发表机构
Ecole Polytechnique, IN2P3-CNRS（巴黎综合理工学院，法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对高粒度量能器的重建任务，利用探测器几何优化GNN算法以降低复杂度，在粒子识别等任务上验证有效性，并提出通用模块化架构HIBOU。
AI 中文摘要
近年来，高能物理的发现依赖于亮度和/或探测器粒度的提升，这一发展带来了更大的统计量和数据样本，但也使得现有方法和算法难以处理探测器输出。图神经网络（GNNs）已被证明是应对这些挑战的强大工具，尽管GNNs可处理从这些探测器提取的非欧几里得性质的数据，但其部署存在显著困难，主要源于其底层算法的计算复杂度。我们提议利用探测器的已知几何结构来优化GNN流水线所用的算法，这类算法通常具有二次算法复杂度。我们在粒子识别、能量回归和实例分割等各类任务上验证了优化后方法的有效性。最后，我们提出HIBOU，这是一种通用模块化架构，为如何在实验特定的重建框架内实现这些GNN模块提供了蓝图。
英文摘要
In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process the detector outputs with current methods and algorithms. Graph Neural Networks (GNNs), have been shown to be powerful tools to address these challenges. While GNNs can deal with the non-euclidean nature of the data extracted from these detectors, their deployment presents significant difficulties, mainly from the computational complexity of the algorithms they are based on. We propose to use the known geometries of the detectors to optimise the algorithms used for GNN pipelines, which usually have quadratic algorithmic complexities. We validate the validity of our optimised approach on various tasks, such as particle identification, energy regression and instance segmentation. Finally, we present HIBOU, a generic modular architecture that provides a blueprint for how these GNN blocks are to be implemented within experiment-specific reconstruction frameworks.
