Disentangling boosted Higgs Boson production modes with machine learning

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 $p_T$ Boson production is contaminated by a diversity modes other than ggF: vector boson fusion, in association boson, and top-quark pair. Combining jet substructure event information modern machine learning, we demonstrate ability to focus on particular modes. These tools hold great discovery potential for boosted bosons ggF may also provide additional about sector Model extreme phase space regions as well.