A new calibration method for charm jet identification validated with proton-proton collision events at root s=13 TeV
File(s)
Published version
Author(s)
Type
Journal Article
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 √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.
Date Issued
2022-03-01
Date Acceptance
2022-02-18
Citation
Journal of Instrumentation, 2022, 17 (3)
ISSN
1748-0221
Publisher
IOP Publishing
Journal / Book Title
Journal of Instrumentation
Volume
17
Issue
3
Copyright Statement
c 2022 CERN for the benefit of the CMS collaboration. Published by
IOP Publishing Ltd on behalf of Sissa Medialab. Original content from
this work may be used under the terms of the Creative Commons Attribution 4.0 licence.
Any further distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
IOP Publishing Ltd on behalf of Sissa Medialab. Original content from
this work may be used under the terms of the Creative Commons Attribution 4.0 licence.
Any further distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000775007900007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Instruments & Instrumentation
Large detector-systems performance
Pattern recognition
cluster finding
calibration and fitting methods
FRAGMENTATION
Publication Status
Published
Article Number
ARTN P03014
Date Publish Online
2022-03-17