Disentangling Boosted Higgs Boson Production Modes with Machine Learning

TL;DR

This paper develops machine learning techniques combining jet substructure and event data to distinguish Higgs boson production modes, enhancing the study of boosted Higgs bosons and probing physics beyond the Standard Model.

Contribution

It introduces a novel machine learning approach that disentangles Higgs production modes using jet and event features, improving mode identification in high transverse momentum regions.

Findings

Effective separation of Higgs production modes using ML methods.

Enhanced sensitivity to boosted Higgs bosons in high $p_T$ regions.

Potential to explore new physics beyond the Standard Model.

Abstract

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.