Sines, steps and droplets: Semiparametric Bayesian modeling of arrival time series

TL;DR

This paper introduces Bayesian semiparametric models for analyzing arrival time series data in astronomy, aiming to enhance detection and measurement of pulsars and gamma-ray bursts without data binning.

Contribution

It develops flexible Bayesian Poisson point process models that connect to traditional methods, improving analysis of arrival time data in high-energy astrophysics.

Findings

Enhanced detection capabilities for pulsars and gamma-ray bursts.

Flexible modeling approach adaptable to various time series data.

Close connection to existing frequentist methods.

Abstract

I describe ongoing work developing Bayesian methods for flexible modeling of arrival time series data without binning, aiming to improve detection and measurement of X-ray and gamma-ray pulsars, and of pulses in gamma-ray bursts. The methods use parametric and semiparametric Poisson point process models for the event rate, and by design have close connections to conventional frequentist methods currently used in time-domain astronomy.