"Machine Learning for Gravitational Wave Astronomy "Stephen Green , University of Nottingham [Host: Alexander Grant]
ABSTRACT:
Since 2015, the LIGOVirgoKAGRA Collaboration has detected 90 signals from merging compact objects such as black holes and neutron stars. Each of these is analyzed using Bayesian inference, employing a stochastic algorithm such as Markov Chain Monte Carlo to compare data against models—thereby characterizing the source. However, this is becoming extremely costly as event rates grow with improved detector sensitivity. In this talk I will describe a powerful alternative using probabilistic deep learning to analyze each event in orders of magnitude less time while maintaining strict accuracy requirements. This uses simulated data to train a normalizing flow to model the posterior distribution over source parameters given the data—amortizing training costs over all future detections. I will also describe the use of importance sampling to establish complete confidence in these deep learning results. Finally I will describe prospects going forward for simulationbased inference to enable improved accuracy in the face of nonstationary or nonGaussian noise. 
Gravity Seminar Monday, December 5, 2022 1:30 PM Physics, Room via Zoom (Zoom link and meeting ID provided below) Note special room. Meeting link: https://virginia.zoom.us/j/94422619812?pwd=L3RCS1FoZXliZzJ0Q3FQNDl3c0RUdz09
Passcode: 507640

To add a speaker, send an email to physspeakers@Virginia.EDU. Please include the seminar type (e.g. Gravity Seminars), date, name of the speaker, title of talk, and an abstract (if available).