基于稀疏卷积神经网络的MicroBooNE事件重构语义分割
Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
摘要：
我们提出了一个语义分割网络，SparseSSNet，提供像素级分类的MicroBooNE数据的性能。MicroBooNE实验采用液态氩时间投影室来研究中微子的性质和相互作用。SparseSSNet是一个子流形稀疏卷积神经网络，它提供了MicroBooNE的$ν_e$ -外观振荡分析中使用的基于初始机器学习的算法。该网络经过训练，将像素分为五类，然后重新分为与当前分析更相关的两类。SparseSSNet的输出是进一步分析步骤的关键输入。该技术首次用于液态氩时间投影室数据，与以前使用的卷积神经网络相比，在精度和计算资源利用率方面都有所改进。供试品达到的准确度为$\geq 99\%$。对于完整的中微子相互作用模拟，处理一幅图像的时间为$\approx$ 0.5秒，内存使用量为1 GB，这允许利用大多数典型的CPU工作机。
英文摘要：
We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's $ν_e$-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is $\geq 99\%$. For full neutrino interaction simulations, the time for processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.
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