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arXiv · 0807.4483

Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection

John Veitch·Alberto Vecchio
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Abstract

Bayesian model selection provides a powerful and mathematically transparent framework to tackle hypothesis testing, such as detection tests of gravitational waves emitted during the coalescence of binary systems using ground-based laser interferometers. Although its implementation is computationally intensive, we have developed an efficient probabilistic algorithm based on a technique known as nested sampling that makes Bayesian model selection applicable to follow-up studies of candidate signals produced by on-going searches of inspiralling compact binaries. We discuss the performance of this approach, in terms of "false alarm rate" and "detection probability" of restricted second post-Newtonian inspiral waveforms from non-spinning compact objects in binary systems. The results confirm that this approach is a viable tool for detection tests in current searches for gravitational wave signals.

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John Veitch, Alberto Vecchio. 2008-07-28. Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection. https://doi.org/10.1088/0264-9381%2F25%2F18%2F184010

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