Multivariate Time Series Classification of Fermi-Detected Gamma-Ray Transients Using Convolutional-Recurrent Neural Networks

TL;DR

This paper introduces deep learning classifiers combining convolutional and recurrent neural networks to accurately categorize Fermi gamma-ray transients from multivariate time-series data, enhancing real-time astrophysical event identification.

Contribution

The authors develop and validate novel deep learning models that improve classification accuracy and outlier detection for gamma-ray transients using Fermi data.

Findings

Achieved 93% overall classification accuracy.

Identified 2.5% of triggers as outliers of unknown origin.

Correctly assigned 60% of uncertain events to TGF category with over 60% confidence.

Abstract

Fermi Gamma-ray Space Telescope has detected a diverse range of gamma-ray transients since its launch in 2008. Over the years, Fermi has accumulated an extensive public archive of transient events. Traditional classification methods for these events typically rely on fixed thresholds, localisation accuracy, and characteristic light curve features. However, in the current era of time-critical, multi-wavelength, and multi-messenger astronomy, rapid and reliable classification is essential to enable timely follow-up and coordinated observations. In this work, we develop and present two deep learning-based classifiers that integrate convolutional and recurrent neural network architectures. Using multivariate time-series inputs derived from Fermi-GBM data, our models are trained to distinguish among four classes of gamma-ray transients: Gamma-Ray Bursts (GRBs), Terrestrial Gamma-ray Flashes (TGFs), Solar Flares (SFLAREs), and Soft Gamma Repeaters (SGRs). Furthermore, the models are designed to flag events that do not conform to any of these categories, providing a pathway for identifying potentially new or rare transient types. Training was conducted using a carefully curated subset of high-confidence Fermi events. The resulting models achieve an overall classification accuracy of 93%, and identify approximately 2.5% of the triggers as outliers of unknown origin. When applied to Fermi events with uncertain classifications, our models assign 60% of them to the TGF category with over 60% confidence. These results demonstrate that incorporating deep learning-based classification into onboard or automated data pipelines can significantly enhance transient identification, minimize misclassification, and improve the discovery potential of new phenomena in future high-energy astrophysics missions.