Jet Energy Measurement and Its Systematic Uncertainty in Proton
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EUROPEAN ORGANISATION FOR NUCLEAR RESEARCH (CERN) CERN-PH-EP-2013-222 Submitted to: European Physics Journal C Jet energy measurementp and its systematic uncertainty in proton–proton collisions at s = 7 TeV with the ATLAS detector The ATLAS Collaboration Abstract The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with p the ATLAS detector using proton–proton collision data with a centre-of-mass energy of s = 7 TeV corresponding to an integrated luminosity of 4:7 fb−1. Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells using the anti-kt algorithm with distance parameters R = 0:4 or R = 0:6, and are calibrated using MC simulations. A residual JES correction is applied to account for differences between data and MC simulations. This correction and its systematic uncer- tainty are estimated using a combination of in situ techniques exploiting the transverse momentum jet balance between a jet and a reference object such as a photon or a Z boson, for 20 ≤ pT < 1000 GeV and pseudorapidities jhj < 4:5. The effect of multiple proton–proton interactions is corrected for, and an uncertainty is evaluated using in situ techniques. The smallest JES uncertainty of less than 1% is jet found in the central calorimeter region (jhj < 1:2) for jets with 55 ≤ pT < 500 GeV. For central jets at lower pT, the uncertainty is about 3%. A consistent JES estimate is found using measurements of the calorimeter response of single hadrons in proton–proton collisions and test-beam data, which also jet provide the estimate for pT > 1 TeV. The calibration of forward jets is derived from dijet pT balance measurements. The resulting uncertainty reaches its largest value of 6% for low-pT jets at jhj = 4:5. arXiv:1406.0076v3 [hep-ex] 28 Jan 2015 Additional JES uncertainties due to specific event topologies, such as close-by jets or selections of event samples with an enhanced content of jets originating from light quarks or gluons, are also dis- cussed. The magnitude of these uncertainties depends on the event sample used in a given physics analysis, but typically amounts to 0.5% to 3%. c 2015 CERN for the benefit of the ATLAS Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-3.0 license. 1 Contents 13.4 Combination results . 48 13.5 Comparison of the g–jet calibration methods . 49 1 Introduction . .2 13.6 Simplified description of the correlations . 49 2 The ATLAS detector . .2 13.7 Jet energy scale correlation scenarios . 50 2.1 Detector description . .2 13.8 Alternative reduced configurations . 51 2.2 Calorimeter pile-up sensitivity . .3 14 Comparison to jet energy scale uncertainty from single-hadron 3 Monte Carlo simulation of jets in the ATLAS detector . .3 response measurements . 52 3.1 Inclusive jet Monte Carlo simulation samples . .3 15 Jet energy scale uncertainty from the W boson mass constraint 53 3.2 Z–jet and g–jet Monte Carlo simulation samples . .3 15.1 Event samples . 53 3.3 Top-quark pair Monte Carlo simulation samples . .4 15.2 Reconstruction of the W boson . 54 3.4 Minimum bias samples . .5 15.3 Extraction of the relative light jet scale . 54 3.5 Detector simulation . .5 15.4 Systematic uncertainties . 55 4 Dataset . .5 15.5 Results . 55 5 Jet reconstruction and calibration with the ATLAS detector .5 16 Systematic uncertainties on corrections for pile-up interactions 55 5.1 Topological clusters in the calorimeter . .5 16.1 Event and object selection . 55 5.2 Jet reconstruction and calibration . .7 16.2 Derivation of the systematic uncertainty . 55 5.3 Jet quality selection . .7 16.3 Summary on pile-up interaction corrections . 61 5.4 Track jets . .9 17 Close-by jet effects on jet energy scale . 62 5.5 Truth jets . .9 17.1 Samples and event selection . 62 5.6 Jet kinematics and directions . 10 17.2 Non-isolated jet energy scale uncertainty . 62 6 Jet energy correction for pile-up interactions . 10 18 Jet response difference for quark and gluon induced jets and 6.1 Pile-up correction method . 10 associated uncertainty . 63 6.2 Principal pile-up correction strategy . 10 18.1 Event selection . 63 6.3 Derivation of pile-up correction parameters . 12 18.2 Calorimeter response to quark and gluon induced jets . 64 6.4 Pile-up validation with in situ techniques and effect of 18.3 Discrimination of light-quark and gluon induced jets . 66 out-of-time pile-up in different calorimeter regions . 14 18.4 Summary of the jet flavour dependence analysis . 66 19 Jets with heavy-flavour content . 66 7 In situ transverse momentum balance techniques . 14 19.1 Jet selection and response definition . 67 7.1 Relative in situ calibration between the central and 19.2 Track selection . 68 forward rapidity regions . 14 19.3 Event selection . 68 7.2 In situ calibration methods for the central rapidity region 15 19.4 MC-based systematic uncertainties on the calorimeter 8 Relative forward-jet calibration using dijet events . 15 b-jet energy scale . 69 8.1 Intercalibration using events with dijet topologies . 15 19.5 Calorimeter jet energy measurement validation using 8.2 Event selection for dijet analysis . 16 tracks . 69 8.3 Dijet balance results . 17 19.6 Systematic uncertainties . 69 8.4 Systematic uncertainty . 19 19.7 Results . 73 8.5 Summary of the h-intercalibration and its uncertainties 21 19.8 Semileptonic correction and associated uncertainties . 73 9 Jet energy calibration using Z–jet events . 21 19.9 Semileptonic neutrino energy validation using dijet bal- 9.1 Description of the p balance method . 21 T ance . 74 9.2 Selection of Z–jet events . 21 19.10Conclusions on heavy-flavour jets . 74 p 9.3 Measurement of the T balance . 23 20 Jet response in problematic calorimeter regions . 75 9.4 Measuring out-of-cone radiation and underlying event 20.1 Correction algorithms for non-operating calorimeter contributions . 23 modules . 75 9.5 Systematic uncertainties . 24 20.2 Performance of the bad calorimeter region corrections . 76 9.6 Summary of the Z–jet analysis . 27 21 Summary of the total jet energy scale systematic uncertainty 76 10 Jet energy calibration using g–jet events . 27 22 Conclusions . 84 10.1 In situ jet calibration techniques . 27 23 Acknowledgements . 84 10.2 Event selection of g–jet events . 27 10.3 Jet response measurement . 31 10.4 Systematic uncertainties of photon–jet balance . 31 10.5 Summary of the g–jet analysis . 34 11 High-pT jet energy calibration using multijet events . 34 11.1 Multijet balance technique and uncertainty propagation 34 11.2 Selection of multijet events . 37 11.3 Multijet balance measurement . 37 11.4 Systematic uncertainties on the multijet balance . 37 11.5 Summary of multijet analysis . 42 12 Forward-jet energy measurement validation using Z–jet and g–jet data . 42 13 Jet energy calibration and uncertainty combination . 44 13.1 Overview of the combined JES calibration procedure . 44 13.2 Combination technique . 44 13.3 Uncertainty sources of the in situ calibration techniques 46 2 1 Introduction sult of the jet calibration, including its systematic uncertainty, from the combination of the in situ techniques. Jets are the dominant feature of high-energy, hard proton–pro- Section 14 compares the JES uncertainty as derived from ton interactions at the Large Hadron Collider (LHC) at CERN. the single-hadron calorimeter response measurements to that They are key ingredients of many physics measurements and obtained from the in situ method based on pT balance dis- for searches for new phenomena. In this paper, jets are ob- cussed in the preceding sections. Comparisons to JES uncer- served as groups of topologically related energy deposits in the tainties using the W boson mass constraint in final states with ATLAS calorimeters, associated with tracks of charged parti- hadronically decaying W bosons are presented in Sect. 15. cles as measured in the inner tacking detector. They are recon- Additional contributions to the systematic uncertainties of structed with the anti-kt jet algorithm [1] and are calibrated us- the jet measurement in ATLAS are presented in Sects. 16 to ing Monte Carlo (MC) simulation. 18, where the correction for the effect of additional proton– A first estimate of the jet energy scale (JES) uncertainty of proton interactions in the event, the presence of other close-by about 5% to 9% depending on the jet transverse momentum jets, and the response dependence on the jet fragmentation (jet (pT), described in Ref. [2], is based on information available flavour) are discussed. The uncertainties for explicitly tagged before the first proton–proton collisions at the LHC, and initial jets with heavy-flavour content are outlined in Sect. 19. A brief proton–proton collision data taken in 2010. A reduced uncer- discussion of the correction of the calorimeter energy in regions tainty of about 2:5% in the central calorimeter region over a with hardware failures and the associated uncertainty on the jet wide pT range of 60 . pT < 800 GeV was achieved after ap- energy measurement is presented in Sect. 20. plying the increased knowledge of the detector performance A summary of the total jet energy scale uncertainty is given obtained during the analysis of this first year of ATLAS data in Sect. 21. Conclusions follow in Sect. 22. A comparison of taking [3]. This estimation used single-hadron calorimeter re- the systematic uncertainties of the JES in ATLAS with previous sponse measurements, systematic variations of MC simulation calibrations is presented in Appendix A.