Factorized neural posterior estimation for rapid and reliable inference of parameterized post-Einsteinian deviation parameters in gravitational waves
This paper introduces a factorized neural posterior estimation framework that leverages normalizing flows and hybrid deep learning architectures to achieve millisecond-scale, statistically reliable inference of parameterized post-Einsteinian deviation parameters for gravitational wave tests of general relativity, offering a speedup over traditional Markov chain Monte Carlo methods.