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astropy / astroquery

Functions and classes to access online data resources. Maintainers: @keflavich and @bsipocz and @ceb8
http://astroquery.readthedocs.org/en/latest/
BSD 3-Clause "New" or "Revised" License
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Astroquery's CASDA query for RACS-MID DR1 cutout images #3001

Closed astro-fermi closed 6 months ago

astro-fermi commented 6 months ago

Hello,

I am trying to download the cutout image for an object using the RACS-MID DR1 catalog, which was recently released (https://ui.adsabs.harvard.edu/abs/2024PASA...41....3D/abstract). I followed the example on Astroquery's CASDA website (https://astroquery.readthedocs.io/en/latest/casda/casda.html#cutouts). Since I am interested in RACS-MID DR1 data rather than RACS-DR1, I replaced the command np.char.startswith(publicdata['filename'], 'RACS-DR1' with np.char.startswith(publicdata['filename'], 'RACS-MID1'

and was able to download the desired cutout. The beam size of the downloaded cutout image is 25"x25". However, when I tried to download the cutout image from the CASDA cutout website (https://data.csiro.au/domain/casdaCutoutService), the downloaded cutout fits has a beam size of 13.29" x 9.39", i.e., smaller, hence higher resolution. I cannot understand why Astroquery's CASDA module did not find this smaller beam-size cutout image. At least, it should download cutouts with all available beam sizes.

Please help. Thanks in advance.

bsipocz commented 6 months ago

cc @jd-au

jd-au commented 6 months ago

HI astro-fermi,

The RACS-mid data release at https://doi.org/10.25919/p524-xb81 consists of two sets of images, the main mosaiced images and the 25" set which is designed to match the RACS-low catalogue. So for each tile there are two files e.g.

The CASDA cutout website uses the criteria filename LIKE 'RACS-MID1%.fits' and filename NOT LIKE 'RACS-MID1%arcsec.fits' to select just the main mosaiced images from the RACS-mid dataset.

To do the same in the CASDA Astroquery module use: subset = public_data[((public_data['obs_collection'] == 'The Rapid ASKAP Continuum Survey') & # (np.char.startswith(public_data['filename'], 'RACS-MID1_')) & # (np.invert(np.char.endswith(public_data['filename'], 'arcsec.fits'))) )]

Note the use of subset[:1] in the next line restricts to just the first result from the subset list. You can remove the [:1] to produce cutouts for every match.

Cheers, James.

astro-fermi commented 6 months ago

I just checked that with the suggested commands; I can download the cutout with the smaller beam size. Many thanks for your help.