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 \textit{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.
@article{arxiv.2509.25128,
title = {Towards a foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning},
author = {Judit Pérez-Romero and Saptashwa Bhattacharyya and Sascha Caron and Dmitry Malyshev and Rodney Nicolas and Giacomo Principe and Zoja Rokavec and Roberto Ruiz de Austri and Danijel Skočaj and Fiorenzo Stoppa and Domen Tabernik and Gabrijela Zaharijas},
journal= {arXiv preprint arXiv:2509.25128},
year = {2026}
}