2009/02/28 by Tyson B. Littenberg, T. B. Littenberg, Neil J. Cornish
Computer Science · Earth and Planetary Sciences · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Frequentist inference #Markov chain Monte Carlo #Mathematics #Meteorological Phenomena and Simulations #Monte Carlo method #Noise (video) #Physics #Posterior probability #Pulsars and Gravitational Waves Research #Statistics #Target Tracking and Data Fusion in Sensor Networks #gr-qc
paper · pdf · doi:10.1103/physrevd.80.063007
published as Phys.Rev.D80:063007,2009 · 19 pages, 12 figures, revised to address referee's comments
arxiv created 2009/07/16 · openalex publication_date 2009/09/24 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The analysis of data from gravitational wave detectors can be divided into three phases: search, characterization, and evaluation. The evaluation of the detection---determining whether a candidate event is astrophysical in origin or some artifact created by instrument noise---is a crucial step in the analysis. The ongoing analyses of data from ground-based detectors employ a frequentist approach to the detection problem. A detection statistic is chosen, for which background levels and detection efficiencies are estimated from Monte Carlo studies. This approach frames the detection problem in terms of an infinite collection of trials, with the actual measurement corresponding to some realization of this hypothetical set. Here we explore an alternative, Bayesian approach to the detection problem, that considers prior information and the actual data in hand. Our particular focus is on the computational techniques used to implement the Bayesian analysis. We find that the parallel tempered Markov chain Monte Carlo (PTMCMC) algorithm is able to address all three phases of the analysis in a coherent framework. The signals are found by locating the posterior modes, the model parameters are characterized by mapping out the joint posterior distribution, and finally, the model evidence is computed by thermodynamic integration. As a demonstration, we consider the detection problem of selecting between models describing the data as instrument noise, or instrument noise plus the signal from a single compact galactic binary. The evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found to be in close agreement with those computed using a reversible jump Markov chain Monte Carlo algorithm.