Factorized neural posterior estimation for rapid and reliable inference of parameterized post-Einsteinian deviation parameters in gravitational waves

Name: Factorized neural posterior estimation for rapid and reliable inference of parameterized post-Einsteinian deviation parameters in gravitational waves
Uploaded: 2026-10-02T04:04:47.100350+00:00
Duration: 608 s
Description: This work proposes a Factorized Neural Posterior Estimation (NPE) framework to rapidly estimate nine post-Einsteinian deviation parameters from gravitational wave signals. It uses independent deep learning models for each parameter, achieving inference times 90,000 times faster than traditional MCMC methods, enabling real-time testing of General Relativity.

This work proposes a Factorized Neural Posterior Estimation (NPE) framework to rapidly estimate nine post-Einsteinian deviation parameters from gravitational wave signals. It uses independent deep learning models for each parameter, achieving inference times 90,000 times faster than traditional MCMC methods, enabling real-time testing of General Relativity.

Open the full episode