Title: AI Meets Antimatter: Unveiling Antihydrogen Annihilations Show Chinese title

Title: AI遇见反物质：揭示反氢湮灭

Abstract: The ALPHA-g experiment at CERN aims to perform the first-ever direct measurement of the effect of gravity on antimatter, determining its weight to within 1% precision. This measurement requires an accurate prediction of the vertical position of annihilations within the detector. In this work, we present a novel approach to annihilation position reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR) outperforms the standard approach to annihilation position reconstruction, providing more than twice the resolution while maintaining a similarly low bias. This work may also offer insights for similar efforts applying deep learning to experiments that require high resolution and low bias. Show Chinese abstract

Abstract: ALPHA-g实验在欧洲核子研究中心旨在对反物质受重力影响进行首次直接测量，确定其重量的精度达到1%。 此测量需要对探测器内湮灭的垂直位置进行准确预测。 在这项工作中，我们提出了一种新的方法，使用基于PointNet深度学习架构的模型集合来重建湮灭位置。 新开发的模型，即用于湮灭重建的PointNet集成模型（PEAR），优于标准的湮灭位置重建方法，在保持相似低偏差的同时，提供了两倍以上的分辨率。 这项工作也可能为类似的努力提供见解，这些努力将深度学习应用于需要高分辨率和低偏差的实验。