# Improving the measurement of the Higgs boson-gluon coupling using convolutional neural networks at $e^+e^-$ colliders

## TL;DR

This paper demonstrates that convolutional neural networks significantly improve the precision of measuring the Higgs boson-gluon coupling at lepton colliders by analyzing jet constituents, reducing uncertainty by approximately 35%.

## Contribution

The study introduces CNN-based techniques for Higgs-gluon coupling measurement, outperforming traditional methods in collider data analysis.

## Key findings

- Uncertainty reduced from 1.94% to 1.28% with PYTHIA data.
- Uncertainty reduced from 1.82% to 1.22% with HERWIG data.
- CNNs utilizing jet constituent energy distributions enhance identification accuracy.

## Abstract

In this paper we propose to use convolutional neural networks (CNNs) to improve the precision measurement of the Higgs boson-gluon effective coupling at lepton colliders. The CNN is employed to recognize the Higgs boson and a $Z$ boson associated production process, with the Higgs boson decaying to a gluon pair and the $Z$ boson decaying to a lepton pair at the center-of-mass energy 250 GeV and integrated luminosity 5 ab$^{-1}$. By using CNNs, the uncertainty of the effective coupling measurement can be decreased from $1.94\%$ to about $1.28\%$ using the PYTHIA data and from $1.82\%$ to about $1.22\%$ using the HERWIG data in the Monte Carlo simulation. Moreover, the performance of CNNs using different final state constituents shows that the energy distributions of the leading and subleading jets constituents play a major role in the identification and the optimal uncertainty of effective coupling using CNNs is reduced by about $35\%$ compared to that using conventional method.