Heavy Lifting: Leveraging Machine Learning to Measure the Masses of Supermassive Black Holes

David Chemaly ( Université de Montréal )

Despite recent advances in the study of supermassive black holes (SMBH), most notably those by the Event Horizon Telescope (EHT) team, a fast and effective methodology to determine the masses of these leviathans at high redshifts continues to elude the astronomical community. Nowadays, the best method to conduct such calculations is to resolve the kinematic of the molecular gas in the region where the SMBH’s gravitational potential dominates over the galaxy’s potential. Considering how negligible the mass of a SMBH (∼10^8 M_☉) is compared to a host galaxy (∼10^12 M_☉), a high spatial resolution is required to resolve such regions, which are of the order of a few tens of parsecs. This need for high-resolution data prevents us from adequately measuring masses at further distances. Here, we present a completely automated machine learning-based pipeline to reconstruct lensed observations of galaxies and measure the mass of a SMBH using the kinematics of the surrounding molecular gas. Our initial findings show that using gravitational lensing on mock ALMA data obtained from the realistic simulations provided by MassiveFIRE leads to spatially resolved images at redshifts that go far beyond what is currently achievable. We will discuss the implications of such a tool and showcase the surprising extent to which this new methodology can enrich our knowledge on the primary state of our universe.