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We present a method that assesses the theoretical detection limit of aBayesian Markov chain Monte Carlo search for a periodic gravitational wavesignal emitted by a neutron star. Inverse probability yields an upper limitestimate for the strength when a signal could not be detected in an observeddata set. The proposed method is based on Bayesian model comparison thatautomatically quantifies Occam s Razor. It limits the complexity of a model byfavoring the most parsimonious model that explains the data. By comparing themodel 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 asearch, for example, for a narrow-band emission for SN1987a, might yield ondata at the sensitivity of LIGO data for an observation time of one year.

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