Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection

Details Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection

2008-07-28

arxiv.org/abs/0807.4483v1

Class. Quantum Grav. 25 (2008) 184010

Astronomy and Astrophysics

Astrophysics

General Relativity and Quantum Cosmology

From the Proceedings of the 11th Gravitational Wave Data Analysis Workshop 11 pages, 4 pages

Scientific paper

10.1088/0264-9381/25/18/184010

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.

Affiliated with

Also associated with

No associations

Rating

Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-313549