用深度学习直接从粒子轨迹点预测反氢原子湮灭位置，提升精度与鲁棒性。
Antimatter Annihilation Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber
- 基于PointNet的集成模型直接学习空间点与湮灭点关系，跳过轨迹拟合步骤。
- 在模拟数据上对垂直位置重建性能全面优于传统方法，且在传统方法失效时仍可工作。
- 适用于高精度反物质引力实验，尤其适合复杂或低信噪比场景。
ALPHA-g实验旨在精确测量反氢原子在地球重力场中的加速度。一个环绕磁阱的径向时间投影室（rTPC）用于确定湮灭位置（即顶点）。传统方法需从气体中相互作用的位置（空间点）识别离子化粒子轨迹，并通过拟合螺旋轨迹找到它们最接近的点来推断顶点。本文提出一种新方法，采用基于PointNet架构的模型集成——点云湮灭重建集成模型（PEAR），直接学习顶点位置与rTPC空间点之间的映射关系，无需显式识别和拟合粒子轨迹。在模拟数据上，PEAR在所有评估指标下均优于标准方法；此外，当标准方法失败时，深度学习方法仍能成功重建垂直顶点位置。
原文摘要 · Abstract (English)
The ALPHA-g experiment at CERN aims to precisely measure the terrestrial gravitational acceleration of antihydrogen atoms. A radial Time Projection Chamber (rTPC), that surrounds the ALPHA-g magnetic trap, is employed to determine the annihilation location, called the vertex. The standard approach requires identifying the trajectories of the ionizing particles in the rTPC from the location of their interaction in the gas (spacepoints), and inferring the vertex positions by finding the point where those trajectories (helices) pass closest to one another. In this work, we present a novel approach to vertex reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR), directly learns the relation between the location of the vertices and the rTPC spacepoints, thus eliminating the need to identify and fit the particle tracks. PEAR shows strong performance in reconstructing vertical vertex positions from simulated data, that is superior to the standard approach for all metrics considered. Furthermore, the deep learning approach can reconstruct the vertical vertex position when the standard approach fails.
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