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

The CMS Collaboration, A. Tumasyan, W. Adam, P. Eerola, Laurent Forthomme, H. Kirschenmann, K. Österberg, M. Voutilainen, Shudhashil Bharthuar, Erik Brücken, F. Garcia, J. Havukainen, Jaana Heikkilä, Minsuk Kim, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. LindénMikko Lotti, P. Luukka, Laura Martikainen, Mikael Erkki Johannes Myllymäki, Jennifer Ott, Juska Pekkanen, H. Siikonen, E. Tuominen, J. Tuominiemi, Jussi Viinikainen, H. Petrow, T. Tuuva

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Article numberP03014
JournalJournal of Instrumentation
Volume17
Issue number3
Number of pages65
ISSN1748-0221
DOIs
Publication statusPublished - Mar 2022
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 114 Physical sciences
  • Large detector-systems performance
  • Pattern recognition
  • cluster finding
  • calibration and fitting methods
  • FRAGMENTATION

Cite this