论文标题
大型强子对撞机上的Mono-Higgs + MET信号：一项关于$γγ$和$ b \ bar {b} $最终状态的研究
The mono-Higgs + MET signal at the Large Hadron Collider: a study on the $γγ$ and $b\bar{b}$ final states
论文作者
论文摘要
我们研究了在高亮度大型强调对撞机（LHC）上产生深色母校信号的通道{\ em Mono-higgs + met}的潜力。作为例证，已经选择了Higgs门户场景中的标量暗物质，通过假设尺寸-6操作员的存在来确保其现象学生存能力确保，这些尺寸-6操作员可以在直接搜索实验中取消某些振幅以弹性散射的弹性散射。发现这些运算符对单希格斯信号具有不可忽略的贡献。此后，我们对此信号进行了详细的分析，随附的MET提供了在抑制背景中有用的手柄。已经研究了Higgs的信号，这些信号已研究到Diphoton和$ b {\ bar b} $通道。首先提出了基于剪切的模拟，然后证明如何通过基于增强决策树和人工神经网络的分析来提高统计意义。发现改进对于$ b {\ bar b} $ channel特别明显。
We investigate the potential of the channel {\em mono-Higgs + MET} in yielding signals of dark mater at the high-luminosity Large Hadron Collider (LHC). As illustration, a scalar dark matter in a Higgs portal scenario has been chosen, whose phenomenological viability has been ensured by postulating the existence of dimension-6 operators that enable cancellation in certain amplitudes for elastic scattering of dark matter in direct search experiments. These operators are found to have non-negligible contribution to the mono-Higgs signal. Thereafter, we carry out a detailed analysis of this signal, with the accompanying MET providing a useful handle in suppressing backgrounds. Signals for the Higgs decaying into both the diphoton and $b{\bar b}$ channels have been studied. A cut-based simulation is presented first, followed by a demonstration of how the statistical significance can be improved through analyses based on Boosted Decision Trees and Artificial Neural Network. The improvement is found to be especially noticeable for the $b{\bar b}$ channel.
