论文标题
从事件图像分割中重建增强的希格斯喷气机
Reconstructing boosted Higgs jets from event image segmentation
论文作者
论文摘要
基于JET图像方法将每个热量计细胞中的能量沉积视为像素强度，与传统的JET子结构分析相比，已经发现卷积神经网络（CNN）方法可实现JET标记的显着改善。在这项工作中，采用了面具R-CNN框架来重建类似撞机的事件中的希格斯喷气机，并考虑了堆积污染的影响。这种自动喷气重建方法比传统的喷气聚集和喷气子结构标记方法获得了HIGGS喷射检测效率更高，Higgs Boson四摩梅氏菌的精度更高。此外，对包含单个希格斯喷气机的事件进行训练的蒙版R-CNN能够在几个不同过程的事件中检测一个或多个希格斯喷气机，而没有明显的重建效率和准确性降解。网络的输出还可以作为$ t \ bar {t} $背景抑制作用的新手柄，并补充了传统的Jet子结构变量。
Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditional jet substructure analysis. In this work, the Mask R-CNN framework is adopted to reconstruct Higgs jets in collider-like events, with the effects of pileup contamination taken into account. This automatic jet reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and jet substructure tagging methods. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. The outputs of the network also serve as new handles for the $t\bar{t}$ background suppression, complementing to traditional jet substructure variables.
