利用参数化机器学习通过 $bbZZ$ 道中共振 $HH$ 产生探测扩展单态标量模型中的电弱相变
Probing Electroweak Phase Transition in Extended Singlet Scalar Model with Resonant $HH$ production in $bbZZ$ Channel using Parameterized Machine Learning
AI总结：
提出在扩展单态标量模型中用参数化机器学习分析 HL-LHC 的 $bbZZ$ 共振双希格斯信号，以探测强一阶电弱相变，最高可发现 490 GeV 共振。
摘要：
本文研究了 14 TeV HL-LHC 上重希格斯玻色子的对撞机特征，其中重希格斯玻色子衰变为一对标准模型希格斯玻色子，后者进一步衰变至 $bbZZ$ 态，并最终衰变至 $bb\ell^{+}\ell^{-}ν_{\ell}ν_{\ell}$ 末态。为研究这一问题，我们考虑标准模型的单态标量扩展，并选择重希格斯玻色子的参数空间和质量，使其倾向于强一阶电弱相变。研究遵循 CMS 合作组的 $bbZZ$ 分析，并进一步使用参数化机器学习进行最终判别，这简化了训练过程，同时在各基准点范围内改善了信号与背景之间的区分。尽管分支比较低，利用 CMS 和 ATLAS 实验在 14 TeV HL-LHC 上以 3 ab$^{-1}$ 积分亮度收集的数据集，该道仍可成为电弱相变的潜在探针，并且共振双希格斯信号的产生在共振质量高达 490 GeV 时具有被发现的潜力。
英文摘要：
In this paper, a collider signature of a heavy Higgs boson at $14$ TeV HL-LHC is studied, where the heavy Higgs boson decays into a pair of standard model Higgs boson, which further decays to $bbZZ$ state and subsequently to $bb\ell^{+} \ell^{-}ν_{\ell} ν_{\ell}$ final state. To study this, we consider singlet scalar extension of the standard model and select the parameter space and mass of the heavy Higgs boson such that it prefers a strong first-order electroweak phase transition. The study is done following the $bbZZ$ analysis of CMS Collaboration and further using parameterized machine learning for final discrimination which simplifies the training process along with an improved discrimination between signal and background over the range of benchmark points. Despite the lower branching fraction, this channel can be a potential probe of the electroweak phase transition with the data sets collected by the CMS and ATLAS experiments at the $14$ TeV HL-LHC with $3$ $\rm{ab}^{-1}$ of integrated luminosity and a production of resonant di-Higgs signal can be potentially discovered up to 490 GeV of resonance mass.
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