Simultaneous Source Detection and Analysis Using a Zero-inflated Count Rate Model.

This paper proposes a novel Bayesian technique that allows for simultaneous source detection and count rate analysis. The technique involves using priors, which include a finite probability that the source count rate is exactly zero. Such priors are called "zero-inflated." Solving the posterior distribution of a zero-inflated count rate model provides the probability that the sample contains a source and a probability distribution for the source count rate if the source exists, without the need to perform redundant computations. Sampling from zero-inflated distributions is straightforward and can be accomplished with easily accessible open source software. In addition, zero-inflated priors lead to finite posterior probabilities of "no source," which is an easy-to-understand and satisfying result.