ComptonUNet: A Deep Learning Model for GRB Localization with Compton Cameras under Noisy and Low-Statistic Conditions

2026-02-19

AI Summary

This work addresses the challenge of accurately detecting and localizing faint gamma-ray bursts (GRBs) from distant cosmic sources under conditions of low photon statistics and intense background noise. To this end, the authors propose ComptonUNet, a hybrid deep learning framework that integrates end-to-end processing of raw Compton camera data with image reconstruction. ComptonUNet uniquely combines the statistical efficiency of direct reconstruction models with the denoising capabilities of a UNet architecture, significantly enhancing GRB localization robustness in low signal-to-noise scenarios. Evaluated on simulated data mimicking low-Earth-orbit missions with varying degrees of low-count statistics and high background interference, the method demonstrates markedly superior localization accuracy compared to existing approaches.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Gamma-ray bursts (GRBs) are among the most energetic transient phenomena in the universe and serve as powerful probes for high-energy astrophysical processes. In particular, faint GRBs originating from a distant universe may provide unique insights into the early stages of star formation. However, detecting and localizing such weak sources remains challenging owing to low photon statistics and substantial background noise. Although recent machine learning models address individual aspects of these challenges, they often struggle to balance the trade-off between statistical robustness and noise suppression. Consequently, we propose ComptonUNet, a hybrid deep learning framework that jointly processes raw data and reconstructs images for robust GRB localization. ComptonUNet was designed to operate effectively under conditions of limited photon statistics and strong background contamination by combining the statistical efficiency of direct reconstruction models with the denoising capabilities of image-based architectures. We perform realistic simulations of GRB-like events embedded in background environments representative of low-Earth orbit missions to evaluate the performance of ComptonUNet. Our results demonstrate that ComptonUNet significantly outperforms existing approaches, achieving improved localization accuracy across a wide range of low-statistic and high-background scenarios.

Problem

• Gamma-ray bursts
• Compton cameras
• low-statistic
• background noise
• source localization

Innovation

• ComptonUNet
• deep learning
• gamma-ray burst localization
• low-statistics
• background denoising