论文标题
用机器学习解开增强的希格斯玻色子生产模式
Disentangling Boosted Higgs Boson Production Modes with Machine Learning
论文作者
论文摘要
通过具有较大横向动量（$ p_t $）的Gluon-Gluon融合（GGF）产生的Higgs玻色子是超出标准模型以外的物理探针的敏感探针。但是，高$ p_t $ higgs玻色子的生产受到GGF以外的各种生产模式的污染：矢量玻色融合，生产Higgs Boson与Vector Boson合作，并生产了Higgs Boson和Top-Ququark对。将JET子结构和事件信息与现代机器学习相结合，我们证明了专注于特定生产模式的能力。这些工具对通过GGF生产的增强的HIGGS玻色子具有巨大的发现潜力，并且还可能提供有关其他生产模式的极端空间区域中标准模型的Higgs玻色子部门的其他信息。
Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.
