论文标题
基于机器学习的预测，使用量热的观测值
Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables
论文作者
论文摘要
本文描述了一种基于神经网络的新型方法，用于研究使用高度颗粒状量热量计提供的观察物在Hadronon阵雨中产生的次生分布。分析了高度颗粒状的闪烁体钢筋强化量量量表对负乳头的响应，其中有10至80 GEV的Momenta模拟了Geant4软件包版本10.3的两个物理列表。几种全球可观察物，这些可观察物被用作深度神经网络的输入。网络回归模型是使用监督学习和从模拟中利用真实信息的培训的。训练有素的模型用于预测辐射淋浴中产生的中性乳头的许多中子和能量。讨论了该模型在模拟验证中的实现性能和可能的应用。
The paper describes a novel neural-network-based approach to study the distributions of secondaries produced in hadronic showers using observables provided by highly granular calorimeters. The response is analysed of the highly granular scintillator-steel hadron calorimeter to negative pions with momenta from 10 to 80 GeV simulated with two physics lists from the Geant4 package version 10.3. Several global observables, which characterise different aspects of hadronic shower development, are used as inputs for a deep neural network. The network regression model is trained using a supervised learning and exploiting true information from the simulations. The trained model is applied to predict a number of neutrons and energy of neutral pions produced within a hadronic shower. The achieved performance and possible application of the model to validation of simulations are discussed.
