HIBEAM 寻找中子-反中子转换的顶点重建
Vertex reconstruction for a search for neutron-antineutron conversions with HIBEAM
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中文总结 AI 辅助
针对 HIBEAM 寻找中子-反中子转换的顶点重建问题，对比经典与机器学习方法，发现二者在简单事例顶点坐标重建性能相当，机器学习方法可提供重要事例形状信息。
中文摘要 AI 辅助
欧洲散裂中子源（ESS）的 HIBEAM/NNBAR 计划（已纳入 FINESSE/NNBAR 计划）旨在寻找中子转换为反中子的过程。一个重要的可观测量是反中子在薄靶箔上湮灭产生的带电粒子所形成的重建顶点，这些带电粒子会穿过时间投影室（TPC）。本文研究该拓扑结构下的径迹聚类和箔平面顶点重建，测试并比较了非机器学习方法和图神经网络方法，包括确定性聚类、无径迹投影，以及带有径迹分类和顶点优化的混合聚类/图神经网络链。我们得出结论：对于 HIBEAM TPC 中几何结构简单的事例，经典重建方法在顶点坐标重建方面与基于机器学习的方法性能相当；而定制的基于机器学习的方法则能提供事例形状信息，这对后续分析可能具有重要意义。
英文摘要
The HIBEAM/NNBAR programme (incorporated into the FINESSE/NNBAR programme) at the European Spallation Source is proposed to search for neutrons converting to antineutrons. An important observable is the reconstructed vertex arising from charged particles produced by an antineutron annihilating on a thin target foil and which pass through a time projection chamber. This paper studies track clustering and foil-plane vertex reconstruction for this topology. Both non-machine-learning methods and graph-neural-network methods are tested and compared with each other, including deterministic clustering, trackless projection and a hybrid clustering/graph-neural-network chain with track classification and vertex refinement. We conclude that, for the geometrically simple events in the HIBEAM TPC, classical reconstruction methods perform on-par with machine-learning based methods in terms of vertex coordinate reconstruction. Custom machine-learning based methods can, however, deliver event-shape information that may be important in downstream analyses.
发表机构
- Stockholm University（斯德哥尔摩大学）
- University of Helsinki（赫尔辛基大学）
- Lund University（隆德大学）
机构由 AI 辅助整理，请以论文原文为准。
