论文标题
复活$ b \ bar {b} h $带有运动形状
Resurrecting $b\bar{b}h$ with kinematic shapes
论文作者
论文摘要
$ b \ bar {b} $与higgs玻色子的相关生产可以为底部Quark Yukawa耦合的大小和阶段提供重要的探测，即$ y_b $。但是，信号被几个背景过程所笼罩，包括不可约$ zh，z \ to b \ bar {b} $背景。我们表明，对运动形状的分析为我们提供了一种具体的处方，用于将$ y_b $敏感的生产模式与不可减至和QCD-QED背景分开，并使用$ b \ bar {b}γγγ$最终状态。我们从游戏理论中汲取了一个页面，并使用沙普利值以运动量可测量的方式来解释促进的决策树，并为不同渠道的运动形状中的方差提供物理学见解，以帮助我们完成这一壮举。在机器学习算法上增加可解释性可以打开黑框，并使我们只能挑选出分析中最重要的运动变量。我们复活了使用全HL-LHC数据和FCC-HH的$ B \ bar {B} H $生产的运动形状研究来限制$ y_b $的阶段的希望。
The associated production of a $b\bar{b}$ pair with a Higgs boson could provide an important probe to both the size and the phase of the bottom-quark Yukawa coupling, $y_b$. However, the signal is shrouded by several background processes including the irreducible $Zh, Z\to b\bar{b}$ background. We show that the analysis of kinematic shapes provides us with a concrete prescription for separating the $y_b$-sensitive production modes from both the irreducible and the QCD-QED backgrounds using the $b\bar{b}γγ$ final state. We draw a page from game theory and use Shapley values to make Boosted Decision Trees interpretable in terms of kinematic measurables and provide physics insights into the variances in the kinematic shapes of the different channels that help us complete this feat. Adding interpretability to the machine learning algorithm opens up the black-box and allows us to cherry-pick only those kinematic variables that matter most in the analysis. We resurrect the hope of constraining the size and, possibly, the phase of $y_b$ using kinematic shape studies of $b\bar{b}h$ production with the full HL-LHC data and at FCC-hh.
