论文标题
通过深度学习，通过特定于分析的快速模拟在LHC上的数据增强
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
论文作者
论文摘要
我们提出了一个基于深层神经网络的快速模拟应用程序，该应用程序旨在创建大型分析的数据集。以SQRT（S）= 13 TEV Proton-Proton碰撞产生的W+JET事件的产生，我们训练一个神经网络，以对检测器分辨率的效应进行建模作为转移函数，该效应作用于分析特定的相关特征，即在没有探测器效应的情况下计算出的一系列相关特征。基于此模型，我们提出了一种新颖的快速仿真工作流，该工作流程从大量发生器级事件开始，以提供大型分析特定的样本。这种方法的采用将导致碰撞模拟工作流的计算和存储要求降低速度顺序。该策略可以帮助高能物理学界面对未来高劳斯力LHC的计算挑战。
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.
