DOAJ Open Access 2023

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

Jerry Sun Paul T. Baker Aaron D. Johnson Dustin R. Madison Xavier Siemens

Abstrak

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.

Topik & Kata Kunci

Astrophysics

Penulis (5)

Jerry Sun

Paul T. Baker

Aaron D. Johnson

Dustin R. Madison

Xavier Siemens

Format Sitasi

APA MLA BibTeX

Sun, J., Baker, P.T., Johnson, A.D., Madison, D.R., Siemens, X. (2023). Implementation of an Efficient Bayesian Search for Gravitational-wave Bursts with Memory in Pulsar Timing Array Data. https://doi.org/10.3847/1538-4357/acd2cc

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.3847/1538-4357/acd2cc

Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3847/1538-4357/acd2cc
Akses: Open Access ✓