用点集网络融合双视角数据，高效提升中微子探测器粒子分割精度。
Heterogeneous Point Set Transformers for Segmentation of Multiple View Particle Detectors
- 设计异构点集变换器，联合处理XZ与YZ两个视角的稀疏数据
- 内存消耗不足旧方法10%，达到96.8% AUC，优于单视图独立处理（85.4%）
- 适合高维稀疏探测数据的实时粒子识别任务
NOvA是费米实验室中微子束长基线振荡实验，需将探测器原始信号匹配到源粒子并识别其类型。传统方法结合了经典聚类与卷积神经网络。由于探测器结构，数据以两个稀疏二维图像（XZ与YZ视图）呈现，而非三维形式。本文提出一种点集神经网络，可在稀疏矩阵上操作，并融合双视图信息。该模型内存占用不足先前方法的10%，实现96.8%的AUC得分，高于分别处理双视图所得的85.4%。
原文摘要 · Abstract (English)
NOvA is a long-baseline neutrino oscillation experiment that detects neutrino particles from the NuMI beam at Fermilab. Before data from this experiment can be used in analyses, raw hits in the detector must be matched to their source particles, and the type of each particle must be identified. This task has commonly been done using a mix of traditional clustering approaches and convolutional neural networks (CNNs). Due to the construction of the detector, the data is presented as two sparse 2D images: an XZ and a YZ view of the detector, rather than a 3D representation. We propose a point set neural network that operates on the sparse matrices with an operation that mixes information from both views. Our model uses less than 10% of the memory required using previous methods while achieving a 96.8% AUC score, a higher score than obtained when both views are processed independently (85.4%).
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