Automated Cross-identifying Radio to Infra-red Surveys Using the LRPY Algorithm: A Case Study
Weston, S; Seymour, N; Gulyaev, S; Norris, RP; Banfield, J; Vaccari, M; Hopkins, AM; Franzen, TMO
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Cross-identifying complex radio sources with optical or infra red (IR) counterparts in surveys such as the Australia Telescope Large Area Survey (ATLAS) has tradi- tionally been performed manually. However, with new surveys from the Australian Square Kilometre Array Path nder (ASKAP) detecting many tens of million of ra- dio 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 modi cation 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 multi-wavelength fusion catalogue, identifying 3,848 matched sources via our LR-based selection criteria. A subset of 1987 sources have ux density values for all IRAC bands which allow us to use criteria to distinguish between active galactic nuclei (AGN) and star-forming galaxies (SFG). We nd that 936 radio sources ( 47 %) 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 ux ratio vs redshift, proposing an AGN selection criterion below the Elvis radio-loud (RL) AGN limit for this dataset. Taking the union of all three AGN selection criteria we identify 956 as AGN ( 48 %). From this dataset, we nd a decreasing fraction of AGN with lower radio ux densities consistent with other results in the literature.