Searching for Spitzer Enhanced Imaging Products

This notebook tutorial demonstrates the process of querying IRSA’s Simple Image Access v2 (SIAv2) service for the Spitzer Enhanced Imaging Products (SEIP), 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 Spitzer Enhanced Imaging Products (SEIP) images.
• Use the Python pyvo package to identify which of IRSA’s SEIP images cover a specified coordinate.
• Download one of the identified images.
• Create and display a cutout of the downloaded image.

Introduction¶

The Spitzer Enhanced Imaging Products (SEIP) include Super Mosaics combining data from multiple programs if appropriate, and a high-reliability Source List of compact objects. The SEIP include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. More information about the SEIP can be found at:

https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/

The NASA/IPAC Infrared Science Archive (IRSA) at Caltech is the archive for SEIP images and catalogs. The SEIP 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 SEIP 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 SEIP 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 SEIP 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 SEIP images¶

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

Search on “seip”

Locate the COLLECTION=spitzer_seip from the SIA2 URL https://irsa.ipac.caltech.edu/SIA?COLLECTION=spitzer_seip&

To work with PyVO, define this SIAv2 service and use the collection argument separately

seip_service2= vo.dal.sia2.SIA2Service('https://irsa.ipac.caltech.edu/SIA')

Section 3 - Search the service¶

Search for images covering within 10 arcseconds of the star

im_table = seip_service2.search(pos=(pos.ra.deg, pos.dec.deg, 10*u.arcsec),
                                collection='spitzer_seip')

Display the table of images that is returned

im_table.to_table()

Section 4 - Locate and download an image of interest¶

Let’s search the image results for a short-exposure IRAC Ch3 mosaic.

for i in range(len(im_table)):
    if im_table[i].getdataurl().endswith('short.IRAC.3.mosaic.fits'):
        break
print(im_table[i].getdataurl())

https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/images/4/0019/40019821/9/40019821-9/40019821.40019821-9-short.IRAC.3.mosaic.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 = 10)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

Exercise¶

Search the SEIP Source List using IRSA Gator service, find an object of interest and repeat the steps above to extract cutouts in all four IRAC bands.

About this notebook¶

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

Updated: 2022-02-14

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 SEIP data in published research, please cite the dataset Digital Object Identifier (DOI): SSC and IRSA (2020).