SciPost Submission Page
Towards a foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning
by Judit Pérez-Romero, Saptashwa Bhattacharyya, Sascha Caron, Dmitry Malyshev, Rodney Nicolas, Giacomo Principe, Zoja Rokavec, Roberto Ruiz de Austri, Danijel Skočaj, Fiorenzo Stoppa, Domen Tabernik, Gabrijela Zaharijas
Submission summary
| Authors (as registered SciPost users): | Judit Pérez-Romero |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2509.25128v1 (pdf) |
| Date submitted: | Sept. 30, 2025, 9:56 a.m. |
| Submitted by: | Judit Pérez-Romero |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
Abstract
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Learning (DL) based pipeline for detection, localization, and characterization of gamma-ray sources. We extend our AutoSourceID (ASID) method, initially tested with Fermi-LAT simulated data and optical data (MeerLICHT), to Cherenkov Telescope Array Observatory (CTAO) simulated data. This end-to-end pipeline demonstrates a versatile framework for future application to other surveys and potentially serves as a building block for a foundational model for astrophysical source detection.
Current status:
In refereeing