A Bayesian method to set upper limits on the strength of a periodic gravitational wave signal from the remnant of SN1987A: possible applications in LIGO searches

Abstract

We present a method that assesses the theoretical detection limit of a Bayesian Markov chain Monte Carlo search for a periodic gravitational wave signal emitted by a neutron star. Inverse probability yields an upper limit estimate for the strength when a signal could not be detected in an observed data set. The proposed method is based on Bayesian model comparison that automatically quantifies Occam s Razor. It limits the complexity of a model by favoring the most parsimonious model that explains the data. By comparing the model with a signal from a pulsar to the null model that assumes solely noise, we derive the detection probability and an estimate for the upper limit that a search, for example, for a narrow-band emission for SN1987a, might yield on data at the sensitivity of LIGO data for an observation time of one year.