Abstract

Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36–81 fb^{-1} of proton–proton collision data with a centre-of-mass energy of sqrt{s}=13 {text {Te}}{text {V}} collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-k_t jet algorithm with radius parameter R=0.4 is the primary jet definition used for both jet types. This result presents new jet energy scale and resolution measurements in the high pile-up conditions of late LHC Run 2 as well as a full calibration of particle-flow jets in ATLAS. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several in situ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets (|eta |<1.2) vary from 1% for a wide range of high-p_{{text {T}}} jets (250<p_{{text {T}}} <2000~{text {Ge}}{text {V}}), to 5% at very low p_{{text {T}}} (20~{text {Ge}}{text {V}}) and 3.5% at very high p_{{text {T}}} (>2.5~{text {Te}}{text {V}}). The relative jet energy resolution is measured and ranges from (24 pm 1.5)% at 20 {text {Ge}}{text {V}} to (6 pm 0.5)% at 300 {text {Ge}}{text {V}}.

Highlights

  • At the beginning of the chain, the pile-up corrections remove the excess energy due to additional proton–proton interactions within the same or nearby bunch crossings. These corrections consist of two components: a correction based on the jet area and transverse momentum density of the event, and a residual correction derived from Monte Carlo (MC) simulation and parameterized as a function of the mean number of interactions per bunch crossing (μ) and the number of reconstructed primary vertices in the event (NPV)

  • The calibration of the jet energy scale and resolution for jets reconstructed with the anti-kt algorithm with radius parameter R = 0.4 is presented

  • The measurements discussed here use 36–81 fb−1 of data recorded with the ATLAS detector during 2015–2017 in pp collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider

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Summary

The ATLAS detector

The ATLAS detector [10] at the LHC covers nearly the entire solid angle around the collision point. It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets. The ATLAS detector [10] at the LHC covers nearly the entire solid angle around the collision point.2 It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets. Interfaces that exist between each of these components, in particular between the barrel and endcap regions, provide for space to route various services and infrastructure, such as electrical and fiber-optic cabling, cooling, and support structures These so-called transition regions create discontinuities in the response of the calorimeter to both charged and neutral particles due to energy absorption in the inactive materials and changes in the geometry of the active materials of the calorimeters. Interesting events are selected to be recorded by the firstlevel trigger system implemented in custom hardware, fol-

Data and Monte Carlo simulated samples
Jet reconstruction
Jet energy scale calibration
Pile-up corrections
Jet energy scale and η calibration
Global sequential calibration
In situ jet calibrations
Relative calibration measurement in η using dijet events
High- pT jet calibration using multijet balance
Pile-up and the in situ analyses
In situ combination
Systematic uncertainties
Uncertainty correlations and reductions
Uncertainties for EMtopo and PFlow jets
Jet energy resolution
Resolution measurement using dijet events
Noise measurement using random cones
Combination of in situ jet energy resolution
Application of JER and its systematic uncertainties
Conclusions
Findings
Methods

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