Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows.

Pulsar timing arrays perform Bayesian posterior inference with expensive Markov chain Monte Carlo (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.