利用距离加权图网络学习不规则粒子探测器几何的表示
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
AI总结：
提出 GarNet 和 GravNet 两种距离加权图网络层，用于不规则高粒度量能器中的能量沉积聚类，并在模拟数据上验证其性能与跨探测器泛化潜力。
摘要：
我们在粒子重建背景下探索使用图网络来处理不规则几何探测器。得益于其表示学习能力，图网络能够利用探测器的全部粒度，同时原生地处理事件稀疏性和任意复杂的探测器几何。我们引入了两种距离加权图网络架构，称为 GarNet 和 GravNet 层，并将其应用于一项典型的粒子重建任务。新架构的性能在一个模拟粒子相互作用数据集上进行评估，该数据集基于高粒度量能器的玩具模型，其设计大致受到高亮度 LHC 阶段将安装在 CMS 探测器中的端盖量能器启发。我们研究了能量沉积的聚类，这是量能器粒子重建的基础，并提供了与替代方法的定量比较。所提出的算法为现有方法提供了一种有吸引力的替代方案，可给出性能相当或资源需求更低的解决方案，同时对探测器几何的底层假设更少，因此具有推广到其他探测器的可能性。
英文摘要：
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
