HAGRID -- High Accuracy GRB Rapid Inference with Deep learning

TL;DR

This paper presents a deep learning-based method for rapid, accurate localization and characterization of Gamma-Ray Bursts using the POLAR-2 observatory, enabling real-time alerts within 2 minutes of GRB onset.

Contribution

It introduces a novel deep learning approach tailored for POLAR-2 to quickly identify and localize GRBs, improving speed and accuracy over traditional methods.

Findings

POLAR-2 can provide localization alerts within 2 minutes of GRB onset.

Deep learning models are feasible for real-time GRB analysis on space-based observatories.

The method enhances multi-messenger astrophysics by enabling faster GRB follow-up observations.

Abstract

Since their discoveries in 1967, Gamma-Ray Bursts (GRBs) continue to be one of the most researched objects in astrophysics. Multi-messenger observations are key to gaining a deeper understanding of these events. In order to facilitate such measurements, fast and accurate localization of the gamma-ray prompt emission is required. As traditional localization techniques are often time consuming or prone to significant systematic errors, here we present a novel method which can be applied on the POLAR-2 observatory. POLAR-2 is a dedicated GRB polarimeter, which will be launched towards the China Space Station (CSS) in 2025. The CSS provides POLAR-2 access to a GPU, which makes it possible and advantageous to run a Deep Learning model on it. In this work, we explore the possibility to identify GRBs in real time and to infer their location and spectra with deep learning models. Using POLAR simulations and data, a feasibility experiment was performed to implement this method on POLAR-2. Our results indicate that using this method, in combination with real time data downlinking capabilities, POLAR-2 will be able to provide accurate localization alerts within 2 minutes of the GRB onset.