A multi-band AGN-SFG classifier for extragalactic radio surveys using machine learning^★

¹ Kapteyn Astronomical Institute, University of Groningen, Groningen 9747, AD, The Netherlands
e-mail: jesper.karsten1999@gmail.com; karsten@astro.rug.nl
² SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD, Groningen, The Netherlands
³ Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK
⁴ ASTRON, the Netherlands Institute for Radio Astronomy, Oude Hoogeveensedijk 4, 7991 PD Dwingeloo, The Netherlands
⁵ Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
⁶ Inter-University Institute for Data Intensive Astronomy, Department of Astronomy, University of Cape Town, 7701 Rondebosch, Cape Town, South Africa
⁷ Inter-University Institute for Data Intensive Astronomy, Department of Physics and Astronomy, University of the Western Cape, Robert Sobukwe Road, 7535 Bellville, Cape Town, South Africa
⁸ INAF - Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy
⁹ UK Astronomy Technology Centre, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK

Received: 28 April 2023
Accepted: 5 June 2023

Abstract

Context. Extragalactic radio continuum surveys play an increasingly more important role in galaxy evolution and cosmology studies. While radio galaxies and radio quasars dominate at the bright end, star-forming galaxies (SFGs) and radio-quiet active galactic nuclei (AGNs) are more common at fainter flux densities.

Aims. Our aim is to develop a machine-learning classifier that can efficiently and reliably separate AGNs and SFGs in radio continuum surveys.

Methods. We performed a supervised classification of SFGs versus AGNs using the light gradient boosting machine (LGBM) on three LOFAR Deep Fields (Lockman Hole, Boötes, and ELAIS-N1), which benefit from a wide range of high-quality multi-wavelength data and classification labels derived from extensive spectral energy distribution (SED) analyses.

Results. Our trained model has a precision of 0.92±0.01 and a recall of 0.87±0.02 for SFGs. For AGNs, the model performs slightly worse, with a precision of 0.87±0.02 and a recall of 0.78±0.02. These results demonstrate that our trained model can successfully reproduce the classification labels derived from a detailed SED analysis. The model performance decreases towards higher redshifts, which is mainly due to smaller training sample sizes. To make the classifier more adaptable to other radio galaxy surveys, we also investigate how our classifier performs with a poorer multi-wavelength sampling of the SED. In particular, we find that the far-infrared and radio bands are of great importance. We also find that a higher signal-to-noise ratio in some photometric bands leads to a significant boost in the model performance. In addition to using the 150 MHz radio data, our model can also be used with 1.4 GHz radio data. Converting 1.4 GHz to 150 MHz radio data reduces the performance by ~4% in precision and ~3% in recall.