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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.



