829
06545831-2640090	103.742994	-26.669167	16.355	15.685	18.128725
06545780-2640467	103.740844	-26.679644	15.921	15.068	18.661951
06545765-2640113	103.740249	-26.669823	13.789	13.657	16.623756
06550010-2640487	103.750443	-26.680216	16.046	15.739	18.310627
06550006-2640340	103.750286	-26.676113	15.136	15.039	16.940012
06550048-2641143	103.752022	-26.687319	16.383	15.695	17.837942
06550040-2639348	103.751686	-26.659678	15.415	15.527	16.990917
06550025-2639509	103.75105	-26.66416	15.522	15.292	16.93812
06550504-2640232	103.771012	-26.673117	15.649	15.174	18.920807
06550151-2641142	103.756294	-26.687286	14.139	14.124	16.556156
06550292-2640074	103.762204	-26.66873	15.595	15.395	17.554699
06550363-2640572	103.765125	-26.682556	13.995	13.875	15.622164
06543817-2642130	103.659062	-26.703613	16.005	15.016	19.36678
06543882-2642562	103.66175	-26.71563	14.391	14.236	15.837558
06543881-2642487	103.661746	-26.713541	14.478	14.349	15.86585
06543912-2643298	103.663013	-26.724953	15.045	14.939	18.58063
06543942-2643080	103.664291	-26.718916	15.419	15.307	16.771603
06544053-2643307	103.668888	-26.725222	14.552	14.372	15.731012
06544016-2643313	103.66737	-26.72538	15.503	15.111	17.339947
06544029-2642277	103.667897	-26.707705	14.169	14.222	16.248013
06544164-2643157	103.67353	-26.721043	15.468	15.013	16.942991
06544205-2643260	103.675222	-26.723898	15.134	15.056	17.244776
06544330-2642414	103.680432	-26.711517	15.83	15.691	16.959612
06544298-2641266	103.67909	-26.690739	11.837	11.801	13.017016
06544364-2641518	103.681834	-26.697739	15.817	15.608	16.517591
06544444-2641560	103.685202	-26.698898	15.801	15.703	17.983042
06544200-2641381	103.675002	-26.693939	12.981	12.961	14.510725
06544465-2641304	103.686061	-26.69179	13.983	13.896	15.409149
06544074-2641426	103.669757	-26.695183	14.713	14.588	16.237791
06544573-2642324	103.690544	-26.709017	13.162	13.106	14.364839
06545001-2641286	103.708399	-26.691288	14.669	14.662	16.041847
06544577-2641391	103.690725	-26.694202	14.811	14.78	16.717669
06544623-2640367	103.692647	-26.676868	15.171	14.327	18.58356
06543496-2641534	103.645686	-26.698172	10.895	10.8	12.215064
06560422-2639092	104.017586	-26.652565	16.295	15.76	19.62056
06585741-2635059	104.739224	-26.584993	15.503	15.558	18.368963

Step 7: Build a color-color diagram

We now use the data we got from the cross-match to get a WISE/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()