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 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.
