Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events
Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events
复制标题
用于 ProtoDUNE-SP 事件中粒子分类的稀疏卷积神经网络
DOI：
10.1088/1742-6596/2438/1/012125
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Abed Abud A
中科院分区：
文献类型：
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作者：
Abed Abud A
深度学习方法和计算机视觉正在成为粒子物理探测器中事件重建的重要工具。在这项工作中，我们报告了使用子流形稀疏卷积神经网络(SparseNets)来分类来自CERN(ProtoDUNE-SP)沙丘原型液体Ar探测器的径迹和簇射命中。通过利用问题的三维性质，我们使用一组九个输入特征来分类与跟踪或淋浴粒子相关的稀疏和局部密集的命中。SparseNet已经在测试样本上进行了训练，并显示出令人振奋的结果：效率和纯度超过90%。与诸如图神经网络的其他DL网络相比，这也是以相当大的加速比和显著更少的资源利用来实现的。这种方法为未来的大型中微子探测器提供了巨大的可扩展性优势，例如计划中的沙丘实验。
Deep Learning (DL) methods and Computer Vision are becoming important tools for event reconstruction in particle physics detectors. In this work, we report on the use of submanifold sparse convolutional neural networks (SparseNets) for the classification of track and shower hits from a DUNE prototype liquid-argon detector at CERN (ProtoDUNE-SP). By taking advantage of the three-dimensional nature of the problem we use a set of nine input features to classify sparse and locally dense hits associated to track or shower particles. The SparseNet has been trained on a test sample and shows promising results: efficiencies and purities greater than 90%. This has also been achieved with a considerable speedup and substantially less resource utilization with respect to other DL networks such as graph neural networks. This method offers great scalability advantages for future large neutrino detectors such as the planned DUNE experiment.