Machine learning techniques for jet reconstruction at LHCb and application to the search for H → b b and H → c c in √(s)=13 TeV pp collisions

2026/01/23 by LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1194 · 1 voice
Physics and Astronomy · #hep-ex

paper · pdf

Abstract

Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between b-quark, c-quark, and light parton jets. These techniques are applied to a search for inclusive H → \bbbar and H → c\barcc decays using a LHCb dataset corresponding to an integrated luminosity of 1.6\invfb. The observed (expected) 95% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the H → b b process, and 1003 (1834) times the SM cross-section for the H → c c process.

Citations

• The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
• Transforming jet flavour tagging at ATLAS
• Discovery potential of LHCb Upgrade II
• Measurements of WH and ZH production with Higgs boson decays into bottom quarks and direct constraints on the charm Yukawa coupling in 13 TeV pp collisions with the ATLAS detector
• Higgs Physics at the HL-LHC and HE-LHC
• Machine Learning in High Energy Physics Community White Paper
• A Brief Introduction to PYTHIA 8.1
• Geant4 developments and applications
• PHOTOS Monte Carlo: a precision tool for QED correctionsin Z and W decays
• Presentation of search results: theCLstechnique

Discussions

• Machine learning techniques for jet reconstruction at LHCb and application to the search for H→bb̅ and H→cc̅ in √s=13 TeV pp collisions arxiv.org/abs/2601.16802 - The observed (expected) 95% confidenc [bsky, 2 points, 0 comments]

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