Towards recognizing the light facet of the Higgs Boson

TL;DR

This paper explores advanced machine learning methods to detect the Higgs boson decay to gluons at the 14 TeV LHC, aiming to improve observation prospects and set upper bounds on the decay rate.

Contribution

It introduces a novel approach combining convolutional neural networks and boosted decision trees to enhance Higgs to gluon decay detection.

Findings

Potential to observe Higgs to gluon decays with 2.4σ significance.

Achieves an upper bound of 1.74 times the Standard Model rate at 95% CL.

Significant improvement over previous cut-and-count analysis methods.

Abstract

The Higgs boson couplings to bottom and top quarks have been measured and agree well with the Standard Model predictions. Decays to lighter quarks and gluons, however, remain elusive. Observing these decays is essential to complete the picture of the Higgs boson interactions. In this work, we present the perspectives for the 14 TeV LHC to observe the Higgs boson decay to gluon jets assembling convolutional neural networks, trained to recognize abstract jet images constructed embodying particle flow information, and boosted decision trees with kinetic information from Higgs-strahlung $ZH\to \ell^+\ell^- + gg$ events. We show that this approach might be able to observe Higgs to gluon decays with a significance of around $2.4\sigma$ improving significantly previous prospects based on cut-and-count analysis. An upper bound of $BR(H\to gg)\leq 1.74\times BR^{SM}(H\to gg)$ at 95\% confidence level after 3000 fb$^{-1}$ of data is obtained using these machine learning techniques.