A Compound Poisson Generator approach to Point-Source Inference in Astrophysics

Understand

The identification and description of point sources is one of the oldest problems in astronomy; yet, even today the correct statistical treatment for point sources remains one of the field's hardest problems.

• For dim or crowded sources, likelihood based inference methods are required to estimate the uncertainty on the characteristics of the source population.
• In this work, a new parametric likelihood is constructed for this problem using Compound Poisson Generator (CPG) functionals which incorporate instrumental effects from first principles.
• We demonstrate that the CPG approach exhibits a number of advantages over Non-Poissonian Template Fitting (NPTF) - an existing method - in a series of test scenarios in the context of X-ray astronomy.