Posterior predictive checking for gravitational-wave detection with pulsar timing arrays: I. The optimal statistic

TL;DR

This paper introduces a rigorous Bayesian framework for assessing gravitational-wave signals in pulsar timing arrays, proposing a new statistic and linking it to hypothesis testing to improve detection significance evaluation.

Contribution

It formalizes the hybrid classical-Bayesian approach as posterior predictive checking and introduces the Bayesian signal-to-noise ratio for more accurate significance assessment.

Findings

The Bayesian signal-to-noise ratio supports detection and falsification of correlations.

The new statistic provides a direct interpretation of significance.

Calibration of Bayes factor with hypothesis-testing significance.

Abstract

A gravitational-wave background can be detected in pulsar-timing-array data as Hellings--Downs correlations among the timing residuals measured for different pulsars. The optimal statistic implements this concept as a classical null-hypothesis statistical test: a null model with no correlations can be rejected if the observed value of the statistic is very unlikely under that model. To address the dependence of the statistic on the uncertain pulsar noise parameters, the pulsar-timing-array community has adopted a hybrid classical--Bayesian scheme (Vigeland et al. 2018) in which the posterior distribution of the noise parameters induces a posterior distribution for the statistic. In this article we propose a rigorous interpretation of the hybrid scheme as an instance of posterior predictive checking, and we introduce a new summary statistic (the Bayesian signal-to-noise ratio) that should be used to accurately quantify the statistical significance of an observation instead of the mean posterior signal-to-noise ratio, which does not support such a direct interpretation. In addition to falsifying the no-correlation hypothesis, the Bayesian signal-to-noise ratio can also provide evidence supporting the presence of Hellings--Downs correlations. We demonstrate our proposal with simulated datasets based on NANOGrav's 12.5-yr data release. We also establish a relation between the posterior distribution of the statistic and the Bayes factor in favor of correlations, thus calibrating the Bayes factor in terms of hypothesis-testing significance.