Speaker
Description
All-sky searches for continuous gravitational waves (CWs) emitted by yet-unknown neutron stars pose high computational costs and are typically optimized for signal sensitivity through hierarchical pipelines. Initial search stages reduce the required accuracy in estimated signal parameters by accepting high false alarm rates. All recorded alarms are then analyzed for signal consistency in several follow-up searches with increasing scrutiny. Current frameworks identify signal candidates through maximum likelihood estimation on optimized template grids and establish rejection criteria and uncertainty ranges for the likelihoods and signal parameters through Monte Carlo studies of a reference population of simulated signals, i.e., purely frequentist statistics. This process is highly manual and must be repeated for each follow-up search. Here, we present HierarchMC, a new framework designed around Bayesian Parameter Estimation for the rapid, automated follow-up of the many signal candidates produced by the early stages of wide-parameter space searches for CWs. We further examine the viability and performance of HierarchMC relative to the most sensitive all-sky search for CWs emitted by isolated neutron stars published to date.
