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。

卷积神经网络中微中子事件分类器

A Convolutional Neural Network Neutrino Event Classifier

The approximate computation of all gravitational forces between $N$
interacting particles via the fast multipole method (FMM) can be made as
accurate as direct summation, but requires less than $\mathcal{O}(N)$
operations. FMM groups particles into spatially bounded cells and uses
cell-cell interactions to approximate the force at any position within the sink
cell by a Taylor expansion obtained from the multipole expansion of the source
cell. By employing a novel estimate for the errors incurred in this process, I
minimise the computational effort required for a given accuracy and obtain a
well-behaved distribution of force errors. For relative force errors of
$\sim10^{-7}$, the computational costs exhibit an empirical scaling of $\propto
N^{0.87}$. My implementation (running on a 16 core node) out-performs a
GPU-based direct summation with comparable force errors for $N\gtrsim10^5$.

通过快速多极子方法（FMM）精确计算 N 个相互作用粒子之间的所有引力，所需的操作少于 O（N），同时利用空间有限约束性将粒子分组到细胞中，并通过细胞 - 细胞相互作用，使用源细胞的多极展开来近似汇聚细胞内的力。通过引入一种新的误差估计方式来最小化所需的计算和获得一个良好的力误差分布。相对于力误差约为 10-7，我的实现（在 16 个核心节点上运行）在 N≈105 时表现出 O (N) 的计算成本，超过了基于 GPU 的直接求和。