Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection

TL;DR

This paper introduces a self-supervised ConvLSTM framework for detecting transient gamma-ray events in Fermi-LAT data by modeling normal sky behavior and identifying anomalies.

Contribution

It develops a novel deep learning approach combining simulations and real data to improve transient detection in gamma-ray astronomy.

Findings

Successfully modeled ten-year synthetic Fermi-LAT sky maps.

Effectively identified localized transient events and variability.

Provided a benchmark for anomaly detection in long-term gamma-ray datasets.

Abstract

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 emission, and departures from the learned baseline are quantified through pixel-wise mean-squared residual maps. We then define statistically motivated anomaly criteria by estimating per-pixel thresholds from the residual distribution on the training set, and we enforce spatial coherence via local filtering to suppress isolated fluctuations. The ConvLSTM is then deployed as trained predictor on Fermi-LAT daily maps, where the sky can depart from the nominal behavior because of genuine astrophysical variability and instrumental non-stationarities. The resulting pipeline flags localized, time-dependent excesses consistent with high-variable sources or transient events (e.g., flares or GRBs) and provides a benchmark for evaluating anomaly-detection strategies on long-duration, Fermi-LAT-like datasets.