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.

翻译：伽马射线暴（GRBs）是宇宙中能量最高的瞬变现象之一，可作为研究高能天体物理过程的有力探针。特别是源自遥远宇宙的微弱伽马射线暴，可能为恒星形成的早期阶段提供独特见解。然而，由于光子统计量低和背景噪声显著，检测和定位此类弱源仍具挑战性。尽管近期的机器学习模型针对这些挑战的个别方面提出了解决方案，但往往难以在统计鲁棒性与噪声抑制之间取得平衡。为此，我们提出ComptonUNet——一种联合处理原始数据并重建图像的混合深度学习框架，用于实现鲁棒的伽马射线暴定位。该模型通过结合直接重建模型的统计效率与基于图像的架构的去噪能力，使其能在有限光子统计量和强背景污染条件下有效运行。我们通过模拟代表低地球轨道任务背景环境中嵌入的类伽马射线暴事件，对ComptonUNet的性能进行评估。结果表明，ComptonUNet在多种低统计量和高背景场景下均显著优于现有方法，实现了更高的定位精度。