Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to (Formula presented) or (Formula presented), have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at (Formula presented) during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.

Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at √( s ) = 13 TeV

Calzaferri, S.
Membro del Collaboration Group
;
Montagna, P.
Membro del Collaboration Group
;
Riccardi, C.
Membro del Collaboration Group
;
Salvini, P.
Membro del Collaboration Group
;
Vai, I.
Membro del Collaboration Group
;
Vitulo, P.
Membro del Collaboration Group
;
2025-01-01

Abstract

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to (Formula presented) or (Formula presented), have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at (Formula presented) during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.
2025
Esperti anonimi
Inglese
Internazionale
ELETTRONICO
20
11
calibration and fitting methods; cluster finding; Pattern recognition; Performance of High Energy Physics Detectors
https://iopscience.iop.org/article/10.1088/1748-0221/20/11/P11006
2398
info:eu-repo/semantics/article
262
Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Benato, L.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Hussain, P. S.; ...espandi
1 Contributo su Rivista::1.1 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1551717
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