论文标题
LHC信号作为暗物质门户网站：基于剪切的方法和梯度提升和神经网络的改进
LHC signals of triplet scalars as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks
论文作者
论文摘要
我们考虑了一种场景，其中SU（2）三重态标量充当标量暗物质粒子的门户。我们确定了参数空间的区域，其中这样的三胞胎与通常的Higgs Doublet共存，并与所有理论和中微子，加速器和暗物质约束稳定，而三胞胎主导的中性状态具有实质性的隐形分支分数。在最终状态下，研究了LHC信号，在最终状态下，在高亮度运行时可以直接可检测到基于削减的分析的某些基准分析的某些基准分析，可以在高亮度点上进行其他基准，但在高光度上可以取得重要的网络，可以在高亮度点上进行良好的网络，以实现逐步/neural neural neuration neural neural neural neural ne Nevor，预测有一个基于基于基础的基准的基于基于基于基础的基准。
We consider a scenario where an SU(2) triplet scalar acts as the portal for a scalar dark matter particle. We identify regions of the parameter space, where such a triplet coexists with the usual Higgs doublet consistently with all theoretical as well as neutrino, accelerator and dark matter constraints, and the triplet-dominated neutral state has substantial invisible branching fraction. LHC signals are investigated for such regions, in the final state {\em same-sign dilepton + $\ge$ 2 jets + $\not E_T$.} While straightforward detectability at the high-luminosity run is predicted for some benchmark points in a cut-based analysis, there are other benchmarks where one has to resort to gradient boosting/neural network techniques in order to achieve appreciable signal significance.
