图神经网络用于 IceCube 低能事件分类与重建
高能物理 - 实验
2022-11-16 v3 天体物理仪器与方法
机器学习
数据分析、统计与概率
仪器与探测器
摘要
IceCube 是一个由光学传感器构成的立方千米阵列，建造用于探测 1 GeV 至 1 PeV 间的大气与天体物理中微子，部署于南极冰盖表面下 1.45 km 至 2.45 km 处。来自冰内探测器的事件分类与重建在 IceCube 数据分析中起着核心作用。由于探测器几何不规则、冰中光散射与吸收的不均匀性，以及低于 100 GeV 时每个事件产生的信号光子数相对较少，事件的重建与分类是一项挑战。为应对该挑战，可将 IceCube 事件表示为点云图，并使用图神经网络（GNN）作为分类与重建方法。该 GNN 能够区分中微子事件与宇宙射线本底、分类不同中微子事件类型，并重建沉积能量、方向与相互作用顶点。基于模拟，我们在 1–100 GeV 能量范围内与当前 IceCube 分析中使用的最先进极大似然技术进行比较，包括已知系统不确定性的影响。对于中微子事件分类，在固定误报率（FPR）下，GNN 相较当前 IceCube 方法将信号效率提高了 18%。或者，在固定信号效率下，GNN 将 FPR 降低超过 8 倍（降至千分之五以下）。对于能量、方向与相互作用顶点的重建，在 1–30 GeV 能量范围内分辨率相较当前极大似然技术平均提升 13%–20%。在 GPU 上运行时，GNN 处理 IceCube 事件的速率近乎 IceCube 中位触发率 2.7 kHz 的两倍，这为在在线瞬态事件搜寻中使用低能中微子开辟了可能。
引用
@article{arxiv.2209.03042,
title = {Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube},
author = {R. Abbasi and M. Ackermann and J. Adams and N. Aggarwal and J. A. Aguilar and M. Ahlers and M. Ahrens and J. M. Alameddine and A. A. Alves and N. M. Amin and K. Andeen and T. Anderson and G. Anton and C. Argüelles and Y. Ashida and S. Athanasiadou and S. Axani and X. Bai and A. Balagopal V. and M. Baricevic and S. W. Barwick and V. Basu and R. Bay and J. J. Beatty and K. -H. Becker and J. Becker Tjus and J. Beise and C. Bellenghi and S. Benda and S. BenZvi and D. Berley and E. Bernardini and D. Z. Besson and G. Binder and D. Bindig and E. Blaufuss and S. Blot and F. Bontempo and J. Y. Book and J. Borowka and C. Boscolo Meneguolo and S. Böser and O. Botner and J. Böttcher and E. Bourbeau and J. Braun and B. Brinson and J. Brostean-Kaiser and R. T. Burley and R. S. Busse and M. A. Campana and E. G. Carnie-Bronca and C. Chen and Z. Chen and D. Chirkin and K. Choi and B. A. Clark and L. Classen and A. Coleman and G. H. Collin and A. Connolly and J. M. Conrad and P. Coppin and P. 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journal= {arXiv preprint arXiv:2209.03042},
year = {2022}
}
备注
Prepared for submission to JINST