Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
This addresses a computational bottleneck for astrophysicists analyzing gravitational wave data, offering a significant speed-up but is incremental as it applies an existing simulation-based inference method to a specific domain.
The paper tackles the slow Bayesian posterior inference for the stochastic gravitational wave background in pulsar timing arrays, which traditionally takes days to weeks using MCMC, by using conditional normalizing flows trained on simulated data to reduce sampling time to seconds.
Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing residuals each, producing a posterior distribution for the stochastic gravitational wave background (SGWB) can take days to a week. The computational bottleneck arises because the likelihood evaluation required for MCMC is extremely costly when considering the dimensionality of the search space. Fortunately, generating simulated data is fast, so modern simulation-based inference techniques can be brought to bear on the problem. In this paper, we demonstrate how conditional normalizing flows trained on simulated data can be used for extremely fast and accurate estimation of the SGWB posteriors, reducing the sampling time from weeks to a matter of seconds.