Automated Cross-identifying Radio to Infra-red Surveys Using the LRPY Algorithm: A Case Study

Abstract

Cross-identifying complex radio sources with optical or infra red (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 (ASKAP) detecting many tens of million 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 {\it Spitzer}-based multi-wavelength fusion catalogue, identifying 3,848 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 (AGN) and star-forming galaxies (SFG). We find that 936 radio sources ($\approx 47\%$) 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 flux 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 ($\approx 48\%$). From this dataset, we find a decreasing fraction of AGN with lower radio flux densities consistent with other results in the literature.

arXiv comment32 pages, 14 Figures, 9 Tables, accepted for publication in MNRAS

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• The GLEAM 4-Jy (G4Jy) Sample: II. Host-galaxy identification for individual sources
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• A machine learning classifier for LOFAR radio galaxy cross-matching techniques
• Radio AGN Selection and Characterization in Three Deep-Drilling Fields of the Vera C. Rubin Observatory Legacy Survey of Space and Time
• Optimal Probabilistic Catalogue Matching for Radio Sources
• Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey Using Deep Learning Combined with Visual Inspection