Searching for 2MASS All-Sky Atlas Images

This notebook tutorial demonstrates the process of querying IRSA’s Simple Image Access (SIA) service for the 2MASS All-Sky Atlas, making a cutout image (thumbnail), and displaying the cutout.

Learning Goals¶

By the end of this tutorial, you will:

• Learn how to search the NASA Astronomical Virtual Observatory Directory web portal for a service that provides access to IRSA’s 2MASS images.
• Use the Python pyvo package to identify which of IRSA’s 2MASS images cover a specified coordinate.
• Download one of the identified images.
• Create and display a cutout of the downloaded image.

Introduction¶

The Two Micron All Sky Survey (2MASS) project uniformly scanned the entire sky in three near-infrared bands to detect and characterize point sources brighter than about 1 mJy in each band, with signal-to-noise ratio (SNR) greater than 10. More information about 2MASS can be found at:

https://irsa.ipac.caltech.edu/Missions/2mass.html

The NASA/IPAC Infrared Science Archive (IRSA) at Caltech is the archive for 2MASS images and catalogs. The 2MASS images that are the subject of this tutorial are made accessible via the International Virtual Observatory Alliance (IVOA) Simple Image Access (SIA) protocol. IRSA’s 2MASS SIA service is registered in the NASA Astronomical Virtual Observatory (NAVO) Directory. Based on the registered information, the Python package pyvo can be used to query the 2MASS SIA service for a list of images that meet specified criteria, and standard Python libraries can be used to download and manipulate the images. Other datasets at IRSA are available through other SIA services:

https://irsa.ipac.caltech.edu/docs/program_interface/api_images.html

Imports¶

• pyvo for querying IRSA’s 2MASS SIA service
• astropy.coordinates for defining coordinates
• astropy.nddata for creating an image cutout
• astropy.wcs for interpreting the World Coordinate System header keywords of a fits file
• astropy.units for attaching units to numbers passed to the SIA service
• matplotlib.pyplot for plotting
• astropy.utils.data for downloading files
• astropy.io to manipulate FITS files

# Uncomment the next line to install dependencies if needed.
# !pip install matplotlib astropy pyvo

import pyvo as vo
from astropy.coordinates import SkyCoord
from astropy.nddata import Cutout2D
from astropy.wcs import WCS
import astropy.units as u
import matplotlib.pyplot as plt
from astropy.utils.data import download_file
from astropy.io import fits

Section 1 - Setup¶

Set images to display in the notebook

%matplotlib inline

Define coordinates of a bright star

ra = 314.30417
dec = 77.595559
pos = SkyCoord(ra=ra, dec=dec, unit='deg')

Section 2 - Lookup and define a service for 2MASS images¶

Start at STScI VAO Registry at https://vao.stsci.edu/keyword-search/

Limit by Publisher “NASA/IPAC Infrared Science Archive” and Capability Type “Simple Image Access Protocol” then search on “2MASS”

Locate the SIA URL https://irsa.ipac.caltech.edu/cgi-bin/2MASS/IM/nph-im_sia?type=at&ds=asky&

twomass_service = vo.dal.SIAService("https://irsa.ipac.caltech.edu/cgi-bin/2MASS/IM/nph-im_sia?type=at&ds=asky&")

Section 3 - Search the service¶

Search for images covering within 1 arcsecond of the star

im_table = twomass_service.search(pos=pos, size=1.0*u.arcsec)

Examine the table of images that is returned

im_table.to_table()

Section 4 - Locate and download an image of interest¶

Locate the first H-band image and display its URL

for i in range(len(im_table)):
    if im_table[i]['band'] == 'H':
        break
print(im_table[i].getdataurl())

https://irsa.ipac.caltech.edu:443/cgi-bin/2MASS/IM/nph-im?ds=asky&atdir=/ti08&dh=000616n&scan=060&name=hi0600256.fits

Download the image and open it in Astropy

fname = download_file(im_table[i].getdataurl(), cache=True)
image1 = fits.open(fname)

Section 5 - Extract a cutout and plot it¶

wcs = WCS(image1[0].header)

cutout = Cutout2D(image1[0].data, pos, (60, 60), wcs=wcs)
wcs = cutout.wcs

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection=wcs)
ax.imshow(cutout.data, cmap='gray_r', origin='lower',
          vmax = 1000)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

Exercise¶

Repeat the steps above to retrieve a cutout from the AllWISE Atlas images

About this notebook¶

Author: David Shupe, IRSA Scientist, and the IRSA Science Team

Updated: 2023-02-16

Contact: the IRSA Helpdesk with questions or reporting problems.

Citations¶

If you use astropy for published research, please cite the authors. Follow these links for more information about citing astropy:

• Citing astropy

If you use 2MASS data in published research, please cite the canonical paper Skrutskie et al (2006), and include the following standard acknowledgment:

“This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.”

Please also cite the doi for the 2MASS All-Sky Atlas Image Service at IPAC (2020)