论文标题
一般食谱，形成输入空间以进行深度学习分析的HEP散射过程
General recipe to form input space for deep learning analysis of HEP scattering processes
论文作者
论文摘要
深度学习神经网络技术（DNN）是对撞机实验的多变量数据分析的最有效，最通用的方法之一。分析的重要步骤是优化多元技术的输入空间。在文章中，我们提出了一般配方，如何形成对围硬散射过程差异的低水平可观察结果敏感的集合。本文显示的是，如果没有对运动特性进行任何复杂分析，就可以使用拟议的一组低级可观察物组来实现DNN的最佳性能。
Deep learning neural network technique (DNN) is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to form the set of low-level observables sensitive for the differences in hard scattering processes at the colliders. It is shown in the paper that without any sophisticated analysis of the kinematic properties one can achieve close to optimal performance of DNN with the proposed general set of low-level observables.
