标题： 基于机器学习的分辨率函数参数化方法用于欧洲核子研究中心（CERN）n_TOF设施的第一实验区（EAR1） 显示英文标题

标题： Machine learning based parametrization of the resolution function for the first experimental area (EAR1) of the n_TOF facility at CERN

摘要： 本研究解决了一个挑战，即从欧洲核子研究中心（CERN）的中子飞行时间设施n_TOF中对中子束的分辨率函数进行参数化。 困难源于这样一个事实，即分辨率函数在形状上表现出相当强烈的变化，在中子能量大约10个数量级范围内变化。 为了避免需要手动识别适当的解析形式——这阻碍了过去对其参数化的尝试——我们利用了通用的机器学习技术。 特别是，我们通过训练一个多层前馈神经网络来对其进行参数化，依靠的关键思想是这样的网络作为通用逼近器。 对于n_TOF设施第一实验区域的分辨率函数，给出了概念验证，该区域来自其第三阶段的运行。 我们提出了一个针对所讨论的分辨率函数的最优网络结构，该结构预计也将是对其他实验区域以及n_TOF不同运行阶段的分辨率函数最优或接近最优的结构。 为了从单一参数化形式重建几种常用的分辨率函数形式，我们提供了一个实用工具，以专用C++类的形式呈现，该类封装了计算效率高的过程，适合该任务。 具体来说，该类允许将用户指定的初级质子束的时间扩散（来自n_TOF中的中子生成过程）应用于所需的分辨率函数形式。 显示英文摘要

摘要： This study addresses a challenge of parametrizing a resolution function of the neutron beam from the neutron time of flight facility n_TOF at CERN. A difficulty stems from a fact that a resolution function exhibits rather strong variations in shape, over approximately 10 orders of magnitude in neutron energy. In order to avoid a need for a manual identification of the appropriate analytical forms - hindering past attempts at its parametrization - we take advantage of the versatile machine learning techniques. In particular, we parametrize it by training a multilayer feedforward neural network, relying on a key idea that such networks act as the universal approximators. The proof of concept is presented for a resolution function for the first experimental area of the n_TOF facility, from the third phase of its operation. We propose an optimal network structure for a resolution function in question, which is also expected to be optimal or near-optimal for other experimental areas and for different phases of n_TOF operation. In order to reconstruct several resolution function forms in common use from a single parametrized form, we provide a practical tool in the form of a specialized C++ class encapsulating the computationally efficient procedures suited to the task. Specifically, the class allows an application of a user-specified temporal spread of a primary proton beam (from a neutron production process at n_TOF) to a desired resolution function form.