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SUMMARY:Heavy Lifting: Leveraging Machine Learning to Measure the Masses o
 f Supermassive Black Holes - David Chemaly\, Université de Montréal
DTSTART:20221012T121500Z
DTEND:20221012T124500Z
UID:TALK183857@talks.cam.ac.uk
CONTACT:Francisco Paz-C
DESCRIPTION:Despite recent advances in the study of supermassive black hol
 es (SMBH)\, most notably those by the Event Horizon Telescope (EHT) team\,
  a fast and effective methodology to determine the masses of these leviath
 ans at high redshifts continues to elude the astronomical community. Nowad
 ays\, the best method to conduct such calculations is to resolve the kinem
 atic of the molecular gas in the region where the SMBH’s gravitational p
 otential dominates over the galaxy’s potential. Considering how negligib
 le 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-reso
 lution data prevents us from adequately measuring masses at further distan
 ces. 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 lea
 ds to spatially resolved images at much higher redshift. By training our n
 ew neural network on these simulated datasets\, we obtained an algorithm c
 apable of measuring\, rapidly and accurately\, the mass of a lensed SMBH. 
 Additionally\, the simulated galaxies were treated as mock ALMA data to en
 able an easy transfer of our model on real data. I will also discuss the i
 mplications of such a tool and showcase the surprising extent to which thi
 s new methodology can enrich our knowledge on the primary state of our uni
 verse.
LOCATION:The Hoyle Lecture Theatre + Zoom 
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