Automated Supervised Identification of Thunderstorm Ground Enhancements (TGEs)

Abstract

Thunderstorm Ground Enhancements (TGEs) are bursts of high-energy particle fluxes detected at Earth's surface, linked to the Relativistic Runaway Electron Avalanche (RREA) mechanism within thunderclouds. Accurate detection of TGEs is vital for advancing atmospheric physics and radiation safety, but event selection methods heavily rely on expert-defined thresholds. In this study, we use an automated supervised classification approach on a newly curated dataset of 2024 events from the Aragats Space Environment Center (ASEC). By combining a Tabular Prior-data Fitted Network (TabPFN) with SHAP-based interpretability, we attain 94.79% classification accuracy with 96% precision for TGEs. The analysis reveals data-driven thresholds for particle flux increases and environmental parameters that closely match the empirically established criteria used over the last 15 years. Our results demonstrate that modest but concurrent increases across multiple particle detectors, along with strong near-surface electric fields, are reliable indicators of TGEs. The framework we propose offers a scalable method for automated, interpretable TGE detection, with potential uses in real-time radiation hazard monitoring and multi-site atmospheric research.