Automated Cross-identifying Radio to Infra-red Surveys Using the LRPY
  Algorithm: A Case Study
  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 ($\approx47\,\%$) 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
($\approx48\,\%$). From this dataset, we find a decreasing fraction of AGN with
lower radio flux densities consistent with other results in the literature.
