Date: 2025-10-10

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/81986712072

Meeting ID: 819 8671 2072

Passcode: None

Abstract:

In this talk, I’ll introduce an approach to analyze nonhomogeneous Poisson processes (NHPP) observed with noise which focuses on previously unstudied second-order characteristics of the noisy process. Utilizing a hierarchical Bayesian model with noisy data, we first estimate hyperparameters governing a physically motivated NHPP intensity. Leveraging the posterior distribution, we then infer the probability of detecting a certain number of events within a given radius, the $k$-contact distance. This methodology is demonstrated by its motivating application: observations of fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment’s FRB Project (CHIME/FRB). The approach allows us to identify repeating FRB sources by computing the probability of observing $k$ physically independent sources within some radius in the detection domain, or the probability of coincidence ($P_C$). Applied, the new methodology improves the repeater detection $P_C$, in 86% of cases when applied to the largest sample of previously classified observations, with a median improvement factor (existing metric over $P_C$ from our methodology) of ~ 3000. Throughout the talk, I will provide the necessary astrophysical context to motivate the application and highlight some of the other active statistical problems in FRB science.

Speaker

Amanda M. Cook is Banting postdoctoral fellow at the Trottier Space Institute & Physics Department at McGill University and holds a guest affiliation at the University of Amsterdam’s Anton Pannekoek Institute for Astronomy. She develops and applies statistical methodology to answer questions about the nature of fast radio bursts (FRBs) and to study the otherwise invisible gas between stars and galaxies that FRBs travel through.