Implementation of an efficient Bayesian search for gravitational wave bursts with memory in pulsar timing array data

TL;DR

This paper introduces a computationally efficient Bayesian method for detecting gravitational wave bursts with memory in pulsar timing data, significantly reducing analysis time while maintaining accuracy.

Contribution

The paper presents a new Bayesian search technique leveraging signal model factorization to drastically cut computational costs in pulsar timing GW burst detection.

Findings

Reduces computational complexity by a factor of 100.

Produces upper limits consistent with previous methods.

Enables all analyses possible with standard MCMC techniques.

Abstract

The standard Bayesian technique for searching pulsar timing data for gravitational wave (GW) bursts with memory (BWMs) using Markov Chain Monte Carlo (MCMC) sampling is very computationally expensive to perform. In this paper, we explain the implementation of an efficient Bayesian technique for searching for BWMs. This technique makes use of the fact that the signal model for Earth-term BWMs (BWMs passing over the Earth) is fully factorizable. We estimate that this implementation reduces the computational complexity by a factor of 100. We also demonstrate that this technique gives upper limits consistent with published results using the standard Bayesian technique, and may be used to perform all of the same analyses that standard MCMC techniques can perform.