An Evidence Based Time-Frequency Search Method for Gravitational Waves from Pulsar Glitches

TL;DR

This paper presents an enhanced Bayesian search method for gravitational waves from pulsar glitches, improving detection accuracy by clustering signals and utilizing information from multiple time segments.

Contribution

It introduces an extension to existing Bayesian techniques that clusters signals based on frequency content and incorporates adjacent segment data to reduce false positives.

Findings

Improved detection accuracy for gravitational waves from pulsar glitches.

Reduced false positives through signal clustering.

Enhanced confidence in detections by using neighboring time segment information.

Abstract

We review and expand on a Bayesian model selection technique for the detection of gravitational waves from neutron star ring-downs associated with pulsar glitches. The algorithm works with power spectral densities constructed from overlapping time segments of gravitational wave data. Consequently, the original approach was at risk of falsely identifying multiple signals where only one signal was present in the data. We introduce an extension to the algorithm which uses posterior information on the frequency content of detected signals to cluster events together. The requirement that we have just one detection per signal is now met with the additional bonus that the belief in the presence of a signal is boosted by incorporating information from adjacent time segments.