论文标题
在LHC上使用深度学习的最高学习和单顶夸克的生产
Application of deep learning in top pair and single top quark production at the LHC
论文作者
论文摘要
我们证明了一个非常有效的标签仪的性能适用于基于深神经网络算法的强发腐烂的顶级夸克对作为信号，并与QCD多射流背景事件进行比较。通过有限的计算资源，可以观察到提升的顶级夸克事件中的性能的显着提高。我们还比较了现代机器学习方法，并通过$ \ sqrt {s} = $ 14 TEV Proton-Proton Collider进行了较弱的互动，对增强的顶级和顶级夸克产生进行多变量分析。合并了最相关的已知背景过程。通过增强决策树（BDT），可能性和多层命中剂（MLP）的技术，对分析进行了训练，以与常规剪切和计数方法相比观察性能。
We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at $\sqrt{s}=$14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach.
