论文标题
基于机器学习的事件分类，用于$^\ text {nat} $ c（n，p）和$^\ text {nat} $ c（n，d）反应
Machine learning based event classification for the energy-differential measurement of the $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reactions
论文作者
论文摘要
本文探讨了使用机器学习技术，尤其是神经网络的可行性，以分类关节$^\ text {nat} $ c（n，p）和$^\ text {nat} $ c（n，n，d）$^\ text {nat} $ c（n，p）的可行性。每个相关的$ΔE$ - $ e $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $来自两个分段的硅望远镜的条均可单独处理，并提供了自己的专用神经网络。该过程的一个重要部分是根据GEANT4模拟的原始数据仔细准备培训数据集。我们将相关的3参数空间分为离散的体素，而是根据粒子/反应类型对每个体素进行分类，而不是使用这些原始数据进行训练，而是将相关的3参数空间分为离散的体素，然后将这些素体提交给训练程序。发现结构优化和训练有素的神经网络的分类能力优于手动选择的切割。
The paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reaction cross section measurement from the neutron time of flight facility n_TOF at CERN. Each relevant $ΔE$-$E$ pair of strips from two segmented silicon telescopes is treated separately and afforded its own dedicated neural network. An important part of the procedure is a careful preparation of training datasets, based on the raw data from Geant4 simulations. Instead of using these raw data for the training of neural networks, we divide a relevant 3-parameter space into discrete voxels, classify each voxel according to a particle/reaction type and submit these voxels to a training procedure. The classification capabilities of the structurally optimized and trained neural networks are found to be superior to those of the manually selected cuts.
