Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection
We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sky with self-supervised spatio-temporal deep learning. We generate a ten-year synthetic Universe with gtobssim and process the simulated events into daily all-sky maps of counts and exposure, obtaining a time-ordered sequence that mirrors the structure of Fermi-LAT observations. To model the nominal evolution of the sky, we employ a Convolutional Long Short-Term Memory (ConvLSTM) network that operates directly on map sequences, preserving spatial locality while learning temporal dependencies. The model is trained to reconstruct expected emiss...
| Authors | Alberto Garinei, Stefano Speziali, Alessandro Vispa et al. |
|---|---|
| Primary category | astro-ph.HE |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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