The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Poitevineau, R. Combes, F. Garcia-Burillo, S. Cornu, D. Herrero, A. Alonso Almeida, C. Ramos Audibert, A. Bellocchi, E. Boorman, P. G. Bunker, A. J. Davies, R. Díaz-Santos, T. García-Bernete, I. García-Lorenzo, B. González-Martín, O. Hicks, E. K. S. Hönig, S. F. Hunt, L. K. Imanishi, M. Pereira-Santaella, M. Ricci, C. Rigopoulou, D. Rosario, D. J. Rouan, D. Martin, M. Villar Ward, M. |
| author_facet | Poitevineau, R. Combes, F. Garcia-Burillo, S. Cornu, D. Herrero, A. Alonso Almeida, C. Ramos Audibert, A. Bellocchi, E. Boorman, P. G. Bunker, A. J. Davies, R. Díaz-Santos, T. García-Bernete, I. García-Lorenzo, B. González-Martín, O. Hicks, E. K. S. Hönig, S. F. Hunt, L. K. Imanishi, M. Pereira-Santaella, M. Ricci, C. Rigopoulou, D. Rosario, D. J. Rouan, D. Martin, M. Villar Ward, M. |
| contents | The detailed feeding and feedback mechanisms of Active Galactic Nuclei (AGN) are not yet well known. For low-luminosity and obscured AGN, as well as late-type galaxies, determining the central black hole (BH) masses is challenging. Our goal with the GATOS sample is to study circum-nuclear regions and better estimate BH masses with more precision than scaling relations offer. Using ALMA's high spatial resolution, we resolve CO(3-2) emissions within ~100 pc around the supermassive black hole (SMBH) in seven GATOS galaxies to estimate their BH masses when sufficient gas is present. We study seven bright ($L_{AGN}(14-150\mathrm{keV}) \geq 10^{42}\mathrm{erg/s}$), nearby (<28 Mpc) galaxies from the GATOS core sample. For comparison, we searched the literature for previous BH mass estimates and made additional calculations using the \mbh~ - $σ$ relation and the fundamental plane of BH activity. We developed a supervised machine learning method to estimate BH masses from position-velocity diagrams or first-moment maps using ALMA CO(3-2) observations. Numerical simulations with a wide range of parameters created the training, validation, and test sets. Seven galaxies provided enough gas for BH mass estimations: NGC4388, NGC5506, NGC5643, NGC6300, NGC7314, NGC7465, and NGC~7582. Our BH masses, ranging from 6.39 to 7.18 log$(M_{BH}/M_\odot)$, align with previous estimates. Additionally, our machine learning method provides robust error estimations with confidence intervals and offers greater potential than scaling relations. This work is a first step toward an automated \mbh estimation method using machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18200 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning Poitevineau, R. Combes, F. Garcia-Burillo, S. Cornu, D. Herrero, A. Alonso Almeida, C. Ramos Audibert, A. Bellocchi, E. Boorman, P. G. Bunker, A. J. Davies, R. Díaz-Santos, T. García-Bernete, I. García-Lorenzo, B. González-Martín, O. Hicks, E. K. S. Hönig, S. F. Hunt, L. K. Imanishi, M. Pereira-Santaella, M. Ricci, C. Rigopoulou, D. Rosario, D. J. Rouan, D. Martin, M. Villar Ward, M. Astrophysics of Galaxies The detailed feeding and feedback mechanisms of Active Galactic Nuclei (AGN) are not yet well known. For low-luminosity and obscured AGN, as well as late-type galaxies, determining the central black hole (BH) masses is challenging. Our goal with the GATOS sample is to study circum-nuclear regions and better estimate BH masses with more precision than scaling relations offer. Using ALMA's high spatial resolution, we resolve CO(3-2) emissions within ~100 pc around the supermassive black hole (SMBH) in seven GATOS galaxies to estimate their BH masses when sufficient gas is present. We study seven bright ($L_{AGN}(14-150\mathrm{keV}) \geq 10^{42}\mathrm{erg/s}$), nearby (<28 Mpc) galaxies from the GATOS core sample. For comparison, we searched the literature for previous BH mass estimates and made additional calculations using the \mbh~ - $σ$ relation and the fundamental plane of BH activity. We developed a supervised machine learning method to estimate BH masses from position-velocity diagrams or first-moment maps using ALMA CO(3-2) observations. Numerical simulations with a wide range of parameters created the training, validation, and test sets. Seven galaxies provided enough gas for BH mass estimations: NGC4388, NGC5506, NGC5643, NGC6300, NGC7314, NGC7465, and NGC~7582. Our BH masses, ranging from 6.39 to 7.18 log$(M_{BH}/M_\odot)$, align with previous estimates. Additionally, our machine learning method provides robust error estimations with confidence intervals and offers greater potential than scaling relations. This work is a first step toward an automated \mbh estimation method using machine learning. |
| title | The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2411.18200 |