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.

本文描述了卷积神经网络（CNN）在粒子物理学领域的应用，尤其是在高能中微子物理学和高能物理学中通常使用的采样量热计中识别粒子相互作用问题上的应用，通过 CVN（卷积视觉网络）算法对中微子相互作用进行拓扑识别，不需要详细重建，优于目前 NOvA 协作组使用的算法。