A machine-learning classifier for LOFAR radio galaxy cross-matching techniques

Monthly Notices of the Royal Astronomical Society, vol. 516, pp. 4716–4738

Abstract

ABSTRACT New-generation radio telescopes like LOFAR are conducting extensive sky surveys, detecting millions of sources. To maximize the scientific value of these surveys, radio source components must be properly associated into physical sources before being cross-matched with their optical/infrared counterparts. In this paper, we use machine learning to identify those radio sources for which either source association is required or statistical cross-matching to optical/infrared catalogues is unreliable. We train a binary classifier using manual annotations from the LOFAR Two-metre Sky Survey (LoTSS). We find that, compared to a classification model based on just the radio source parameters, the addition of features of the nearest-neighbour radio sources, the potential optical host galaxy, and the radio source composition in terms of Gaussian components, all improve model performance. Our best model, a gradient boosting classifier, achieves an accuracy of 95 per cent on a balanced data set and 96 per cent on the whole (unbalanced) sample after optimizing the classification threshold. Unsurprisingly, the classifier performs best on small, unresolved radio sources, reaching almost 99 per cent accuracy for sources smaller than 15 arcsec, but still achieves 70 per cent accuracy on resolved sources. It flags 68 per cent more sources than required as needing visual inspection, but this is still fewer than the manually developed decision tree used in LoTSS, while also having a lower rate of wrongly accepted sources for statistical analysis. The results have an immediate practical application for cross-matching the next LoTSS data releases and can be generalized to other radio surveys.

Authors 8

1. L. Alegre corresponding

   Royal Observatory · University of Edinburgh

   Affiliation as printed

   SUPA, Institute for Astronomy, University of Edinburgh , Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

2. J. Sabater

   Royal Observatory · UK Astronomy Technology Centre · University of Edinburgh

   Affiliation as printed

   SUPA, Institute for Astronomy, University of Edinburgh , Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

   UK Astronomy Technology Centre , Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

3. P. N. Best

   Royal Observatory · University of Edinburgh

   Affiliation as printed

   SUPA, Institute for Astronomy, University of Edinburgh , Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

4. G. Gürkan

   Thüringer Landessternwarte Tautenburg

   Affiliation as printed

   Thüringer Landessternwarte Tautenburg (TLS) , Sternwarte 5, D-07778 Tautenburg, Germany

5. M. J. Hardcastle

   University of Hertfordshire

   Affiliation as printed

   Centre for Astrophysics Research, Department of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane , Hatfield AL10 9AB, UK

6. R. Kondapally

   Royal Observatory · University of Edinburgh

   Affiliation as printed

   SUPA, Institute for Astronomy, University of Edinburgh , Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

7. T. W. Shimwell Aachen Leiden Observatory

   Leiden University · Leiden Observatory · Netherlands Institute for Radio Astronomy

   Affiliation as printed

   ASTRON, the Netherlands Institute for Radio Astronomy , Oude Hoogeveensedijk 4, NL-7991 PD Dwingeloo, the Netherlands

   Leiden Observatory, Leiden University , PO Box 9513, NL-2300 RA Leiden, the Netherlands

8. Daniel J. B. Smith

   University of Hertfordshire

   Affiliation as printed

   Centre for Astrophysics Research, Department of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane , Hatfield AL10 9AB, UK