A New Bayesian Framework for Hierarchical All-Sky Searches for Continuous Gravitational Waves

Broad all-sky searches for continuous gravitational waves have high computational costs and require hierarchical pipelines. The sensitivity of these approaches is set by the initial search stage and by the number of candidates from that stage that can be followed up. The current follow-up schemes for the deepest surveys require careful tuning and set-up and have a significant human-labor cost, which impacts the number of follow-ups that can be afforded.

We present and demonstrate a new follow-up framework based on Bayesian parameter estimation and nested sampling. The framework implements an automated and adaptive stage transition scheme based on the posterior probability distribution of the model parameters of putative signals. The scheme facilitates the rapid, automated follow-up of the up to millions of signal candidates produced by the early stages of wide-parameter space searches for continuous waves.

We demonstrate the capabilities of the new framework in a demanding setting through a direct application to real searches.