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->b\bar{b} and H->c\bar{c} decays using a LHCb dataset corresponding to an integrated luminosity of 1.6 fb-1. The observed (expected) 95% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the H->b\bar{b} process, and 1003 (1834) times the SM cross-section for the H->c\bar{c} process.

Machine learning techniques for jet reconstruction at LHCb and application to the search for H->b\bar{b} and H->c\bar{c} in \sqrt{s}=13 TeV pp collisions

Celestino I.
Membro del Collaboration Group
;
Cordova G.
Membro del Collaboration Group
;
Kleijne N.
Membro del Collaboration Group
;
Morello M. J.
Membro del Collaboration Group
;
Passaro D.
Membro del Collaboration Group
;
Pica L.
Membro del Collaboration Group
;
Riccardi D.
Membro del Collaboration Group
;
Xu A.
Membro del Collaboration Group
;
2026

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->b\bar{b} and H->c\bar{c} decays using a LHCb dataset corresponding to an integrated luminosity of 1.6 fb-1. The observed (expected) 95% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the H->b\bar{b} process, and 1003 (1834) times the SM cross-section for the H->c\bar{c} process.
2026
Settore FIS/01 - Fisica Sperimentale
Settore PHYS-01/A - Fisica sperimentale delle interazioni fondamentali e applicazioni
Hadron-Hadron Scattering; Higgs Physics; Jet Physics; Jets
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/172243
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