Localization and Confidence Region Estimation of Short GRBs with the COSI BGO Shield Using a HEALPix-Based Deep Learning Approach

TL;DR

This paper introduces a deep learning-based method for localizing short GRBs using the COSI BGO shield data, estimating confidence regions with a neural network classifier and HEALPix framework.

Contribution

It presents a novel neural network approach for GRB localization that can handle complex confidence regions, improving upon classical methods.

Findings

Neural network classifier predicts probability distributions over the sky for GRB locations.

The method estimates 90% confidence regions, including split regions.

Future work will compare this approach with classical localization techniques.

Abstract

The Compton Spectrometer and Imager is a NASA satellite mission under development that will survey the entire sky in the 0.2-5 MeV range using a wide-field germanium detector array, surrounded on the sides and bottom by active shields (the Anticoincidence Subsystem, ACS). The ACS aims to suppress and monitor background events, as well as detect transient sources, such as Gamma-Ray Bursts (GRBs), through its onboard triggering algorithm. The data related to GRBs are sent to the ground and analyzed by an automated pipeline to localize the GRBs and share their positions with the community. In this work, we present a brief GRB localization method based on ACS data, utilizing deep learning (DL) techniques, which can estimate the 90\% confidence region, including cases where it is split into multiple areas. To address this, we developed a neural network classifier that predicts the GRB location as a probability distribution across the sky map following the HEALPix framework. The distribution can be used to compute the 90\% confidence regions. Future work will compare this DL-based localization approach with classical methods such as $\chi^2$ fitting and Maximum Likelihood Estimation.