TY - JOUR
T1 - Finding AGN remnant candidates based on radio morphology with machine learning
AU - Mostert, Rafaël I.J.
AU - Morganti, Raffaella
AU - Brienza, Marisa
AU - Duncan, Kenneth J.
AU - Oei, Martijn S.S.L.
AU - Röttgering, Huub J.A.
AU - Alegre, Lara
AU - Hardcastle, Martin J.
AU - Jurlin, Nika
N1 - Funding Information:
The data and code used to produce the results and figures of this paper are available at: https://lofar-surveys.org/finding_agn_remnants.html . This research has made use of the Astropy (Astropy Collaboration 2018) and the scikit-learn (Pedregosa et al. 2011) Python packages. The authors also acknowledge the usefulness of the PINK ( https://github.com/HITS-AIN/PINK ) and HaralickFeatures ( https://github.com/KushathaNtwaetsile/HaralickFeatures ) implementations. LOFAR data products were provided by the LOFAR Surveys Key Science project (LSKSP; https://lofar-surveys.org/ ) and were derived from observations with the International LOFAR Telescope (ILT). LOFAR (van Haarlem etal. 2013) is the Low Frequency Array designed and constructed by ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and which are collectively operated by the ILT foundation under a joint scientific policy. The efforts of the LSKSP have benefited from funding from the European Research Council, NOVA, NWO, CNRS-INSU, the SURF Co-operative, the UK Science and Technology Funding Council and the Jülich Supercomputing Centre. M.B. acknowledges financial support from INAF under the SKA/CTA PRIN “FORECaST”, from the agreement ASI-INAF no. 2017-14-H.O and from the PRIN MIUR 2017PH3WAT “Blackout”. M.S.S.L. Oei acknowledges support from the VIDI research programme with project number 639.042.729, which is financed by The Netherlands Organisation for Scientific Research (NWO). M.J.H. acknowledges support from the UK STFC [ST/V000624/1]. L.A. is grateful for support from UK STFC via CDT studentship grant ST/P006809/1.
Publisher Copyright:
© The Authors 2023.
PY - 2023/6/23
Y1 - 2023/6/23
N2 - 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 â AGN remnant candidateâ. 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.
AB - 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 â AGN remnant candidateâ. 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.
KW - Methods: data analysis
KW - Radio continuum: galaxies
KW - Surveys
UR - http://www.scopus.com/inward/record.url?scp=85163490435&partnerID=8YFLogxK
U2 - 10.1051/0004-6361/202346035
DO - 10.1051/0004-6361/202346035
M3 - Article
AN - SCOPUS:85163490435
SN - 0004-6361
VL - 674
SP - 1
EP - 21
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A208
ER -