Implementation of an Efficient Bayesian Search for Gravitational-wave Bursts with Memory in Pulsar Timing Array Data

Authors

• Jerry Sun, Oregon State University
• Paul T. Baker, Widener University
• Aaron D. Johnson, University of Wisconsin-Milwaukee
• Dustin R. Madison, University of the PacificFollow
• Xavier Siemens, Oregon State University

Document Type

Article

Publication Title

Astrophysical Journal

Department

Physics

ISSN

15384357

Volume

951

Issue

2

DOI

10.3847/1538-4357/acd2cc

First Page

1

Last Page

9

Publication Date

7-7-2023

Abstract

The standard Bayesian technique for searching pulsar timing data for gravitational-wave 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 of BWMs that standard MCMC techniques can perform.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Sun, Jerry; Baker, Paul T.; Johnson, Aaron D.; Madison, Dustin R.; and Siemens, Xavier, "Implementation of an Efficient Bayesian Search for Gravitational-wave Bursts with Memory in Pulsar Timing Array Data" (2023). Pacific Faculty Work. 148.
https://scholarlycommons.pacific.edu/all-faculty/148