A Parallelized Bayesian Approach To Accelerated Gravitational-Wave Background Characterization

TL;DR

This paper introduces a parallelized Bayesian method for rapid characterization of the gravitational-wave background using pulsar-timing data, enabling efficient analysis of large datasets and new pulsar data integration.

Contribution

A novel Factorized Likelihood technique that significantly accelerates Bayesian analysis of pulsar-timing array data for gravitational-wave background characterization.

Findings

Achieves similar accuracy to full likelihood analysis

Reduces computational time by orders of magnitude

Facilitates incremental data analysis and model validation

Abstract

The characterization of nanohertz-frequency gravitational waves (GWs) with pulsar-timing arrays requires a continual expansion of datasets and monitored pulsars. Whereas detection of the stochastic GW background is predicated on measuring a distinctive pattern of inter-pulsar correlations, characterizing the background's spectrum is driven by information encoded in the power spectra of the individual pulsars' time series. We propose a new technique for rapid Bayesian characterization of the stochastic GW background that is fully parallelized over pulsar datasets. This Factorized Likelihood (FL) technique empowers a modular approach to parameter estimation of the GW background, multi-stage model selection of a spectrally-common stochastic process and quadrupolar inter-pulsar correlations, and statistical cross-validation of measured signals between independent pulsar sub-arrays. We demonstrate the equivalence of this technique's efficacy with the full pulsar-timing array likelihood, yet at a fraction of the required time. Our technique is fast, easily implemented, and trivially allows for new data and pulsars to be combined with legacy datasets without re-analysis of the latter.