Enhanced sensitivity to the H → Zγ → ℓ+ℓ−γ decay at the LHC using machine learning and novel kinematic observables

A bstract At LHC energies, the Drell-Yan (Z/γ *) processes have a substantially large cross section. Their di-lepton (ℓ + ℓ −) 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 * → ℓ + ℓ − background from the H → Zγ → ℓ + ℓ − γ signal at s=13 s = 13 TeV by leveraging Monte Carlo simulated data. The analysis introduces physics-motivated correlated observables derived from the two-dimensional (P 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 Higgs, θ Zγ) plane increases the signal-to-background ratio to 2. 1% and 3. 4% for the electron and muon channels, respectively, near the Higgs mass. This work demonstrates that combining kinematic correlations with interpretable multivariate techniques improves 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.