評估希格斯玻色子罕見衰變的測量有助於理解粒子物理標準模型 （SM）以及希格斯玻色子衰變至正負電子對和光子的貢獻。尋找希格斯 玻色子衰變至Z玻色子和光子具有相對乾淨的終態，但在很大程度上受到 不可避免的背景p-p→Zγ和可減少的背景Z玻色子及噴流過程的影響。為 增強LHC Run-2數據集的分析，已實施了多種新的分析技術。光子識別在 多變量分析中起著關鍵作用，可減少Z+Jets的貢獻。本研究考慮了來自 H→Zγ的光子和來自Z+Jets的光子的特定變數，使用LHC Run-2 Ultra Legacy（UL）模擬數據進行了機器學習。學習結果與CMS-EGM團隊為 Ultra-Legacy的標准光子識別模型進行了比較。預期的中位數顯著性為 1.15σ，表明改進了8.4％。我們的目標是將這一新發展應用於Run-2 Ultra Legacy和Run-3分析。 ;The measurement of the rare decay of the Higgs boson would help understand the Standard Model(SM) of particle physics and the NLO contribution of H→ll γ final state. The search for H→Zγ has a relatively clean final state but is significantly con tributed by irreducible background SM Zγ and reducible background Z+Jets processes. To enhance the analysis of the LHC Run-2 dataset, several new analysis techniques have been implemented. Photon identification plays a crucial role in reducing the contribution of Z+jets in multivariate analysis. This study considers specific shower shape and isolation variables of prompt photons from H→Zγ and jet-fake photons from Z+Jets using LHC Run-2 Ultra-Legacy(UL) simulations. The training results are compared with standard photon identification trained by CMS-EGM groups for ULtra Legacy. The expected significance is 1.15 σ, indicating an improvement of 8.4%. We aim to implement this new development for Run-2 Ultra-Legacy and Run-3 analysis.
