A convolutional neural network neutrino event classifier
A convolutional neural network neutrino event classifier
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DOI：
10.1088/1748-0221/11/09/p09001
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发表时间：
2016-09-01
影响因子：
1.3
通讯作者：
Vahle, P.
中科院分区：
文献类型：
--
作者：
Aurisano, A.;Radovic, A.;Vahle, P.
卷积神经网络（cnn）在计算机视觉领域得到了广泛的应用，以解决图像识别和分析中的复杂问题。我们描述了CNN技术在识别高能物理和高能中微子物理中常用的采样量热计中的粒子相互作用问题中的应用。在讨论了CNN的核心概念以及与深度学习领域相关的CNN架构的最新创新之后，我们概述了NOvA中微子探测器的具体应用。该算法，CVN（卷积视觉网络）识别基于其拓扑的中微子相互作用，而不需要详细的重建，并且优于目前由NOvA合作使用的算法。
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.