Finding AGN remnant candidates based on radio morphology with machine learning

¹ Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
e-mail: mostert@strw.leidenuniv.nl
² ASTRON, the Netherlands Institute for Radio Astronomy, Oude Hoogeveensedijk 4, 7991 PD Dwingeloo, The Netherlands
³ Kapteyn Astronomical Institute, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands
⁴ INAF – Osservatorio di Astrofísica e Scienza dello Spazio di Bologna, Via P. Gobetti 93/3, 40129 Bologna, Italy
⁵ Dipartimento di Fisica e Astronomía, Università di Bologna, via P. Gobetti 93/2, 40129, Bologna, Italy
⁶ INAF – Istituto di Radioastronomia, Bologna Via Gobetti 101, 40129 Bologna, Italy
⁷ Institute for Astronomy, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
⁸ Centre for Astrophysics Research, Department of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
⁹ Department of Astronomy, The University of Texas at Austin, 2515 Speedway, Stop C1400, Austin, TX 78712-1205, USA

Received: 31 January 2023
Accepted: 11 April 2023

Abstract

Context. Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio-galaxy life cycle. The remnants of radio-loud AGN can be identified in radio sky surveys based on their spectral index, and identifications can be confirmed through visual inspection based on their radio morphology. However, this latter confirmation process is extremely time-consuming when applied to the new large and sensitive radio surveys.

Aims. Here, we aim to reduce the amount of visual inspection required to find AGN remnants based on their morphology using supervised machine learning trained on an existing sample of remnant candidates.

Methods. For a dataset of 4107 radio sources with angular sizes of larger than 60 arcsec from the LOw Frequency ARray (LOFAR) Two-Metre Sky Survey second data release (LoTSS-DR2), we started with 151 radio sources that were visually classified as ‘AGN remnant candidate’. We derived a wide range of morphological features for all radio sources from their corresponding Stokes-I images: from simple source-catalogue-derived properties to clustered Haralick-features and self-organising-map(SOM)-derived morphological features. We trained a random forest classifier to separate the AGN remnant candidates from the yet-to-be inspected sources.

Results. The SOM-derived features and the total-to-peak flux ratio of a source are shown to have the greatest influence on the classifier. For each source, our classifier outputs a positive prediction, if it believes the source to be a likely AGN remnant candidate, or a negative prediction. The positive predictions of our model include all initially inspected AGN remnant candidates, plus a number of yet-to-be inspected sources. We estimate that 31 ± 5% of sources with positive predictions from our classifier will be labelled AGN remnant candidates upon visual inspection, while we estimate the upper bound of the 95% confidence interval for AGN remnant candidates in the negative predictions to be 8%. Visual inspection of just the positive predictions reduces the number of radio sources requiring visual inspection by 73%.

Conclusions. This work shows the usefulness of SOM-derived morphological features and source-catalogue-derived properties in capturing the morphology of AGN remnant candidates. The dataset and method outlined in this work bring us closer to the automatic identification of AGN remnant candidates based on radio morphology alone and the method can be used in similar projects that require automatic morphology-based classification in conjunction with small labelled sample sizes.