Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning

• Published in: Astronomy and astrophysics (Berlin) 2024-12
• Main Authors: Poitevineau, R., Combes, F., Garcia-Burillo, S., Cornu, D., Alonso Herrero, A., Ramos Almeida, C., 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., Villar Martin, M., Ward, M.
• Format: Article
• Language: English
• ISSN: 0004-6361; 1432-0746
• DOI: 10.1051/0004-6361/202347566

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 sim 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 keV erg/s $) and nearby (