Computational techniques for parameter estimation of gravitational wave signals. (20th September 2020)

• Record Type: Journal Article
• Authors: Meyer, Renate; Edwards, Matthew C.; Maturana‐Russel, Patricio; Christensen, Nelson
• Is Part Of: Wiley interdisciplinary reviews. Volume 14:Number 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-20
• Subjects: Bayesian inference -- Markov chain Monte Carlo -- Nested Sampling -- parameter estimation -- signal processing; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1532
• Languages: English
• ISSNs: 1939-5108

Abstract

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. This article is categorized under: Applications of Computational Statistics > Signal and Image Processing and Coding Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical Models > Time Series Models Abstract : Bayesian parameter estimation methods using Markov chain Monte Carlo methods allow the extraction of gravitational wave signals out of noisy detector strain measurements as demonstrated here using the data at Hanford (top) and Livingston (bottom) from GW150914, the first binary black hole merger detected by Advanced LIGO (B. Abbott et al., 2016, Physical Review Letters, 116 (24), 241102).