Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation, requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, results of two ResNet networks are presented by investigating global and kinematical quantities, indeed jet- and event-shape variables. The widely used Lund string fragmentation model is applied as a baseline in $\sqrt{s}= 7$ TeV proton-proton collisions to predict the most relevant observables at further LHC energies.
翻译:Hadronizen化是一个非扰动过程,不能从最初的原则中推断出理论描述。 建模 hadron 需要若干假设和各种苯球学方法。 利用最先进的计算机视觉和深学习算法,最终有可能培训神经网络学习物理过程的非线性和非扰动性特征。 在这项研究中,两个ResNet网络的结果通过调查全球和运动数量,甚至喷射和事件形状变量来介绍。 广泛使用的Lund弦碎裂模型被用作以$\sqrt{s ⁇ 7$TeV质子-质子碰撞为基准,以预测进一步LHC能量的最相关的观测结果。