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arXiv 2009.05930hep-phhep-ex
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Disentangling Boosted Higgs Boson Production Modes with Machine Learning

Yi-Lun Chung, Shih-Chieh Hsu, Benjamin Nachman

英文摘要:

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.

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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