Detection is truncation: studying source populations with truncated marginal neural ratio estimation
- Resource Type
- Working Paper
- Authors
- Montel, Noemi Anau; Weniger, Christoph
- Source
- Subject
- Astrophysics - Instrumentation and Methods for Astrophysics
Astrophysics - Cosmology and Nongalactic Astrophysics
Astrophysics - High Energy Astrophysical Phenomena
- Language
Statistical inference of population parameters of astrophysical sources is challenging. It requires accounting for selection effects, which stem from the artificial separation between bright detected and dim undetected sources that is introduced by the analysis pipeline itself. We show that these effects can be modeled self-consistently in the context of sequential simulation-based inference. Our approach couples source detection and catalog-based inference in a principled framework that derives from the truncated marginal neural ratio estimation (TMNRE) algorithm. It relies on the realization that detection can be interpreted as prior truncation. We outline the algorithm, and show first promising results.
Comment: Accepted for the NeurIPS 2022 workshop Machine Learning and the Physical Sciences; 8 pages, 3 figures