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

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.

arXiv comment8 pages,7 figures

References in corpus (26)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• FastJet user manual
• Herwig 7.0 / Herwig++ 3.0 Release Note
• WHIZARD: Simulating Multi-Particle Processes at LHC and ILC
• First Look at the Physics Case of TLEP
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Higgs coupling constants as a probe of new physics
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Quark and Gluon Jet Substructure
• Gauge invariant one-loop corrections to Higgs boson couplings in non-minimal Higgs models
• Natural SUSY Predicts: Higgs Couplings
• Little Higgs theory confronted with the LHC Higgs data
• Physics cross sections and event generation of $e^{+}{e}^{-}$ annihilations at the CEPC
• Measurement of the Higgs boson mass and $e^{+}{e}^{-}\to ZH$ cross section using $Z\to {\mu }^{+}{\mu }^{-}$ and $Z\to e^{+}{e}^{-}$ at the ILC
• Higgs Search by Neural Networks at LHC
• Two-loop top and bottom Yukawa corrections to the Higgs-boson masses in the complex MSSM
• Deep learning for the R-parity violating supersymmetry searches at the LHC
• Interplay between new physics in one-loop Higgs couplings and the top-quark Yukawa coupling
• Measurements of decay branching fractions of $H\to b\bar{b}/c\bar{c}/gg$ in associated $(e^{+}{e}^{-}/{\mu }^{+}{\mu }^{-})H$ production at the CEPC
• Probing the Higgs boson-gluon coupling via the jet energy profile at $e^{+}{e}^{-}$ colliders
• Probing extra Yukawa couplings by precision measurements of Higgs properties
• Is there new particle running in the loop-induced $H\gamma \gamma$ and $Hgg$ vertex?

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