1. The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning
- Author
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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, and Ward, M.
- Subjects
Astrophysics - Astrophysics of Galaxies - Abstract
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~ - $\sigma$ 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.
- Published
- 2024