Abstract

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_Sun) is compared to a host galaxy (∼10}12 M_Sun), 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 new machine learning-based method to resolve the surrounding molecular gas of lensed observations at redshifts that go far beyond what is currently achievable. Our initial findings show that using gravitational lensing on realistic simulations provided by MassiveFIRE leads to spatially resolved images at much higher redshift. By training our new neural network on these simulated datasets, we obtained an algorithm capable of measuring, rapidly and accurately, the mass of a lensed SMBH . Additionally, the simulated galaxies were treated as mock ALMA data to enable an easy transfer of our model on real data. I will also 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.