Speaker
Description
Simultaneous detections of Gamma-Ray Bursts (GRBs) by the Fermi Gamma-ray Burst Monitor (GBM) and the Fermi Large Area Telescope (LAT) instruments are crucial for understanding the full spectral evolution of relativistic jets. Addressing the delay in official catalog releases, we developed a scalable machine learning pipeline to identify coincident events in real-time. By performing a systematic cross-match of available catalog data, we generated a labeled dataset of approximately 220 coincident candidates. We extract a comprehensive set of features for classification, including temporal offsets, spectral hardness ratios, fluence/flux comparisons, peak energy ($E_{peak}$), and duration. These features serve as inputs for machine learning classification models, which are trained to distinguish between physical associations and random spatial coincidences. We report on the feature importance distributions, demonstrating that specific spectral signatures are highly predictive of high-energy emission. This framework allows for the extension of the confirmed GRB catalog beyond August 2022, providing a tool for the immediate classification of the latest bursts.
| Stream | Science or Engineering |
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