Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb(-1) at root s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.

A new calibration method for charm jet identification validated with proton-proton collision events at √s = 13 TeV

Quaranta, Claudio;Amapane, Nicola;Arcidiacono, Roberta;Argiro, Stefano;Bellan, Riccardo;Costa, Marco;Covarelli, Roberto;Migliore, Ernesto;Obertino, Maria Margherita;Pacher, Luca;Sola, Valentina;Solano, Ada;
2022-01-01

Abstract

Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb(-1) at root s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.
2022
Inglese
Esperti anonimi
17
03
03014
03014
64
Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods
SPAGNA
1 – prodotto con file in versione Open Access (allegherò il file al passo 6 - Carica)
262
2363
Tumasyan, Armen; Adam, Wolfgang; Andrejkovic, Janik Walter; Bergauer, Thomas; Chatterjee, Suman; Dragicevic, Marko; Escalante Del Valle, Alberto; Frue...espandi
info:eu-repo/semantics/article
open
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1960890
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