The Rapid ASKAP Continuum Survey: Stokes I Source Catalogue Data Release 1
The Rapid ASKAP Continuum Survey (RACS) is the first large sky survey using ASKAP, covering the sky south of +41 deg declination. RACS observed the entire sky at a central frequency of 887.5 MHz using 903 individual pointings with 15 minute observations. This has resulted in the deepest radio survey of the full Southern sky to date at these frequencies, with initial pipeline images and catalogues of each field released in CASDA in December 2020 as part of McConnell et al. 2020.
The first Stokes I catalogues have now been derived from the RACS survey. These catalogues were assembled from 799 tiles that are convolved to a common resolution of 25′′, covering a large contiguous region in the declination range δ=−80∘ to +30∘. The extragalactic catalogue consists of ∼2.1 million sources. The catalogues are separated into galactic and non-galactic regions. Source island and component catalogues are provided for both regions. The 799 images and corresponding rms maps are also made available.
The methods to obtain this catalogue from the initial RACS observations are described in Hale et al. 2021 (https://arxiv.org/abs/2109.00956)
The four catalogues are:
racs_dr1_gaussians_galacticcut_v2021_08_v01: |b| > 5 deg, Gaussian component catalogue
racs_dr1_gaussians_galacticregion_v2021_08_v01: |b| < 5 deg, Gaussian component catalogue
racs_dr1_sources_galacticcut_v2021_08_v01: |b| > 5 deg, Source or island catalogue
racs_dr1_sources_galacticregion_v2021_08_v01: |b| < 5 deg, Source or island catalogue
The full dataset from Hale et al. 2021, images and catalogues are available here: https://doi.org/10.25919/8zyw-5w85
FAQ
Q. How do I do a cone search against these new RACS DR1 catalogues?
A. To do a cone search for individual RACS catalogue radio sources you can use TOPCAT or Python Astroquery.
TOPCAT Cone Search query
In TOPCAT click on VO -> Cone Search.
In the Cone URL box (near bottom of TOPCAT Cone Search window) put in “https://casda.csiro.au/casda_vo_tools/scs/racs_dr1_gaussians_galacticcut_v2021_08_v01?”
You can then search using the TOPCAT Cone Search UI. Choose Verbosity level of “3 (maximum)” to return the full set of columns.
TOPCAT TAP query
In TOPCAT click on VO -> TAP Query. Input “casda” into keywords to search for the CASDA TAP service, or put “https://casda.csiro.au/casda_vo_tools/tap” directly into the TAP URL box and press “Use Service”.
Use the following TAP ADQL query as an example:
SELECT * FROM AS110.racs_dr1_gaussians_galacticcut_v2021_08_v01 where 1=CONTAINS(POINT(‘ICRS’, ra, dec),CIRCLE(‘ICRS’,183.475,11.236,0.01))
The above example is for RA = 183.475, Dec = 11.236 and search radius of 0.01 deg of the extragalactic Gaussian component catalogue.
(Note that you can run any generalised ADQL query, e.g. search for sources with RA and Dec range, or flux density constraints, etc. The above is just an example).
Python Astroquery
In Python use the astroquery TAP module to run the TAP ADQL query with:
from astroquery.utils.tap.core import TapPlus
casdatap = TapPlus(url=”https://casda.csiro.au/casda_vo_tools/tap”)
job = casdatap.launch_job_async(“SELECT * FROM AS110.racs_dr1_gaussians_galacticcut_v2021_08_v01 where 1=CONTAINS(POINT(‘ICRS’, ra, dec),CIRCLE(‘ICRS’,183.475,11.236,0.01))”)
r = job.get_results()
Q. How do I use the UI to find these images?
A. In the CASDA Observation Search web form:
i) To return all images: search for “AS110” project and filename “*DR1*A.fits”. Leave other options blank.
ii) To return all RMS images: search for “AS110” project and filename “*DR1*A_RMS.fits”. Leave other options blank.
iii) Combine the above with a cone search to return an image of a position of interest.
Q. Why can’t I download the data?
A. Please make sure you login with an OPAL account.
Q. How do I generate scripted cutouts of these RACS DR1 25-arcsec-convolved images?
A.
New answer:
The CASDA Python astroquery module now has a cutout function. Please see https://astroquery.readthedocs.io/en/latest/casda/casda.html for documentation on this API.
Old answer:
Start with the python code from our github repo of python examples.
Use cutouts_by_proj.py and modify the adql query.
i.e. Change
data_product_id_query = “select * from ivoa.obscore where obs_collection
LIKE ‘%” + proj + \
“%’ and dataproduct_subtype = ‘cont.restored.t0’ and
pol_states = ‘/I/’ and 1 = CONTAINS(POINT(‘ICRS’,”+ str(ra) + “,”+ str(dec) + “),s_region)”
to
data_product_id_query = “select * from ivoa.obscore where obs_collection
LIKE ‘%” + proj + \
“%’ and dataproduct_type = ‘cube’ and
calib_level = 3 and filename LIKE ‘RACS%DR1%A.fits’ and 1 = CONTAINS(POINT(‘ICRS’,”+ str(ra) + “,”+ str(dec) + “),s_region)”
The above is for the Stokes images, for RMS maps change the filename parameter accordingly.
Then you run the cutout script you have updated with (for example):
python cutouts_by_proj_mod.py OPALusername Rapid source_list.txt output_dir 0.1
Figure 2 from Hale et al. 2021
