Rapid and Robust Parameter Estimation for Gravitational Wave Observations

Explore rapid and robust parameter estimation techniques for gravitational wave observations in this 45-minute lecture by Jonathan Gair from the Max Planck Institute for Gravitational Physics. Delve into the challenges faced in analyzing data from future detectors like LISA, Cosmic Explorer, and Einstein Telescope. Learn about machine learning approaches, particularly the DINGO neural network, which uses normalizing flows to quickly generate posterior distributions for gravitational wave source parameters. Discover how these advanced methods compare to standard techniques in terms of accuracy and efficiency, and gain insights into future challenges such as long waveforms, non-stationary noise, population inference, and overlapping sources in gravitational wave astronomy.

Rapid and robust parameter estimati for gravitational wave observations
Talk outline
Overview of CW parameter estimation
Overview of GW parameter estimation
Computational cost: GW150914
Challenges in GW parameter estimation
Current solutions: Bayestar
Current solutions: Faster waveform models
New approaches: Neural posterior estimation
Normalizing flows
NPE refinements: embedding network
NPE refinements: group equivariant NPE
NPE network
NPE validation: GWTC-1 BBHS
Future challenges: long waveforms
Future challenges: non-stationary noise
Future challenges: population inference
Future challenges: overlapping sources
Summary