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Author  |
LHCb Collaboration (Aaij, R. et al); Fernandez Casani, A.; Jaimes Elles, S.J.; Jashal, B.K.; Libralon, S.; Lucio Martinez, M.; Martinez-Vidal, F.; Mazorra de Cos, J.; Oyanguren, A.; Rebollo De Miguel, M.; Sanderswood, I.; Svintozelskyi, V.; Velilla Serna, J.; Zhuo, J. |
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Title |
Machine learning techniques for jet reconstruction at LHCb and application to the search for H → b(b)over-bar and H → c(c)over-bar in √s=13 TeV pp collisions |
Type |
Journal Article |
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Year |
2026 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
07 |
Issue |
7 |
Pages |
276 - 32pp |
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Keywords |
Hadron-Hadron Scattering; Higgs Physics; Jet Physics; Jets |
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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 (b) over bar and H -> c (c) over bar 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 (b) over bar process, and 1003 (1834) times the SM cross-section for the H -> c (c) over bar process. |
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Address |
[Bonacci, R. B.; Dekkers, S.; Egede, U.; Hadavizadeh, T.; Harris, T. H.; Henderson, R. D. L.; Lane, J. J.; Liu, F. L.; Monk, M.; Song, R.; Walton, E. J.] Monash Univ, Sch Phys & Astron, Melbourne, Australia, Email: davide.zuliani@cern.ch |
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Springer |
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English |
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Notes |
WOS:001841217700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
7418 |
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