Computational Techniques for Parameter Estimation of Gravitational Wave Signals

TL;DR

This paper reviews Bayesian computational methods used for estimating parameters of gravitational wave signals, highlighting their application to various astrophysical sources and the challenges posed by complex waveform models.

Contribution

It provides a comprehensive review of current Bayesian techniques for gravitational wave parameter estimation across different source types.

Findings

Bayesian methods are essential for extracting signals from noisy data.

Markov chain Monte Carlo techniques are widely used for high-dimensional posterior inference.

The review discusses future prospects for gravitational wave data analysis.

Abstract

Since the very first detection of gravitational waves from the coalescence of two black holes in 2015, Bayesian statistical methods have been routinely applied by LIGO and Virgo to extract the signal out of noisy interferometric measurements, obtain point estimates of the physical parameters responsible for producing the signal, and rigorously quantify their uncertainties. Different computational techniques have been devised depending on the source of the gravitational radiation and the gravitational waveform model used. Prominent sources of gravitational waves are binary black hole or neutron star mergers, the only objects that have been observed by detectors to date. But also gravitational waves from core collapse supernovae, rapidly rotating neutron stars, and the stochastic gravitational wave background are in the sensitivity band of the ground-based interferometers and expected to be observable in future observation runs. As nonlinearities of the complex waveforms and the high-dimensional parameter spaces preclude analytic evaluation of the posterior distribution, posterior inference for all these sources relies on computer-intensive simulation techniques such as Markov chain Monte Carlo methods. A review of state-of-the-art Bayesian statistical parameter estimation methods will be given for researchers in this cross-disciplinary area of gravitational wave data analysis.