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arXiv 2601.13640hep-ph
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在LHC上利用机器学习和新型动力学可观测量增强H→Zγ→ℓ⁺ℓ⁻γ衰变的灵敏度

Enhanced sensitivity to the $H \to Zγ\to \ell^+\ell^-γ$ decay at the LHC using machine learning and novel kinematic observables

Indian Institute of Technology Mandi(印度理工学院曼迪分校)

Manisha Kumari, Amal Sarkar

AI总结:

本文通过引入新的动力学可观测量和机器学习方法,提升H→Zγ→ℓ⁺ℓ⁻γ信号与背景的分离能力,提高灵敏度并增强背景抑制。

摘要:

在LHC上利用机器学习和新型动力学可观测量增强H→Zγ→ℓ⁺ℓ⁻γ衰变的灵敏度,通过引入基于二维(P_Higgs, θ_Zγ)平面的物理动机相关可观测量,改进了Z/γ*→ℓ⁺ℓ⁻背景与H→Zγ→ℓ⁺ℓ⁻γ信号的区分和抑制。分析采用多变量分析(MVA)方法,结合XGBoost分类器,通过引入额外的物理动机相关可观测量,实现了性能的显著提升。在电子和缪子通道中,ROC曲线下的面积(AUC)显著增加,表明扩展特征集的有效性。进一步优化背景拒绝,使信号与背景比分别达到电子通道2.1%和缪子通道3.4%,接近Higgs质量。该工作展示了结合动量相关性和可解释的多变量技术可提高灵敏度和稳健的背景拒绝能力。该方法具有灵活性,可广泛应用于稀有Higgs衰变、共振搜索及超出标准模型的研究。

英文摘要:

At LHC energies, the Drell--Yan ($Z/γ^{*}$) processes have a substantially large cross section. Their di-lepton ($\ell^+\ell^-$) final state contributes significantly to many resonant signal regions, making them one of the dominant backgrounds in numerous physics analyses. The study focuses on improving the discrimination and suppression of the $Z/γ^{*} \rightarrow \ell^{+}\ell^{-}$ background from the $H \rightarrow Zγ\rightarrow \ell^{+}\ell^{-}γ$ signal at $\sqrt{s}=13~\text{TeV}$ by leveraging Monte Carlo simulated data. The analysis introduces physics-motivated correlated observables derived from the two-dimensional $(P_{\mathrm{Higgs}}, θ_{Zγ})$ plane. These observables encode differences in angular and momentum information to enhance signal--background separation while maintaining high signal efficiency. We present a multivariate analysis (MVA) employing a Boosted Decision Tree (XGBoost) classifier. By incorporating additional physics-motivated correlated observables, the classifier achieves measurable improvements in performance. A significant increase in the area under the ROC curve (AUC) is observed in both the electron and muon channels, demonstrating the effectiveness of the expanded feature set. Further, optimised background rejection using $(P_{\mathrm{Higgs}}, θ_{Zγ})$ plane increases the signal-to-background ratio to 2.1\% and 3.4\% for the electron and muon channel respectively near the Higgs mass. This work demonstrates that combining kinematic correlations with interpretable multivariate techniques leads to improved sensitivity and robust background rejection. The approach is flexible and can be readily applied to a wide range of analyses, including rare Higgs decays, resonant searches, and studies beyond the Standard Model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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