Authors: Tyson B. Littenberg, Neil J. Cornish
Date: 2 Feb 2009
Abstract: 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 on-going analyses of data from ground based detectors use variants of the frequentist Neyman-Pearson criterion to set detection thresholds based on signal injections and time-slides of the data. These techniques frame the detection problem in terms of an artificial, infinite collection of trials, and do not take into account the particular character of the data in question. Moreover, these techniques are ill suited to addressing more complex problems such as teasing individual sources from the signal rich data streams of future space based gravitational wave detectors. Here we argue that the detection problem may best be addressed in a Bayesian framework, and demonstrate an "end-to-end" solution based on the Parallel Tempered Markov Chain Monte Carlo algorithm and thermodynamic integration of the model evidence. 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 our end-to-end algorithm are found to be in close agreement with those computed using a Reversible Jump Markov Chain Monte Carlo algorithm.
© M. Vallisneri 2012 — last modified on 2010/01/29
Tantum in modicis, quantum in maximis