论文标题
大型强子对撞机的现象学带有深度学习：类似矢量的夸克的情况腐烂到点亮喷气机
Phenomenology at the Large Hadron Collider with Deep Learning: the case of vector-like quarks decaying to light jets
论文作者
论文摘要
在这项工作中，我们继续探索TEV尺度矢量般的fermion签名，灵感来自基于Trinification Gauge组的大统一场景。在LHC上，在多喷式和带电的Lepton和缺少能量签名的LHC的矢量样Quarks（VLQ）的成对产生拓扑中，给予了特别的重点。我们采用基于进化算法的深度学习方法和技术，这些算法优化了神经网络构建中的超参数，其目标是最大化Asimov估算不同VLQ质量的估计。在本文中，我们通过将检测器图像（也称为JET图像）和包含来自最终状态的运动学信息的表格数据同时组合来考虑创新方法的含义。通过这种技术，我们能够排除特定于所考虑模型的VLQ，以在LHC程序的Run-III阶段的高亮度中提高800 GEV的质量。
In this work, we continue our exploration of TeV-scale vector-like fermion signatures inspired by a Grand Unification scenario based on the trinification gauge group. A particular focus is given to pair-production topologies of vector-like quarks (VLQs) at the LHC, in a multi-jet plus a charged lepton and a missing energy signature. We employ Deep Learning methods and techniques based in evolutive algorithms that optimize hyper-parameters in the neural network construction, whose objective is to maximise the Asimov estimate for distinct VLQ masses. In this article, we consider the implications of an innovative approach by simultaneously combining detector images (also known as jet images) and tabular data containing kinematic information from the final states. With this technique we are able to exclude VLQs, that are specific for the considered model, to up a mass of 800 GeV in both the high-luminosity the Run-III phases of the LHC programme.
