K-contact distance for noisy nonhomogeneous spatial point data with application to repeating fast radio burst sources

This paper introduces an approach to analyze nonhomogeneous Poisson processes (NHPP) observed with noise, focusing on previously unstudied second-order characteristics of the noisy process. Utilizing a hierarchical Bayesian model with noisy data, we estimate hyperparameters governing a physically motivated NHPP intensity. Simulation studies demonstrate the reliability of this methodology in accurately estimating hyperparameters. Leveraging the posterior distribution, we then infer the probability of detecting a certain number of events within a given radius, the k-contact distance. We demonstrate our methodology with an application to observations of fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment’s FRB Project (CHIME/FRB). This approach allows us to identify repeating FRB sources by bounding or directly simulating the probability of observing k physically independent sources within some radius in the detection domain or the probability of coincidence (PC). The new methodology improves the repeater detection PC in 91% of cases when applied to the largest sample of previously classified observations, with a median improvement factor (existing metric over PC from our methodology) of ∼ 4800.