TL;DR
This paper introduces BayesHopperBurst, a Bayesian algorithm for detecting unmodeled gravitational wave bursts in pulsar timing array data, capable of identifying diverse transient signals with unknown morphology.
Contribution
The paper presents a novel Bayesian search method using trans-dimensional MCMC to detect generic gravitational wave bursts in pulsar timing data.
Findings
Successfully tested on simulated datasets with various signals and noise.
Demonstrated application on real NANOGrav data from pulsar B1855+09.
Predicted sensitivity to gravitational wave bursts with specific amplitude thresholds.
Abstract
The nanohertz frequency band explored by pulsar timing arrays provides a unique discovery space for gravitational wave signals. In addition to signals from anticipated sources, such as those from supermassive black hole binaries, some previously unimagined sources may emit transient gravitational waves (a.k.a. bursts) with unknown morphology. Unmodeled transients are not currently searched for in this frequency band, and they require different techniques from those currently employed. Possible sources of such gravitational wave bursts in the nanohertz regime are parabolic encounters of supermassive black holes, cosmic string cusps and kinks, or other, as-yet-unknown phenomena. In this paper we present BayesHopperBurst, a Bayesian search algorithm capable of identifying generic gravitational wave bursts by modeling both coherent and incoherent transients as a sum of Morlet-Gabor…
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