A parallelized Bayesian approach to accelerated gravitational-wave background characterization

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 interpulsar 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 technique empowers a modular approach to parameter estimation of the GW background, multistage model selection of a spectrally-common stochastic process and quadrupolar interpulsar correlations, and statistical cross-validation of measured signals between independent pulsar subarrays. 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 reanalysis of the latter.

My Contributions Cross-validation of single pulsar Bayesian analyses; paper review.

Recommended citation: Taylor et al. Phys. Rev. D 105, 084049
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