The Galaxy Activity, Torus, and Outflow Survey (GATOS)

VI. Black hole mass estimation using machine learning

¹ Observatoire de Paris, LERMA, PSL University, Sorbonne Université, CNRS, F-75014 Paris, France
² Institute of Physics, Laboratory of Astrophysics, École Polytechnique Fédérale de Lausanne (EPFL), 1290 Sauverny, Switzerland
³ Collège de France, 11 Place Marcelin Berthelot, 75231 Paris, France
⁴ Observatorio Astronomico Nacional (OAN-IGN)-Observatorio de Madrid, Alfonso XII, 3, 28014 Madrid, Spain
⁵ Centro de Astrobiología (CAB), CSIC-INTA, Camino Bajo del Castillo s/n, E-28692 Villanueva de la Cañada, Madrid, Spain
⁶ Instituto de Astrofísica de Canarias (IAC), Calle Vía Láctea, s/n, 38205 La Laguna, Tenerife, Spain
⁷ Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
⁸ Departamento de Física de la Tierra y Astrofísica, Fac. de CC Físicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
⁹ Instituto de Física de Partículas y del Cosmos IPARCOS, Fac. CC Físicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
¹⁰ Astronomical Institute, Academy of Sciences, Bôcní II 1401, 14131 Prague, Czech Republic
¹¹ Department of Physics, University of Oxford, Oxford OX1 3RH, UK
¹² Max-Planck-Institut für Extraterrestrische Physik, Postfach 1312, 85741 Garching, Germany
¹³ Institute of Astrophysics, Foundation for Research and Technology- Hellas, 71110 Heraklion, Greece
¹⁴ School of Sciences, European University Cyprus, Diogenes Street, Engomi, 1516 Nicosia, Cyprus
¹⁵ Instituto de Radioastronomía y Astrofísica (IRyA), Universidad Nacional Autónoma de México, Antigua Carretera a Pátzcuaro #8701, Ex-Hda. San José de la Huerta, C.P. 58089 Morelia, Michoacán, Mexico
¹⁶ Department of Physics & Astronomy, University of Alaska Anchorage, Anchorage, AK 99508-4664, USA
¹⁷ School of Physics & Astronomy, University of Southampton, Southampton, SO17 1BJ Hampshire, UK
¹⁸ INAF – Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi 5, 50125 Firenze, Italy
¹⁹ National Astronomical Observatory of Japan, National Institutes of Natural Sciences (NINS), 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan
²⁰ Department of Astronomy, School of Science, Graduate University for Advanced Studies (SOKENDAI), Mitaka, Tokyo 181-8588, Japan
²¹ Instituto de Física Fundamental, CSIC, Calle Serrano 123, 28006 Madrid, Spain
²² Instituto de Estudios Astrofísicos, Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago, Chile
²³ Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, China
²⁴ School of Mathematics, Statistics, and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
²⁵ LESIA, Observatoire de Paris, PSL Research University, CNRS, Sorbonne Université, Paris-Cité University, 5 Place Jules Janssen, 92190 Meudon, France
²⁶ Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham DH1 3LE, UK

^⋆ Corresponding author; remi.poitevineau@epfl.ch

Received: 26 July 2023
Accepted: 23 November 2024

Abstract

The detailed feeding and feedback mechanisms of active galactic nuclei (AGNs) are not yet well known. For low-luminosity AGNs, obscured AGNs, and late-type galaxies, the masses of their central black holes (BH) are difficult to determine precisely. Our goal with the GATOS sample is to study the circum-nuclear regions and, in the present work, to better determine their BH mass, with more precise and accurate estimations than those obtained from scaling relations. We used the high spatial resolution of ALMA to resolve the CO(3–2) emission within ∼100 pc around the supermassive black hole (SMBH) of seven GATOS galaxies and try to estimate their BH mass when enough gas is present in the nuclear regions. We studied the seven bright (L_AGN(14 − 150 keV)≥10⁴² erg/s) and nearby (< 28 Mpc) galaxies from the GATOS core sample. For the sake of comparison, we first searched the literature for previous BH mass estimations. We also made additional estimations using the M_BH–σ relation and the fundamental plane of BH activity. We developed a new method using supervised machine learning to estimate the BH mass either from position-velocity diagrams or from first-moment maps computed from ALMA CO(3–2) observations. We used numerical simulations with a large range of parameters to create the training, validation, and test sets. Seven galaxies had sufficient gas detected, thus, we were able to make a BH estimation from the ALMA data: NGC 4388, NGC 5506, NGC 5643, NGC 6300, NGC 7314, NGC 7465, and NGC 7582. Our BH masses range from 6.39 to 7.18 log(M_BH/M_⊙) and are consistent with the previous estimations. In addition, our machine learning method has the advantage of providing a robust estimation of errors with confidence intervals. The method has also more growth potential than scaling relations. This work represents the first step toward an automatized method for estimating M_BH using machine learning.