Preliminary Results of Using k-nearest-neighbor Regression to Estimate the Redshift of Radio-selected Data Sets

In the near future, all-sky radio surveys are set to produce catalogues of tens of millions of sources with limited multiwavelength photometry. Spectroscopic redshifts will only be possible for a small fraction of these new-found sources. In this paper, we provide the first in-depth investigation into the use of k-nearest-neighbor (kNN) regression for the estimation of redshift of these sources. We use Australia Telescope Large Area Survey (ATLAS) radio data, combined with Spitzer Wide-Area Infrared Extragalactic Survey infrared, Dark Energy Survey optical, and Australian Dark Energy Survey spectroscopic survey data. We then reduce the depth of photometry to match what is expected from the upcoming Evolutionary Map of the Universe survey, testing against both data sets. To examine the generalization of our methods, we test one of the subfields of ATLAS against the other. We achieve an outlier rate of ∼10% across all tests, showing that the kNN regression algorithm is an acceptable method of estimating redshift, and would perform better given a sample training set with uniform redshift coverage.