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Pulsar timing arrays (PTAs) perform Bayesian posterior inference withexpensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timingresiduals each, producing a posterior distribution for the stochasticgravitational wave background (SGWB) can take days to a week. The computationalbottleneck arises because the likelihood evaluation required for MCMC isextremely costly when considering the dimensionality of the search space.Fortunately, generating simulated data is fast, so modern simulation-basedinference techniques can be brought to bear on the problem. In this paper, wedemonstrate how conditional normalizing flows trained on simulated data can beused for extremely fast and accurate estimation of the SGWB posteriors,reducing the sampling time from weeks to a matter of seconds.

Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows