LUNCH: A Lightweight Unified Deep-Learning Framework for General Transients Classification in High-Energy Time-Domain Astronomy

TL;DR

LUNCH is a deep-learning framework that classifies high-energy space transient events directly from raw light curves, achieving high accuracy without relying on precise localization or background subtraction, suitable for real-time applications.

Contribution

It introduces a dual-scale neural network architecture that effectively classifies transients from raw data, outperforming existing classifiers and enabling rapid, instrument-agnostic analysis.

Findings

Achieves 97.23% accuracy on 15-year Fermi/GBM data.

Maintains 95.07% accuracy using only three broad energy bands.

Outperforms the GBM in-flight classifier on independent test data.

Abstract

The increasing data volume of high-energy space monitors necessitates real-time, automated transient classification for multi-messenger follow-up. Conventional methods rely on empirical features like hardness ratios and reliable localization, which are not always precisely available during early detection. We developed the Lightweight Unified Neural Classifier for High-energy Transients (LUNCH) - an end-to-end deep-learning framework that performs general transient classification directly from raw multi-band light curves, eliminating the need for background subtraction or source localization. Its dual-scale architecture fuses long- and short-scale temporal evolution adaptively. Evaluated on 15 years of Fermi/GBM triggers, the optimal model achieves 97.23% accuracy when trained on complete energy spectra. A lightweight version using only three broad energy bands retains 95.07% accuracy, demonstrating that coarse spectral information fused with temporal context enables robust discrimination. The system significantly outperforms the GBM in-flight classifier on three months of independent test data. Feature visualization reveals well-separated class clusters, confirming physical interpretability. LUNCH combines high accuracy, low computational cost, and instrument-agnostic inputs, offering a practical solution for real-time in-flight processing that enables timely triggers for immediate multi-wavelength and multi-messenger follow-up observations in future time-domain missions.