TY - JOUR
T1 - A new calibration method for charm jet identification validated with proton-proton collision events at root s=13 TeV
AU - The CMS Collaboration
AU - Tumasyan, A.
AU - Adam, W.
AU - Eerola, P.
AU - Forthomme, Laurent
AU - Kirschenmann, H.
AU - Österberg, K.
AU - Voutilainen, M.
AU - Bharthuar, Shudhashil
AU - Brücken, Erik
AU - Garcia, F.
AU - Havukainen, J.
AU - Heikkilä, Jaana
AU - Kim, Minsuk
AU - Kinnunen, R.
AU - Lampén, T.
AU - Lassila-Perini, K.
AU - Laurila, S.
AU - Lehti, S.
AU - Lindén, T.
AU - Lotti, Mikko
AU - Luukka, P.
AU - Martikainen, Laura
AU - Myllymäki, Mikael Erkki Johannes
AU - Ott, Jennifer
AU - Pekkanen, Juska
AU - Siikonen, H.
AU - Tuominen, E.
AU - Tuominiemi, J.
AU - Viinikainen, Jussi
AU - Petrow, H.
AU - Tuuva, T.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - 114 Physical sciences
KW - Large detector-systems performance
KW - Pattern recognition
KW - cluster finding
KW - calibration and fitting methods
KW - FRAGMENTATION
U2 - 10.1088/1748-0221/17/03/P03014
DO - 10.1088/1748-0221/17/03/P03014
M3 - Article
SN - 1748-0221
VL - 17
JO - Journal of Instrumentation
JF - Journal of Instrumentation
IS - 3
M1 - P03014
ER -