Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning

TL;DR

This paper explores how machine learning, particularly graph neural networks, can enhance the detection of Higgs boson pairs in hadronic decay channels at future colliders, improving measurements of Higgs properties.

Contribution

It introduces the application of advanced machine learning techniques to hadronic Higgs decay modes to increase sensitivity to Higgs pair production at future colliders.

Findings

Graph neural networks significantly improve signal discrimination.

Boosted hadronic decay modes enhance collider sensitivity.

Potential for more precise Higgs property measurements.

Abstract

One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, including precision measurements of the Higgs sector. This includes a detailed study of Higgs boson (H) pair production, which can reveal the H self-coupling. Since the discovery of the Higgs boson, a large campaign of measurements of the properties of the Higgs boson has begun and many new ideas have emerged during the completion of this program. One such idea is the use of highly boosted and merged hadronic decays of the Higgs boson ($\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}$, $\mathrm{H}\to\mathrm{W}\mathrm{W}\to\mathrm{q}\bar{\mathrm{q}}\mathrm{q}\bar{\mathrm{q}}$) with machine learning methods to improve the signal-to-background discrimination. In this white paper, we champion the use of these modes to boost the sensitivity of future collider physics programs to Higgs boson pair production, the Higgs self-coupling, and Higgs-vector boson couplings. We demonstrate the potential improvement possible at the Future Circular Collider in hadron mode, especially with the use of graph neural networks.