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Higgs Bosons produced via gluon-gluon fusion (ggF) with large transversemomentum ($p_T$) are sensitive probes of physics beyond the Standard Model.However, high $p_T$ Higgs Boson production is contaminated by a diversity ofproduction modes other than ggF: vector boson fusion, production of a Higgsboson in association with a vector boson, and production of a Higgs boson witha top-quark pair. Combining jet substructure and event information with modernmachine learning, we demonstrate the ability to focus on particular productionmodes. These tools hold great discovery potential for boosted Higgs bosonsproduced via ggF and may also provide additional information about the HiggsBoson sector of the Standard Model in extreme phase space regions for otherproduction modes as well.

Disentangling Boosted Higgs Boson Production Modes with Machine Learning