未来轻子对撞机上通过W玻色子融合探测希格斯自耦合的高能探针
High energy probes of Higgs self-coupling via $W$ boson fusion at future lepton colliders
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中文总结 AI 辅助
该研究在3 TeV质心能量的CLIC上，通过开发基于GNN的分类器，实现了5 ab⁻¹积分亮度下约20σ的信号显著性，显著提升了对希格斯自耦合的探测灵敏度，优于HL-LHC的预期。
中文摘要 AI 辅助
我们在质心能量√s=3 TeV的CLIC（未来环形对撞机）中，研究通过W玻色子融合产生的双希格斯过程对希格斯自耦合的灵敏度。在κ框架内，我们分析了希格斯自耦合修饰因子κ_λ与希格斯规范耦合修饰因子κ_V、κ_2V之间的相互作用。为增强信号与本底的区分度，我们开发了一种基于图神经网络（GNN）的分类器，该分类器在积分亮度5 ab⁻¹下可达到约20σ的信号显著性，显著超出预计的HL-LHC（高亮度大型强子对撞机）灵敏度。我们的结果表明，高能轻子对撞机结合基于图的机器学习，能为希格斯自耦合提供优异的灵敏度，并为电弱 sector（领域）中的新物理提供有力探针，可区分线性与非线性实现的电弱对称性破缺。
英文摘要
We investigate the sensitivity to the Higgs self-coupling through $W$ boson fusion di-Higgs production at CLIC with a center-of-mass energy of $\sqrt{s}=3$ TeV. We study the interplay between the Higgs self-coupling modifier ($κ_λ$) and Higgs-gauge coupling modifiers ($κ_{V}$ and $κ_{2V}$) within the $κ$ framework. To enhance the separation between signal and background, we develop a graph neural network (GNN) based classifier that achieves a signal significance of $\mathscr{Z}\approx 20~σ$ at $5~\mathrm{ab}^{-1}$, substantially exceeding projected HL-LHC sensitivity. Our results demonstrate that high-energy lepton colliders, combined with graph-based machine learning, provide excellent sensitivity to the Higgs self-coupling and offer a powerful probe of new physics in the electroweak sector, disentangling linearly and non-linearly realized electroweak symmetry breaking.
