J/MNRAS/473/4523 ATLAS/FUSION SWIRE CDFS and ELAIS-S1 fields (Weston+, 2018)
Automated cross-identifying radio to infrared surveys using the LRPY algorithm:
a case study.
Weston S.D., Seymour N., Gulyaev S., Norris R.P., Banfield J., Vaccari M.,
Hopkins A.M., Franzen T.M.O.
<Mon. Not. R. Astron. Soc., 473, 4523-4537 (2018)>
=2018MNRAS.473.4523W 2018MNRAS.473.4523W (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Galaxies, radio
Keywords: methods: data analysis - catalogues - surveys -
radio continuum: galaxies
Abstract:
Cross-identifying complex radio sources with optical or infrared (IR)
counterparts in surveys such as the Australia Telescope Large Area
Survey (ATLAS) has traditionally been performed manually. However,
with new surveys from the Australian Square Kilometre Array Pathfinder
detecting many tens of millions of radio sources, such an approach is
no longer feasible. This paper presents new software (LRPY -
Likelihood Ratio in PYthon) to automate the process of
cross-identifying radio sources with catalogues at other wavelengths.
lrpy implements the likelihood ratio (LR) technique with a
modification to account for two galaxies contributing to a sole
measured radio component. We demonstrate lrpy by applying it to ATLAS
DR3 and a Spitzer-based multiwavelength fusion catalogue, identifying
3848 matched sources via our LR-based selection criteria. A subset of
1987 sources have flux density values for all IRAC bands which allow
us to use criteria to distinguish between active galactic nuclei
(AGNs) and star-forming galaxies (SFG). We find that 936 radio sources
(~=47 per cent) meet both of the Lacy and Stern AGN selection
criteria. Of the matched sources, 295 have spectroscopic redshifts and
we examine the radio to IR flux ratio versus redshift, proposing an
AGN selection criterion below the Elvis radio-loud AGN limit for this
dataset. Taking the union of all three AGNs selection criteria we
identify 956 as AGNs (~= 48 per cent). From this dataset, we find a
decreasing fraction of AGNs with lower radio flux densities consistent
with other results in the literature.
Description:
This work and analysis concentrates on the ATLAS DR3 catalogue
(Franzen et al., 2015, Cat. J/MNRAS/453/4020) and the Spitzer Data
Fusion catalogue (Vaccari et al. 2010A&A...518L..20V 2010A&A...518L..20V; Vaccari 2015,
http://mattiavaccari.net/df/).
File Summary:
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FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table7.dat 100 3230 ATLAS/FUSION SWIRE cross-identification
catalogue for the CDFS field
table8.dat 100 2367 ATLAS/FUSION SWIRE cross-identification
catalogue for the ELAIS-S1 field
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See also:
J/MNRAS/453/4020 : ATLAS 1.4GHz Data Release 3 (Franzen+, 2015)
http://mattiavaccari.net/df : Spitzer Data Fusion,
A Spitzer-Selected Multi-Wavelength Catalog
Byte-by-byte Description of file: table7.dat table8.dat
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Bytes Format Units Label Explanations
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1- 8 A8 --- ATLAS ATLAS DR3 Identification number of the radio
source
10- 16 F7.4 deg RAdeg Radio source right ascension (J2000)
18- 25 F8.4 deg DEdeg Radio source declination (J2000)
27- 37 F11.7 mJy/beam Sp1.4GHz ?=-999 Peak radio flux densities at 1.4GHz
39- 48 F10.5 mJy Si1.4GHz ?=-999 Integrated radio flux densities
at 1.4GHz
50- 55 I6 --- SWIRE Fusion Identification number,
swireindexspitzer
57- 63 F7.4 deg RAIdeg IR candidate right ascension (J2000)
65- 72 F8.4 deg DEIdeg IR candidate declination (J2000)
74- 81 F8.2 uJy S3.6um IR flux density at 3.6um
83- 87 F5.2 uJy e_S3.6um IR flux density uncertainty at 3.6um
89- 94 F6.3 [-] logLR Log10 of likelihood ratio of the IR candidate
96-100 F5.3 --- Rel Reliability of the IR candidate
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 25-Nov-2020