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

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 (<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 - sigma 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 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 using machine learning.