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

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)≥10 42 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.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2024-12-12
DOI
https://doi.org/10.1051/0004-6361/202347566
Citations
1
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
0.33

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article

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

L. K. Hunt, D. J. Rosario, I. García-Bernete, Cláudio Ricci et al.
1 citations
Astronomy and Astrophysics
Galaxies: Formation, Evolution, Phenomena
0.33
article

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

L. K. Hunt, D. J. Rosario, I. García-Bernete, Cláudio Ricci, A. Alonso‐Herrero, C. Ramos Almeida, E. K. S. Hicks, A. Audibert, S. García‐Burillo, R. Davies, B. García‐Lorenzo, M. J. Ward, M. Villar-Martı́n, Masatoshi Imanishi, David Cornu, D. Rigopoulou, Peter Boorman, Françoise Combes, M. Pereira-Santaella, R. Poitevineau, Daniel Rouan, E. Bellocchi, A. Bunker, O. González-Martín, T. Díaz-Santos, S. F. Hönig
article en
1 citations

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)≥10 42 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.

Astronomy and AstrophysicsVol. 693
National Aeronautics and Space Administration, Space Telescope Science Institute, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Ministerio de Ciencia e Innovación, Agencia Nacional de Investigación y Desarrollo, Observatoire de Paris, Université de Recherche Paris Sciences et Lettres, European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 33%
Galaxies: Formation, Evolution, Phenomena
0.33
DOIOpen Access PDFOpenAlex
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