Gravitational wave detection without boot straps: A Bayesian approach
Abstract
In order to separate astrophysical gravitationalwave signals from instrumental noise, which often contains transient nonGaussian artifacts, astronomers have traditionally relied on bootstrap methods such as time slides. Bootstrap methods sample with replacement, comparing singleobservatory data to construct a background distribution, which is used to assign a falsealarm probability to candidate signals. While bootstrap methods have played an important role establishing the first gravitationalwave detections, there are limitations. First, as the number of detections increases, it makes increasingly less sense to treat singleobservatory data as bootstrapestimated noise, when we know that the data are filled with astrophysical signals, some resolved, some unresolved. Second, it has been known for a decade that background estimation from time slides eventually breaks down due to saturation effects, yielding incorrect estimates of significance. Third, the false alarm probability cannot be used to weight candidate significance, for example when performing population inference on a set of candidates. Given recent debate about marginally resolved gravitationalwave detection claims, the question of significance has practical consequences. We propose a Bayesian framework for calculating the odds that a signal is of astrophysical origin versus instrumental noise without bootstrap noise estimation. We show how the astrophysical odds can safely accommodate glitches. We argue that it is statistically optimal. We demonstrate the method with simulated noise and provide examples to build intuition about this new approach to significance.
 Publication:

Physical Review D
 Pub Date:
 December 2019
 DOI:
 10.1103/PhysRevD.100.123018
 arXiv:
 arXiv:1909.11872
 Bibcode:
 2019PhRvD.100l3018A
 Keywords:

 General Relativity and Quantum Cosmology;
 Astrophysics  High Energy Astrophysical Phenomena
 EPrint:
 9 pages, 3 figures, 1 table, submitted to Phys. Rev. D, v2 fixed typo's