论文标题
在基于机器学习的方法中对带电强子多数的KNO缩放测试
Testing of KNO-scaling of charged hadron multiplicities within a Machine Learning based approach
论文作者
论文摘要
此处介绍了基于机器学习的方法的结果，以研究最终状态充电的强子和平均喷气多重性分布的缩放属性。具有不同复杂性的深度残留神经网络体系结构可用于预测由\ textsc {pythia} Monte Carlo事件发生器生成的Parton级最终状态的最终多重性分布。强调网络通过$ \ sqrt {s} = 7 $ tev事件培训，而对各种LHC Energies进行了预测，从$ \ sqrt {s} = 0.9 $ tev至13 tev。网络在HADRONIC级别采用了缩放特性，实际上保留了KNO尺度 - 尽管平均射流多样性分布的缩放在应用模型方面有所不同。
The results of a Machine Learning-based method is presented here to investigate the scaling properties of the final state charged hadron and mean jet multiplicity distributions. Deep residual neural network architectures with different complexities are utilized to predict the final state multiplicity distribution from the parton-level final state, generated by the \textsc{Pythia} Monte Carlo event generator. Hadronization networks were trained by $\sqrt{s}=7$ TeV events, while predictions have been made for various LHC energies from $\sqrt{s}=0.9$ TeV to 13 TeV. Scaling properties were adopted by the networks at hadronic level, indeed KNO-scaling is preserved -- although, the scaling of the mean jet multiplicity distributions varies for the applied models.
