论文标题
通过图神经网络增强了Higgs喷气式重建
The Boosted Higgs Jet Reconstruction via Graph Neural Network
论文作者
论文摘要
通过将每个对撞机事件表示为点云，我们采用了图形卷积网络（GCN），并以焦点损失来重建其中的希格斯喷气机。与使用JET子结构信息的传统方法相比，该方法提供了更高的HIGG标记效率和更好的重建精度。 GCN接受了$ H $+JETS流程事件的培训，能够在几个不同过程的事件中检测到Higgs喷气机，即使在事件中有增强的重粒子以外的其他重粒子时的性能降低。我们还通过将其应用于$ t \ bar {t} $进程来证明GCN的信号和背景区分能力。以网络的输出为新功能来补充传统的喷气子结构变量，可以将$ t \ bar {t} $事件与$ h $+jets事件相距更远。
By representing each collider event as a point cloud, we adopt the Graphic Convolutional Network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction accuracy than the traditional methods which use jet substructure information. The GCN, which is trained on events of the $H$+jets process, is capable of detecting a Higgs jet in events of several different processes, even though the performance degrades when there are boosted heavy particles other than the Higgs in the event. We also demonstrate the signal and background discrimination capacity of the GCN by applying it to the $t\bar{t}$ process. Taking the outputs of the network as new features to complement the traditional jet substructure variables, the $t\bar{t}$ events can be separated further from the $H$+jets events.
