University of Birmingham > Talks@bham > Astrophysics Talks Series > Simulation-Based Inference for Gravitational Waves

Simulation-Based Inference for Gravitational Waves

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact seminar.

With the fourth LIGO -Virgo-KAGRA observing run well underway, gravitational waves are now being detected roughly every three days. While this routine detection promises exciting results, it is becoming a significant challenge to analyze all events using our most sophisticated theoretical models. In this talk, I will describe how to overcome these challenges using deep learning techniques for rapid, amortized Bayesian inference. This approach uses simulated data to train neural networks (such as normalizing flows) to represent the Bayesian posterior. Once trained, sampling becomes extremely fast. I will also describe how to establish full confidence in results using importance sampling, as well as initial results on population inference and future prospects to treat realistic noise.

This talk is part of the Astrophysics Talks Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

Talks@bham, University of Birmingham. Contact Us | Help and Documentation | Privacy and Publicity.
talks@bham is based on talks.cam from the University of Cambridge.