论文标题
使用机器学习技术在HE-LHC上进行非共鸣的Di-higgs搜索和希格斯玻色子自耦合测量的前景
Prospects of non-resonant di-Higgs searches and Higgs boson self-coupling measurement at the HE-LHC using machine learning techniques
论文作者
论文摘要
在拟议的LHC的高能升级中观察标准模型中非共鸣的Di-higgs的前景，$viz。$ $ he-lhc $〜$（$ \ sqrt {s} = 27〜 {\ rm tev} $ and $ \ rm tev} $ and $ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ {l} = 15〜15〜15〜根据其清洁度和信号产量，考虑了各种最终状态。在HE-LHC上搜索非共振双HIGGS生产在$ b \ bar {b}γγ$，$ b \ bar {b}τ^{+}τ{+}τ^{ - } $，$ b \ bar {b \ bar {b} $ b \ bar {b} zz^{*} $和$ b \ bar {b}μ^{+}μ^{ - } $ channels。信号背景歧视是通过使用$〜$ tmva框架，XGBoost工具包和深神经网络$〜$（DNN）中的Boost decist Yeard Desired Decod树$〜$〜$（BDTD）算法进行多元分析进行的。还研究了希格斯对生产的运动学的变化，这是希格斯玻色子的自我耦合的函数，$λ_{h} $。 $ b \ bar {b}γγ$，$ b \ bar {b}τ^{+}τ^{ - } $和$ b \ b \ bar {b} www^{>
The prospects of observing the non-resonant di-Higgs production in the Standard Model at the proposed high energy upgrade of the LHC, $viz.$ the HE-LHC$~$($\sqrt{s}=27~{\rm TeV}$ and $\mathcal{L} = 15~{\rm ab^{-1}}$) is studied. Various di-Higgs final states are considered based on their cleanliness and signal yields. The search for the non-resonant double Higgs production at the HE-LHC is performed in the $b\bar{b}γγ$, $b\bar{b}τ^{+}τ^{-}$, $b\bar{b}WW^{*}$, $WW^{*}γγ$, $b\bar{b}ZZ^{*}$ and $b\bar{b}μ^{+}μ^{-}$ channels. The signal-background discrimination is performed through multivariate analyses using the Boosted Decision Tree Decorrelated$~$(BDTD) algorithm in the$~$TMVA framework, the XGBoost toolkit and Deep Neural Network$~$(DNN). The variation in the kinematics of Higgs pair production as a function of the self-coupling of the Higgs boson, $λ_{h}$, is also studied. The ramifications of varying $λ_{h}$ on the $b\bar{b}γγ$, $b\bar{b}τ^{+}τ^{-}$ and $b\bar{b}WW^{*}$ search analyses optimized for the SM hypothesis is also explored.
