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

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.

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:

learn how to find fits files
build a map of the coverage of all the fits files
select only the parts of this map that correspond to low-extinction regions
retrieve objects from large catalogs inside the obtained map – that has a non-trivial shape and non-necessarily-connected regions
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()

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

_images/652a84c5754995dea5c3aad68d89ff6a8f23d886b58688b500c40fab880a61c1.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.

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 0x7f51482d8090>

_images/7cfdba7d588b8bad4c4e957f821478dc5abe8de4476ba95acc165eee56c9d41e.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)

II/126 :  IRAS Serendipitous Survey Catalog (IPAC 1986)
II/241 :  2MASS Catalog Incremental Data Release (IPAC/UMass, 2000)
II/246 :  2MASS All-Sky Catalog of Point Sources (Cutri+ 2003)
II/281 :  2MASS 6X Point Source Working Database / Catalog (Cutri+ 2006)
II/307 :  WISE Preliminary Data Release (Cutri+ 2011)
II/311 :  WISE All-Sky Data Release (Cutri+ 2012)
II/328 :  AllWISE Data Release (Cutri+ 2013)
II/365 :  The CatWISE2020 catalog (updated version 28-Jan-2021) (Marocco+, 2021)
VII/233 :  The 2MASS Extended sources (IPAC/UMass, 2003-2006)
J/ApJ/560/566 :  K-band galaxy luminosity function from 2MASS (Kochanek+, 2001)
J/ApJ/564/421 :  Spectra of T dwarfs. I. (Burgasser+, 2002)
J/ApJ/569/23 :  Optical polarisation of 2MASS QSOs (Smith+, 2002)
J/ApJ/635/214 :  Chandra X-ray sources and NIR identifications (Ebisawa+, 2005)
J/ApJ/713/330 :  Spitzer observations of major-merger galaxies (Xu+, 2010)
J/ApJ/719/550 :  Deep NIR imaging of {rho} Oph cloud core (Marsh+, 2010)
J/ApJ/741/68 :  Main Belt asteroids with WISE/NEOWISE. I. (Masiero+, 2011)
J/ApJ/742/40 :  Jovian Trojans asteroids with WISE/NEOWISE (Grav+, 2011)
J/ApJ/743/156 :  NEOWISE observations of NEOs: preliminary results (Mainzer+, 2011)
J/ApJ/744/197 :  WISE/NEOWISE observations of Hilda asteroids (Grav+, 2012)
J/ApJ/759/L8 :  WISE/NEOWISE observations of main belt asteroids (Masiero+, 2012)
J/ApJ/760/L12 :  WISE/NEOWISE NEOs preliminary thermal fits (Mainzer+, 2012)
J/ApJ/780/92 :  Light curves of the RR Lyr SDSS J015450.17+001500.7 (Szabo+, 2014)
J/ApJ/783/122 :  AllWISE motion survey (Kirkpatrick+, 2014)
J/ApJ/784/110 :  NEOWISE observations of 105 near-Earth objects (Mainzer+, 2014)
J/ApJ/792/30 :  NEOWISE magnitudes for near-Earth objects (Mainzer+, 2014)
J/ApJ/805/90 :  WISE ELIRGs and comparison with QSOs (Tsai+, 2015)
J/ApJ/814/117 :  NEOWISE Reactivation mission: 1st yr data (Nugent+, 2015)
J/ApJ/874/82 :  Follow-up photometry & spectroscopy of PTF14jg (Hillenbrand+, 2019)
J/ApJS/95/1 :  Atlas of Quasar Energy Distributions (Elvis+ 1994)
J/ApJS/172/663 :  Infrared observations of the Pleiades (Stauffer+, 2007)
J/ApJS/175/191 :  Variables from 2MASS calibration fields (Plavchan+, 2008)
J/ApJS/190/100 :  NIR proper motion survey using 2MASS (Kirkpatrick+, 2010)
J/ApJS/199/26 :  The 2MASS Redshift Survey (2MRS) (Huchra+, 2012)
J/ApJS/224/36 :  The AllWISE motion survey (AllWISE2) (Kirkpatrick+, 2016)
J/ApJS/234/23 :  The WISE AGN candidates catalogs (Assef+, 2018)
J/AJ/112/62 :  Quasar absorption-line systems (Tanner+ 1996)
J/AJ/125/2521 :  2MASS6x survey of the Lockman Hole (Beichman+, 2003)
J/AJ/126/63 :  Host galaxies of 2MASS-QSOs with z<=3 (Hutchings+, 2003)
J/AJ/127/646 :  Unbiased census of AGN in 2MASS (Francis+, 2004)
J/AJ/135/2245 :  Absolute spectrum of the Sun and Vega for 0.2-30um (Rieke+, 2008)
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)
J/AJ/154/53 :  WISE/NEOWISE observations of comets (Bauer+, 2017)
J/AJ/154/168 :  NEOWISE: thermal model fits for NEOs and MBAs (Masiero+, 2017)
J/AJ/156/60 :  Thermal model fits for short-arc NEOs with NEOWISE (Masiero+, 2018)
J/PASP/113/10 :  Sub-mJy radio sources complete sample (Masci+, 2001)

catalog_list_gaia = Vizier.find_catalogs("Gaia DR2", max_catalogs=1000)
for k, v in catalog_list_gaia.items():
    print(k, ": ", v.description)

WARNING: UnitsWarning: Unit 'x' not supported by the VOUnit standard.  [astropy.units.format.vounit]
WARNING: UnitsWarning: Unit 'Earth' not supported by the VOUnit standard.  [astropy.units.format.vounit]
WARNING: UnitsWarning: Unit 'Sun' not supported by the VOUnit standard. Did you mean uN? [astropy.units.format.vounit]

WARNING: UnitsWarning: Unit 'kau' not supported by the VOUnit standard. Did you mean kA, ka, kadu or ku? [astropy.units.format.vounit]

I/345 :  Gaia DR2 (Gaia Collaboration, 2018)
I/347 :  Distances to 1.33 billion stars in Gaia DR2 (Bailer-Jones+, 2018)
I/349 :  StarHorse, Gaia DR2 photo-astrometric distances (Anders+, 2019)
II/360 :  Gaia DR2 x AllWISE catalogue (Marton+, 2019)
IV/35 :  Gaia DR2-WISE Galactic Plane Matches (Wilson+, 2018)
VI/156 :  M-dwarf Lum-Temp-Radius relationships (Morrell+, 2019)
VII/285 :  Gaia DR2 quasar and galaxy classification (Bailer-Jones+, 2019)
J/ApJ/862/138 :  BANYAN. XIII. Nearby young assoc. with Gaia DR2 (Gagne+, 2018)
J/ApJ/863/89 :  Gaia DR2 PMs of stars in ultra-faint MW satellites (Simon, 2018)
J/ApJ/866/99 :  Revised radii of KIC stars & planets using Gaia DR2 (Berger+, 2018)
J/ApJ/867/151 :  YSOs in the Gould Belt regions with Gaia-DR2 data (Dzib+, 2018)
J/ApJ/868/70 :  Hot subdwarf stars from Gaia DR2 and LAMOST DR5 (Lei+, 2018)
J/ApJ/870/32 :  Kinematics in young star clusters & associations (Kuhn+, 2019)
J/ApJ/872/85 :  Detached eclipsing binaries with Gaia parallaxes (Graczyk+, 2019)
J/ApJ/873/132 :  ICRF3 radio-loud quasars with Gaia DR2 data (Makarov+, 2019)
J/ApJ/874/138 :  Gaia and LAMOST DR4 M giant members of Sgr stream (Li+, 2019)
J/ApJ/875/77 :  Proper motions of MW satellites with Gaia & DES (Pace+, 2019)
J/ApJ/877/29 :  Opt & NIR SMARTS/ANDICAM photometry for DQ Tau (Muzerolle+, 2019)
J/ApJ/878/111 :  Members in Serpens Molecular Cloud with Gaia DR2 (Herczeg+, 2019)
J/ApJ/881/7 :  Hot subdwarf stars from LAMOST DR5 & Gaia DR2 (Luo+, 2019)
J/ApJ/881/135 :  Hot subdwarf stars from Gaia DR2 and LAMOST DR5. II. (Lei+, 2019)
J/ApJ/883/8 :  Cand. young OB stars from GALEX & Gaia DR2 (Casetti-Dinescu+, 2019)
J/ApJ/884/6 :  Known members of Orion A with Gaia DR2 data (McBride+, 2019)
J/ApJ/886/100 :  High-mass white dwarfs in Gaia DR2 (Cheng+, 2019)
J/ApJ/887/126 :  Follow-up obs. of the PSJ0147+4630 lens system (Goicoechea+, 2019)
J/ApJ/888/73 :  Varstrometry of SDSS quasars with Gaia DR2 data (Hwang+, 2020)
J/ApJ/889/99 :  Gaia DR2 Blanco 1 member candidates (Zhang+, 2020)
J/ApJ/889/117 :  Hot subdwarf stars from Gaia DR2 & LAMOST DR6+7. I. (Lei+, 2020)
J/ApJ/889/176 :  Very low-mass binaries with Gaia DR2 data (Faherty+, 2020)
J/ApJ/898/64 :  Hot subdwarf stars from Gaia with LAMOST sp. II. RVs (Luo+, 2020)
J/ApJ/898/173 :  Solar analog rotations from Kepler & Gaia (Do Nascimento+, 2020)
J/ApJ/899/83 :  723 Gaia DR2 White dwarfs cand. in Local Galactic Halo (Kim+, 2020)
J/ApJ/910/46 :  Galactic anticenter substructure stars from LAMOST (Li+, 2021)
J/ApJ/913/120 :  4FGL blazars with Gaia DR2 data (Zeng+, 2021)
J/ApJ/914/16 :  NGC 5128 GCs from PISCes, Gaia DR2 and NSC (Hughes+, 2021)
J/ApJ/914/122 :  Stellar polarimetry with Gaia DR2 data (Doi+, 2021)
J/ApJ/914/123 :  Gaia DR2 and EDR3 stars with sp. follow-up (Ibata+, 2021)
J/ApJ/915/103 :  Oscillator strength ref. for Indus_0 & Indus_13 (Hansen+, 2021)
J/ApJ/921/42 :  Gaia GraL. VI. Quadruply imaged lensed QSOs (Stern+, 2021)
J/ApJS/245/32 :  Newly identified star clusters in Gaia DR2 (Liu+, 2019)
J/ApJS/246/4 :  Catalog of ultrawide binary stars from Gaia DR2 (Tian+, 2020)
J/ApJS/252/3 :  High-velocity stars in the Gal. halo from LAMOST & Gaia (Li+, 2021)
J/ApJS/253/58 :  Ages of field stars from white dwarf comp. in Gaia (Qiu+, 2021)
J/ApJS/254/20 :  Stellar groups in Taurus field from Gaia DR2 & LAMOST (Liu+, 2021)
J/ApJS/256/28 :  Hot subdwarf stars with Gaia DR2 and LAMOST DR7 data (Luo+, 2021)
J/ApJS/260/8 :  541 new open cluster candidates (He+, 2022)
J/A+A/616/A7 :  Gaia DR2 radial velocity standard stars catalog (Soubiran+, 2018)
J/A+A/616/A12 :  Gaia DR2 sources in GC and dSph (Gaia Collaboration+, 2018)
J/A+A/616/A37 :  Close encounters to the Sun in Gaia DR2 (Bailer-Jones+, 2018)
J/A+A/616/L2 :  Planetary Nebulae distances in Gaia DR2 (Kimeswenger+, 2018)
J/A+A/616/L15 :  Parallaxes and Proper Motions of OB stars (Xu+, 2018)
J/A+A/617/A135 :  20 years of photometric microlensing (Mustill+, 2018)
J/A+A/618/A44 :  Predicted microlensing events from Gaia DR2 (Bramich, 2018)
J/A+A/618/A56 :  Gaia GraL. II. Known multiply imaged quasars (Ducourant+, 2018)
J/A+A/618/A59 :  Gaia DR2 confirmed new nearby open clusters (Castro-Ginard+, 2018)
J/A+A/618/A93 :  Gaia DR2 open clusters in the Milky Way (Cantat-Gaudin+, 2018)
J/A+A/619/A8 :  Cepheid period-luminosity-metallicity relation (Groenewegen, 2018)
J/A+A/619/A106 :  3D shape of Orion A from Gaia DR2 (Grossschedl+, 2018)
J/A+A/619/A155 :  Open cluster kinematics with Gaia DR2 (Soubiran+, 2018)
J/A+A/619/A180 :  Gaia DR2 photometric sensitivity curves (Maiz Apellaniz+, 2018)
J/A+A/619/L8 :  Ultra-cool dwarfs candidates in Gaia DR2 (Reyle, 2018)
J/A+A/620/A91 :  New asteroid models (Durech+, 2018)
J/A+A/620/A128 :  Gaia DR2 study of Herbig Ae/Be stars (Vioque+, 2018)
J/A+A/620/A141 :  Physical properties of AM CVn stars (Ramsay+, 2018)
J/A+A/620/A155 :  Gaia proper motions of 7 UFD galaxies (Massari+, 2018)
J/A+A/620/A172 :  Solar neighbourhood young stars 3D mapping (Zari+, 2018)
J/A+A/621/A38 :  Gaia catalogue of hot subluminous stars (Geier+, 2019)
J/A+A/621/A48 :  Gaia-DR2 extended kinematical maps. I. (Lopez-Corredoira+, 2019)
J/A+A/621/L2 :  Hyades tidal tails revealed by Gaia DR2 (Roeser+, 2019)
J/A+A/621/L3 :  Hyades tidal tails with Gaia DR2 (Meingast+, 2019)
J/A+A/622/A60 :  Gaia DR2 misclassified RR Lyrae list (Clementini+, 2019)
J/A+A/622/A165 :  Gaia GraL. III. New lensed systems (Delchambre+, 2019)
J/A+A/622/L13 :  Stellar stream in Gaia DR2 discovery (Meingast+, 2019)
J/A+A/623/A22 :  IC 4996 Vilnius phot. and Gaia DR2 astrometry (Straizys+, 2019)
J/A+A/623/A25 :  Photometric and astrometric study of NGC 6530 (Damiani+, 2019)
J/A+A/623/A72 :  Binarity of Hipparcos stars from Gaia pm anomaly (Kervella+, 2019)
J/A+A/623/A108 :  Age of 269 GDR2 open clusters (Bossini+, 2019)
J/A+A/623/A110 :  Gaia DR2. Variable stars in CMD (Gaia Collaboration+, 2019)
J/A+A/623/A112 :  The Sco OB2 population from Gaia DR2 data (Damiani+, 2019)
J/A+A/623/A116 :  Galactic Cepheids and RR Lyrae multiplicity. I. (Kervella+, 2019)
J/A+A/623/A117 :  Galactic Cepheids and RR Lyrae multiplicity. II (Kervella+, 2019)
J/A+A/623/A129 :  New satellites of the LMC search (Fritz+, 2019)
J/A+A/624/A78 :  Masses and ages of 1059 HARPS-GTO stars (Delgado Mena+, 2019)
J/A+A/624/A126 :  New open clusters in Perseus direction (Cantat-Gaudin+, 2019)
J/A+A/625/A14 :  Reclassification of Cepheids in the Gaia DR2 (Ripepi+, 2019)
J/A+A/626/A80 :  Census of Rho Oph candidate members from Gaia DR2 (Canovas+, 2019)
J/A+A/627/A35 :  New open clusters in Galactic anti-centre (Castro-Ginard+, 2019)
J/A+A/627/A119 :  Extended halo of NGC 2682 (M 67) (Carrera+ 2019)
J/A+A/628/A81 :  Gaia DR2-based catalogue of 237 Ap stars (Scholz+, 2019)
J/A+A/629/A114 :  sigma Ori GTC+INT spectroscopy (Caballero+, 2019)
J/A+A/630/A119 :  Gaia DR2 distances to two clusters (Maiz Apellaniz, 2019)
J/A+A/630/A137 :  Structure and kinematics of the Taurus region (Galli+, 2019)
J/A+A/630/A150 :  Astrometric data for 211 GAPN sample (Gonzalez-Santamaria+, 2019)
J/A+A/631/A2 :  List of new asteroid models (Durech+, 2019)
J/A+A/631/A145 :  HIP stars DEC proper motions comparison (Damljanovic+, 2019)
J/A+A/633/A98 :  Gaia16aye microlensing event photometry (Wyrzykowski+, 2020)
J/A+A/633/A99 :  Gaia DR2 open clusters in the Milky Way. II (Cantat-Gaudin+, 2020)
J/A+A/633/A135 :  Solar neighbourhood carbon stars properties (Abia+, 2020)
J/A+A/634/A98 :  Corona-Australis DANCe. I. (Galli+, 2020)
J/A+A/635/A45 :  570 new open clusters in the Galactic disc (Castro-Ginard+, 2020)
J/A+A/635/L3 :  Candidate member stars of the Sagittarius stream (Antoja+, 2020)
J/A+A/636/A20 :  OGLE-III parallax events with Gaia DR2 (Wyrzykowski+, 2020)
J/A+A/637/A95 :  16 open clusters UBVI and Gaia DR2 photometry (Perren+, 2020)
J/A+A/638/A21 :  New Herbig Ae/Be and classical Be stars catalog (Vioque+, 2020)
J/A+A/638/A103 :  Central stars of planetary nebulae in Gaia DR2 (Chornay+, 2020)
J/A+A/638/A104 :  Gaia DR2 candidate RR Lyrae of Sgr stream & dwarf (Ramos+, 2020)
J/A+A/641/A79 :  FEDReD. Extinction map with 2MASS and Gaia DR2 (Hottier+, 2020)
J/A+A/642/A21 :  Astrometric parameters for 135 OB stars (Russeil+, 2020)
J/A+A/643/A148 :  Lupus DANCe. Census and 6D structure with Gaia-DR2 (Galli+, 2020)
J/A+A/644/A17 :  Astrometrically-selected QSO candidates (Heintz+, 2020)
J/A+A/645/A72 :  Radial velocities of UCAC4 721-037069 (Clavel+, 2021)
J/A+A/645/A116 :  NGC 2808, NGC 6266 and NGC 6397 Gaia DR2 sources (Kundu+, 2021)
J/A+A/646/A46 :  Chamaeleon DANCe. Stellar population with Gaia-DR2 (Galli+, 2021)
J/A+A/646/A99 :  Gaia DR2 Monoceros and ACS candidates (Ramos+, 2021)
J/A+A/646/A104 :  Improving the open cluster census. I. (Hunt+, 2021)
J/A+A/646/A183 :  Transit photometry of NGTS-14Ab (Smith+, 2021)
J/A+A/648/A44 :  Large-amplitude variables in Gaia DR2 (Mowlavi+, 2021)
J/A+A/653/A160 :  Updated radial velocities from Gaia DR2 (Seabroke+, 2021)
J/A+A/657/A131 :  Ten new systems with O stars (Maiz Apellaniz+, 2022)
J/AJ/156/94 :  APOGEE and Gaia DR2 analysis of IC 166 (Schiappacasse-Ulloa+, 2018)
J/AJ/156/139 :  Main belt asteroid shape distrib. from Gaia DR2 (Mommert+, 2018)
J/AJ/156/259 :  Robo-AO detected close binaries in Gaia DR2 (Ziegler+, 2018)
J/AJ/156/264 :  California-Kepler Survey. VII. Planet radius gap (Fulton+, 2018)
J/AJ/157/78 :  Double & multiple star systems from GaiaDR2 (Jimenez-Esteban+, 2019)
J/AJ/158/20 :  K-M stars of class I candidate RSGs in Gaia DR2 (Messineo+, 2019)
J/AJ/158/109 :  Occurrence rates of planets orbiting FGK stars (Hsu+, 2019)
J/AJ/158/155 :  SB candidates from the RAVE & Gaia DR2 surveys (Birko+, 2019)
J/AJ/159/84 :  Machine-learning regression of extinction in Gaia DR2 (Bai+, 2020)
J/AJ/159/95 :  IC 1369 Vilnius photometry and Gaia DR2 astrometry (Straizys+, 2020)
J/AJ/159/121 :  V-band light curve & RVs of the Cepheid V473Lyr (Evans+, 2020)
J/AJ/159/139 :  Multiple M dwarf stars with Robo-AO and Gaia DR2 (Lamman+, 2020)
J/AJ/159/166 :  Membership & properties of moving groups with Gaia (Ujjwal+, 2020)
J/AJ/159/259 :  Opt-NIR light curve of the quasar 3C 273 (Sobrino Figaredo+, 2020)
J/AJ/160/138 :  68 Gaia DR2 ultra-short-period planet host stars (Hamer+, 2020)
J/AJ/160/284 :  2590 binary stars in Hipparcos and Gaia DR2 (Makarov, 2020)
J/AJ/161/65 :  THYME. IV. 3 Exoplanets around TOI-451 B (Newton+, 2021)
J/AJ/161/231 :  A list of ~330000 stars Kepler missed (Wolniewicz+, 2021)
J/AJ/161/234 :  1.7 million K and M dwarfs cross-matching (Medan+, 2021)
J/AJ/161/237 :  Positions of Triton with Sheshan Station telescope (Zhang+, 2021)
J/AJ/162/181 :  RVel & Hipparcos positions of epsilon Eridani (Llop-sayson+, 2021)
J/AJ/163/219 :  Gaia DR2 stellar flybys in the Sco-Cen OB association (Ma+, 2022)
J/PASP/130/L4101 :  NGC 2112, 2477, 7789 and Col 261 members (Gao+ 2018)
J/PASP/131/D4101 :  Likely Pleiades members with Gaia DR2 (Gao, 2019)
J/MNRAS/480/467 :  Surface gravities for 15 000 Kepler stars (Pande+, 2018)
J/MNRAS/480/4505 :  A white dwarf catalogue from Gaia-DR2 (Jimenez-Esteban+, 2018)
J/MNRAS/480/5242 :  (non-)existence of five sparse open clusters (Kos+, 2018)
J/MNRAS/481/1195 :  Gaia DR2 RR Lyrae stars as standard candles (Muraveva+, 2018)
J/MNRAS/481/3887 :  Astrometry and photometry of 7 open clusters (Dias+, 2018)
J/MNRAS/482/4570 :  Gaia DR2 white dwarf candidates (Gentile Fusillo+, 2019)
J/MNRAS/482/5138 :  Galactic GC mean proper motions & velocities (Baumgardt+, 2019)
J/MNRAS/482/5222 :  DA and DB white dwarfs properties in Gaia DR2 (Tremblay+, 2019)
J/MNRAS/483/5508 :  Three new Galactic star clusters (Ferreira+, 2019)
J/MNRAS/484/1838 :  Spatial substructure of Cygnus OB2 (Berlanas+, 2019)
J/MNRAS/484/2832 :  Proper motions of Milky Way globular clusters (Vasiliev, 2019)
J/MNRAS/485/4906 :  GC number density profiles using Gaia DR2 (de Boer+, 2019)
J/MNRAS/485/5573 :  Gaia-DR2 100pc white dwarf population (Torres+, 2019)
J/MNRAS/486/2477 :  Catalogue of members of NGC 6530 (Wright+, 2019)
J/MNRAS/486/5726 :  80 young open clusters from Gaia DR2 (Dias+, 2019)
J/MNRAS/487/1462 :  The metal-rich halo tail extended in z (Fernandez-Alvar+, 2019)
J/MNRAS/487/2385 :  Distances and ages of 150 open clusters (Monteiro+, 2019)
J/MNRAS/487/2771 :  Gaia-DR2 distance to the W3 Complex (Navarete+, 2019)
J/MNRAS/487/4832 :  Period-luminosity relation of red supergiants (Chatys+, 2019)
J/MNRAS/488/2743 :  Giant Galactic dwarf satellite in Gaia DR2 (Torrealba+, 2019)
J/MNRAS/488/2892 :  Gaia DR2 extremely low-mass WD candidates (Pelisoli+, 2019)
J/MNRAS/488/3024 :  Globular clusters members with Gaia DR2 (Bustos Fierro+, 2019)
J/MNRAS/489/2079 :  Calibration of distances with deep learning (Leung+, 2019)
J/MNRAS/489/3093 :  Gaia DR2 parallax of globular clusters (Shao+, 2019)
J/MNRAS/490/157 :  Fastest stars in the Galaxy with Gaia DR2 (Marchetti+, 2019)
J/MNRAS/490/5647 :  Correlations in unbound star trajectories (Montanari+, 2019)
J/MNRAS/492/1164 :  Abundances of Gaia DR2 wide binaries (Hawkins+, 2020)
J/MNRAS/492/L40 :  Disentangling cataclysmic variables in Gaia DR2 (Abril+, 2020)
J/MNRAS/493/2339 :  Internal motions in OB-associations (Melnik+, 2020)
J/MNRAS/495/663 :  Gaia DR2 OB associations (Ward+, 2020)
J/MNRAS/496/2021 :  New Galactic open clusters (Ferreira+, 2020)
J/MNRAS/502/6080 :  New members of Cygnus OB2 from Gaia DR2 (Orellana+, 2021)
J/MNRAS/502/L90 :  Gaia DR2 Galactic bulge new star clusters (Ferreira+, 2021)
J/MNRAS/503/3660 :  Gaia Spectrophotometric Standard Stars. V. (Pancino+, 2021)
J/MNRAS/504/356 :  Updated parameters of 1743 open clusters (Dias+, 2021)
J/MNRAS/505/1135 :  Gaia/IPHAS catalogue of Ha-excess sources (Fratta+, 2021)
J/AZh/97/733 :  Binary Star Pop. with Common Proper Motion (Sapozhnikov+, 2020)
J/AZh/98/239 :  Orbits of Four Visual Binaries (Romanenko+, 2021)
J/AN/340/386 :  Catalogue of spectroscopic binary candidate stars (Jack, 2019)
J/AN/341/770 :  Corrected proper motion for HIP stars (Damljanovic, 2020)
J/AN/342/538 :  Frolov 1 and NGC 7510 CCD UBV photometry (Yontan+, 2021)
J/AcA/68/351 :  Predicted Microlensing Events by nearby VLM objects (Nielsen+ 2018)
J/other/ApSS/365.112 :  Kinematic data for high luminosity stars (Melnik+, 2020)
J/other/RAA/21.93 :  74 new open clusters found in Gaia DR2 (He+, 2021)
J/other/JDSO/15.434 :  New Binary Systems from Data of Gaia DR2 (Romanov, 2019)

WARNING: UnitsWarning: Unit 'k' not supported by the VOUnit standard. Did you mean K? [astropy.units.format.vounit]

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
44.880847	0.616721	02593140+0037001	13.799	0.025	13.222	0.026	13.167	0.037	AAA	222	111	000	0	0
44.919229	0.610531	02594061+0036379	16.794	0.158	16.093	0.170	15.451	0.206	CCC	222	111	000	0	0
44.921658	0.617475	02594119+0037029	14.666	0.033	14.261	0.043	14.140	0.067	AAA	222	111	000	0	0
44.906036	0.626795	02593744+0037364	15.339	0.050	14.535	0.051	13.654	0.045	AAA	222	111	000	0	0
44.935480	0.621464	02594451+0037172	16.350	0.198	15.759	0.266	15.198	0.208	CDC	222	111	ccc	0	0
44.937738	0.624476	02594505+0037281	8.227	0.023	8.079	--	7.871	--	AUU	166	100	c00	0	0
44.937798	0.625384	02594507+0037313	8.562	0.048	8.094	0.042	7.972	0.063	AAA	111	111	ccc	0	0
44.915768	0.634722	02593978+0038049	16.275	0.104	15.532	0.098	15.565	0.228	AAD	222	111	000	0	0
44.897343	0.629454	02593536+0037460	15.688	0.064	15.529	0.102	15.062	0.150	AAB	222	111	000	0	0
44.909451	0.635709	02593826+0038085	16.609	0.148	16.244	0.206	15.903	--	BCU	220	110	000	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
44.856553	0.682358	02592557+0040564	13.869	--	13.752	--	13.295	0.059	UUE	002	001	00c	0	0
44.785319	0.640323	02590847+0038251	16.398	0.134	15.889	0.148	15.408	0.195	BBC	222	111	000	0	0
44.788202	0.655162	02590916+0039185	15.973	0.089	15.531	0.122	14.965	0.137	ABB	222	111	000	0	0
44.806605	0.671498	02591358+0040173	15.770	0.071	15.275	0.080	15.149	0.155	AAB	222	111	000	0	0
44.771958	0.697915	02590526+0041524	16.373	0.126	15.634	0.170	15.013	0.149	BCB	222	111	000	0	0
44.794553	0.719519	02591069+0043102	16.459	0.131	16.263	0.219	16.233	--	BDU	220	110	000	0	0
44.816167	0.725490	02591588+0043317	14.996	0.047	14.406	0.030	14.239	0.071	AAA	222	111	000	0	0
44.461157	0.461605	02575067+0027417	15.449	0.059	14.891	0.070	14.732	0.087	AAA	222	111	000	0	0
44.445146	0.474037	02574683+0028265	16.127	0.113	15.826	0.166	15.801	0.242	BCD	222	111	000	0	0
44.459680	0.483199	02575032+0028595	14.214	0.032	13.861	0.036	13.713	0.040	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 32 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 25 column(s) and 50 row(s) 
	'6:I/345/rrlyrae' with 23 column(s) and 50 row(s) 
	'7:I/345/lpv' with 13 column(s) and 50 row(s) 
	'8:I/345/varres' with 9 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
43.37567140984	0.0787	1.64767103696	0.0624	1199434042673152	0.2597	0.0806	5.630	0.180	-11.762	0.137	0	3953	5.578	16.6960	0.0015	2269	15.95	16.9616	0.0076	2538	15.98	16.2506	0.0068	0.7110	--	--	6178.67	0.2280	0.1035	--	--
43.37501305522	2.3985	1.65561930146	1.9277	1199434042672896	--	--	--	--	--	--	1	111.4	1.406	20.5716	0.0137	65.92	8.151	20.8038	0.1343	180.1	6.338	19.1234	0.0382	1.6804	--	--	--	--	--	--	--
43.39513270047	0.1276	1.66076645797	0.1179	1199468402410624	1.2089	0.1449	2.465	0.279	-6.976	0.244	0	1314	2.141	17.8916	0.0018	398	11.42	18.8517	0.0312	1430	9.682	16.8733	0.0073	1.9783	--	--	--	--	--	--	--
43.37664436023	0.0398	1.66474921324	0.0336	1199537121887616	2.7472	0.0434	5.349	0.084	-1.891	0.072	0	1.232e+04	7.005	15.4614	0.0006	4113	17.66	16.3159	0.0047	1.211e+04	42.33	14.5544	0.0038	1.7615	--	--	3999.00	0.4920	0.2305	0.68	0.108
43.37133802402	0.0426	1.67097166249	0.0353	1199537121887488	3.0263	0.0463	-6.832	0.088	-0.149	0.076	0	1.01e+04	6.026	15.6773	0.0006	3114	17.36	16.6179	0.0061	1.042e+04	22.19	14.7171	0.0023	1.9009	--	--	3849.10	0.0730	0.0385	0.64	0.080
43.37037401008	0.0591	1.67298947920	0.0502	1199537121887232	3.1012	0.0643	-6.557	0.126	0.241	0.105	0	5374	5.161	16.3627	0.0010	1458	16.53	17.4420	0.0123	5955	17.24	15.3247	0.0031	2.1173	--	--	3629.00	0.2912	0.1525	0.55	0.047
43.28964426631	2.2481	1.61837293176	2.7001	1199571481659904	--	--	--	--	--	--	0	185.1	2.015	20.0200	0.0118	179.7	9.093	19.7150	0.0549	509.2	8.803	17.9946	0.0188	1.7204	--	--	--	--	--	--	--
43.28266478407	0.1879	1.61673564165	0.2327	1199567185943808	2.9764	0.2883	45.350	0.398	-37.403	0.396	0	839.2	1.861	18.3787	0.0024	140.3	11.59	19.9839	0.0897	1090	9.65	17.1686	0.0096	2.8153	--	--	--	--	--	--	--
43.28541334923	0.1972	1.62910580263	0.2925	1199670265163008	0.3526	0.3248	-0.637	0.421	1.528	0.446	0	842.1	2.046	18.3750	0.0026	365.5	10.56	18.9441	0.0314	739.7	13.66	17.5893	0.0201	1.3548	--	--	--	--	--	--	--
43.31147449055	0.1671	1.63908663549	0.1406	1199743280320256	0.8106	0.1816	0.198	0.363	-12.608	0.297	0	1041	2.602	18.1452	0.0027	220.1	7.794	19.4950	0.0385	1248	16.81	17.0210	0.0146	2.4740	--	--	--	--	--	--	--
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
43.16260585484	0.6304	1.63909018647	0.6567	1201560050910976	0.5479	0.7111	17.964	1.262	-4.538	1.243	0	197.1	1.535	19.9519	0.0085	96.65	17.38	20.3884	0.1952	243.9	19.17	18.7938	0.0853	1.5946	--	--	--	--	--	--	--
43.18328248057	8.6373	1.65409284989	2.3734	1201598706261248	--	--	--	--	--	--	0	89.12	1.449	20.8134	0.0177	58.07	12.11	20.9414	0.2264	59.16	16.44	20.3319	0.3018	0.6095	--	--	--	--	--	--	--
43.21840897363	0.0617	1.65948241460	0.0700	1201667425669504	0.4540	0.0731	3.966	0.124	3.945	0.133	0	5465	4.957	16.3444	0.0010	2883	17.56	16.7017	0.0066	3788	20.21	15.8160	0.0058	0.8857	--	--	5635.63	0.2535	0.1298	--	--
43.21564596936	0.4823	1.66024893820	0.5461	1201663129995264	1.4655	0.5736	2.869	1.014	0.636	1.094	0	234.6	1.388	19.7625	0.0064	91.28	10.27	20.4504	0.1221	275.5	9.663	18.6617	0.0381	1.7887	--	--	--	--	--	--	--
43.20386956231	0.1152	1.66081348887	0.1243	1201667425669888	0.2775	0.1406	1.284	0.249	-2.669	0.260	0	1856	2.826	17.5168	0.0017	992.7	36.83	17.8593	0.0403	1309	14.67	16.9699	0.0122	0.8894	--	--	--	--	--	--	--
43.12139599208	17.1743	1.61414835070	8.1939	1201804864208000	--	--	--	--	--	--	1	112.6	2.928	20.5597	0.0282	27.37	15.66	21.7583	0.6211	266.1	12.32	18.6994	0.0503	3.0589	--	--	--	--	--	--	--
43.14339449010	0.0270	1.65068368722	0.0263	1202384684461440	3.3337	0.0319	54.576	0.056	-61.800	0.055	0	4.091e+04	14.45	14.1587	0.0004	1.731e+04	30.26	14.7556	0.0019	3.394e+04	107.3	13.4351	0.0034	1.3204	--	--	5049.69	--	--	0.52	0.159
43.09609491604	0.0432	1.63648124122	0.0364	1202075446817152	2.8015	0.0453	19.838	0.091	-14.500	0.075	0	5.243e+05	327.6	11.3894	0.0007	2.91e+05	253	11.6918	0.0009	3.361e+05	390.1	10.9459	0.0013	0.7460	-2.33	0.66	5926.00	0.4810	0.2410	1.53	2.618
43.09701024614	0.1373	1.64733321106	0.1376	1202075446851968	1.9128	0.1646	-6.063	0.273	1.246	0.247	0	1598	3.348	17.6797	0.0023	398.2	5.162	18.8510	0.0141	1856	16.5	16.5906	0.0097	2.2604	--	--	--	--	--	--	--
43.07008499536	2.3747	1.64028324280	6.6744	1202109806879360	--	--	--	--	--	--	0	91.25	2.553	20.7878	0.0304	67.64	37.05	20.7759	0.5948	147.4	29.38	19.3404	0.2164	1.4355	--	--	--	--	--	--	--

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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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	--	--	--	--	--
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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
...	...	...	...	...	...
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()

_images/0a0e01f3d8a607288293c50a2a2b0d7a9ad9883535b2a0a4b90338be41d8202f.png

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

Contents

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

Introduction#

Step 1: Finding the images#

Step 2: Create a MOC of the MASH images#

Organising data#

Create the MOC#

Plot the MOC#

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

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

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

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

Step 7: Build a color-color diagram#