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

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.

19 pages, 3 figures, 1 table. Submitted to Astrophysical Journal

References in corpus (6)

• The NANOGrav 12.5-year Data Set: Search For An Isotropic Stochastic Gravitational-Wave Background
• The gravitational-wave memory effect
• New advances in the Gaussian-process approach to pulsar-timing data analysis
• Low-rank approximations for large stationary covariance matrices, as used in the Bayesian and generalized-least-squares analysis of pulsar-timing data
• Assessing Pulsar Timing Array Sensitivity to Gravitational Wave Bursts with Memory
• The NANOGrav 11-Year Data Set: Limits on Gravitational Wave Memory

Cited by in corpus (3)

• The Need For Speed: Rapid Refitting Techniques for Bayesian Spectral Characterization of the Gravitational Wave Background Using PTAs
• Exploring the Capabilities of Gibbs Sampling in Pulsar Timing Arrays
• Efficient Bayesian inference and model selection for continuous gravitational waves in pulsar timing array data