A1026
Title: Quantifying the clustering probability in noisy nonhomogeneous spatial data to identify repeating fast radio bursts
Authors: Amanda M Cook - McGill University (Canada) [presenting]
Abstract: The aim is to introduce 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, hyperparameters governing a physically motivated NHPP intensity are estimated. Simulation studies are performed to demonstrate the reliability of this methodology in accurately estimating hyperparameters. Leveraging the posterior distribution, the probability of detecting a certain number of events within a given radius, the k-contact distance are then inferred. The methodology is demonstrated 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 identifying repeating FRB sources by bounding or directly simulating the probability of observing $k$ physically independent sources within some radius, or the probability of chance coincidence ($P_cc$). The new methodology improves the repeater detection $P_cc$, in 86\% of cases when applied to the largest sample of previously classified observations, with a median improvement factor (existing metric over $P_cc$, from the methodology) of approximately 3000.
