Abstract
Higgs Bosons produced via gluon-gluon fusion with large transverse momentum (pT ) are sensitive probes of physics beyond the Standard Model. However, high pT Higgs Boson production is contaminated by many production modes other than gluon-gluon fusion, including 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. By using modern machine learning techniques to analyze jet substructure and event information, we demonstrate the capability of machine learning to identify production modes accurately. 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 regions of phase space for other production modes.
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