Estimating Galaxy Cluster Mass Accretion Rates from Observations using Machine Learning
Presenter: John Soltis
Title: Estimating Galaxy Cluster Mass Accretion Rates from Observations using Machine Learning
Date/Time: Monday, June 17th, 2:30 - 4:00 PM; Thursday, June 20th, 3:30 - 5:00 PM
Abstract: Galaxy clusters are sensitive probes of dark matter physics, cosmology, and astrophysics. The mass accretion rates of galaxy clusters can obscure this information, but could also provide a new means of obtaining it. We present a machine learning model, trained on mock observations of galaxy clusters from the Millennium TNG simulation, that can directly estimate the mass accretion rate of galaxy clusters from observations.