Jet energy scale and resolution measured in proton-proton collisions at s √ =13 TeV with the ATLAS detector
EEUROPEAN ORGANISATION FOR NUCLEAR RESEARCH (CERN)
Submitted to: EPJC CERN-EP-2020-0837th July 2020
Jet energy scale and resolution measured inproton–proton collisions at √ s =
13 TeV with the
ATLAS detector
The ATLAS Collaboration
Jet energy scale and resolution measurements with their associated uncertainties are reportedfor jets using 36–81 fb − of proton–proton collision data with a centre-of-mass energy of √ s =
13 TeV collected by the ATLAS detector at the LHC. Jets are reconstructed usingtwo 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 algorithmwith radius parameter R = . in situ techniques are employed to correct for differences between data and simulation and to measurethe resolution of jets. The systematic uncertainties in the jet energy scale for central jets( | η | < .
2) vary from 1% for a wide range of high- p T jets (250 < p T < p T (20 GeV) and 3.5% at very high p T ( > . ± . ± . © 2020 CERN for the benefit of the ATLAS Collaboration.Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license. a r X i v : . [ h e p - e x ] J u l ontents In situ jet calibrations 165.3 Systematic uncertainties 34 in situ jet energy resolution 476.4 Application of JER and its systematic uncertainties 50 Introduction
The energetic proton–proton ( pp ) collisions produced by the Large Hadron Collider (LHC) yield finalstates that are predominantly characterized by jets, or collimated sprays of charged and neutral hadrons.Jets constitute an essential piece of the physics programme carried out using the ATLAS detector due totheir presence in the signal processes being measured and searched for, the various background processesthat hide those signals, and the additional activity due to simultaneous pp collisions. Measurements of theenergy scale and resolution of these complex objects, as well as their associated systematic uncertainties,are therefore essential both for precision measurements of the Standard Model (SM) and for sensitivesearches for new physics beyond it. This paper presents the strategy used for the determination of the jetenergy scale (JES) and resolution (JER) by the ATLAS experiment and its implementation as it pertains tothe analysis of data from Run 2 of the LHC. Results for the JES and JER are presented using data collectedduring 2015–2017, corresponding to integrated luminosities in the range 36–81 fb − , depending on theanalysis method and its goals. This publication focuses on calibrating jets reconstructed with the anti- k t [1]algorithm with radius parameter R = . ofjets reconstructed with the anti- k t algorithm with R = . R = .
0, and a dedicated in situ calibrationof large-radius jets has also been completed in Run 2 data [9].Section 2 describes the ATLAS detector, and Section 3 describes the recorded data and the Monte Carlo(MC) simulation samples used in this paper. Section 4 presents the inputs and algorithms used to reconstructthe jets. Section 5 and Section 6 present the methods used and the result of both the calibration and theresulting systematic uncertainties of the JES and the JER, respectively.
The ATLAS detector [10] at the LHC covers nearly the entire solid angle around the collision point. Itconsists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic andhadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets.The inner-detector system (ID) is immersed in a 2 T axial magnetic field and provides charged-particletracking in the range | η | < . Comparisons in Run 1 between R = . R = . ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in the centre of the detector. Thepositive x -axis is defined by the direction from the interaction point to the centre of the LHC ring, with the positive y -axispointing upwards, while the beam direction defines the z -axis. Cylindrical coordinates ( r , φ ) are used in the transverse plane, φ being the azimuthal angle around the z -axis. The pseudorapidity η is defined in terms of the polar angle θ by η = − ln tan ( θ / ) .Rapidity is defined as y = 0 . [( E + p z )/( E − p z )] , where E denotes the energy and p z is the component of the momentumalong the beam direction. The angular distance ∆ R is defined as (cid:112) ( ∆ y ) + ( ∆ φ ) . | η | = .
0. The TRT also provides electron identification information based on thefraction of hits (typically 30 in total) above a higher energy-deposit threshold corresponding to transitionradiation.The calorimeter system covers the pseudorapidity range | η | < .
9. Within the region | η | < . | η | < .
8, to correct for energy loss inmaterials traversed by particles prior to reaching the calorimeters. Hadronic calorimetry is providedby the steel/scintillator-tile calorimeter, segmented into three barrel structures within | η | < .
7, andtwo copper/LAr hadronic endcap calorimeters. The solid angle coverage between 3 . < | η | < . | η | < . | η | < . The data used for the measurements presented here were collected in pp collisions at the LHC with acentre-of-mass energy of 13 TeV and a 25 ns proton bunch crossing interval during 2015–2017. Theintegrated luminosities of the datasets used are in the range 36–81 fb − after requiring that all detectorsubsystems were operational during data recording.Additional pp collisions in the same and nearby bunch crossings are referred to as pile-up . The numberof reconstructed primary vertices ( N PV ) and the mean number of interactions per bunch crossing ( µ ) areoptimal observables to quantify the level of pile-up activity. The average value of µ is 13.7, 24.9, and 37.8in the 2015, 2016, and 2017 datasets, respectively [14]. As described below, these conditions are accountedfor in the production and reconstruction of simulated data.Simulated dijet, multijet, Z +jet, and γ +jet samples are used in determining the jet energy scale and itsuncertainties. Table 1 summarizes the MC generators, adjustable sets of parameters (tunes), and partondistribution function (PDF) sets used for all nominal and alternative samples of the various simulatedprocesses. The nominal samples for the majority of analyses were generated with Pythia 8.186 [15] (fromnow on referred to as Pythia8) or Powheg+Pythia8.186 [15, 18, 19]. The multijet balance analysis usesSherpa 2.1.1 [20] as the nominal generator since it incorporates up to three jets in the matrix element andis thus more suitable for multijet processes that have more than two jets in the final state. The dijet, multijet,and γ +jet nominal samples use the NNPDF2.3LO PDF set [17] and the A14 set of tuned parameters [16].4 able 1: List of generators used for various processes. Information is given regarding the underlying-event tunes, thePDF parameter sets, and the perturbative QCD highest-order accuracy used in the matrix element. Abbreviationsin the PDF names and matrix element orders are LO (leading order), NLO (next-to-leading order), and NNLO(next-to-next-to-leading order).Process Generator Tune PDF set Matrix element+ fragmentation/hadronization orderDijet Pythia 8.186 [15] A14 [16] NNPDF2.3LO [17] LO& Powheg+Pythia 8.186 [15, 18, 19] A14 NNPDF2.3LO [17] NLOmultijet Sherpa 2.1.1 [20] Sherpa-default CT10 [21] LO (2 → → Z +jet Powheg+Pythia 8.186 [15, 18, 19] AZNLO [24] CT10 [21] Z+0j@NLOSherpa 2.2.1 [20] Sherpa-default NNPDF3.0NNLO [23] Z+0,1,2j@NLO γ +jet Pythia 8.186 [15] A14 [16] NNPDF2.3LO [17] LOSherpa 2.1.1 [20] Sherpa-default CT10 [21] LO For the Z +jet analysis, the dedicated AZNLO tune [24] is used instead. Alternative samples for definingsystematic variations use various generators and tunes.Stable particles, defined as those with c τ >
10 mm, output by the generators were passed through theGeant4-based simulation of the ATLAS detector [25, 26]. This step simulates the interactions of theparticles with matter in the detector and generates outputs which can be reconstructed in the same way asdata. Hadronic showers were simulated using the FTFP BERT model as described in Ref. [27]. A set ofsimulated dijet events using the less detailed Atlfast-II (AFII) are also studied to determine the differencein performance between full and fast simulation and provide appropriate calibrations for AFII samples inanalyses [25].Pile-up is incorporated in the MC samples by overlaying simulated inelastic interactions on the generatedhard-scatter interaction. The inelastic interactions were simulated in Pythia 8.210 using the A3 tune andthe NNPDF2.3LO PDF set [17, 28]. To determine the number of simulated pp collisions to overlay ontoa particular hard-scattering process, a random value is drawn from a Poisson distribution of the numberof pp collisions per bunch crossing with a mean given by the desired average number of collisions percrossing for a particular data period. Events simulated with a particular pile-up profile are then comparedwith data from the corresponding data period. One set of MC samples was created using the pile-up profileof 2015+2016 data (average number of collisions 23.7) while a second independent set of samples used theprofile of 2017 data. When data and simulation are compared in this paper, both sets of MC samples areused unless otherwise specified and are normalized to the luminosity of 2015+2016 data and 2017 dataseparately. The primary jet definition used in the majority of physics analyses by the ATLAS Collaboration and inthe studies presented here is the anti- k t [1] algorithm with a radius parameter R = . origin correction – is applied to every topo-cluster, based on its depth within the calorimeter andpseudorapidity. This method is to be contrasted with earlier approaches [7] that applied this correctiononly to the jet four-momentum rather than to its constituents.Jets reconstructed using only calorimeter-based energy information use the origin-corrected EM scaletopo-clusters and are referred to as EMtopo jets . This was the primary jet definition used in ATLAS physicsresults prior to the end of Run 2. EMtopo jets exhibit robust energy scale and resolution characteristicsacross a wide kinematic range, and are independent of other reconstruction algorithms such as tracking atthe jet-building stage.Hadronic final-state measurements can be improved by making more complete use of the information fromboth the tracking and calorimeter systems. The particle flow (
PFlow ) algorithm is based on Ref. [31] andupdated as described below. Particle flow directly combines measurements from both the tracker and thecalorimeter to form the input signals for jet reconstruction, which are intended to approximate individualparticles. Specifically, energy deposited in the calorimeter by charged particles is subtracted from theobserved topo-clusters and replaced by the momenta of tracks that are matched to those topo-clusters.These resulting
PFlow jets exhibit improved energy and angular resolution, reconstruction efficiency, andpile-up stability compared to calorimeter jets [31]. EMtopo and PFlow jets are retained for the analysesdiscussed in this paper only if they have an uncalibrated p T > | η | < . (cid:104) E dep (cid:105) , and its expected standard deviation, σ ( E dep ) , wererecomputed using the updated simulation, geometry, and topo-cluster noise thresholds for Run 2 [7]. Theshower profiles were similarly updated. The only algorithmic change was an improvement in the transitionbetween using track energy and cluster energy in high- p T jets. Since energetic particles are often in thecore of jets and thus poorly isolated from nearby activity, accurate removal of the calorimeter energyassociated with the track can be difficult. Therefore, the PFlow algorithm prevents energy subtraction inthese cases. Formerly this was managed by applying a simple p trkT <
40 GeV cut in the track selection. Inthe updated algorithm, a more sophisticated procedure is used to prevent the subtraction in cases where theadvantages of the tracker are smaller and where the particle shower falls in a region with significant energydepositions from other particles. For all tracks up to p trkT =
100 GeV, if the energy E clus in a cone of size ∆ R = .
15 around the extrapolated particle satisfies E clus − (cid:104) E dep (cid:105) σ ( E dep ) > . × log (
40 GeV / p trkT ) , (1)then the subtraction is not performed. With this parameterization, the subtraction is performed at lowertrack momenta unless the calorimeter activity measured by E clus is very high, such as in very denseenvironments where the accuracy of the subtraction is degraded. Since the calorimeter provides a goodenergy measurement at high p trkT , this parameterization effectively slowly truncates the algorithm, yet6llows the subtraction to continue to be performed for a small range above this cut-off even when thecalorimeter energy deposition is low or near the expected value, (cid:104) E dep (cid:105) . The momentum range up towhich the subtraction is still allowed to be performed is driven by the coefficient of 33.2 in Eq. (1) andis typically about 20–50% above the 40 GeV cut-off previously used. Above p trkT =
100 GeV no trackinformation is used and the PFlow algorithm becomes equivalent to EMtopo, benefitting from excellentcalorimeter performance at high energies. The result of the improved subtraction method detailed here isthat the energy resolution of PFlow jets becomes compatible with that of EMtopo jets at high energieswhile remaining superior at low energies.After the subtraction, two scalings are applied. These account for the difference in response, here defined asthe ratio of measured to true particle energy, between topo-clusters at the EM scale and tracks for which theenergy scale is closer to the true particle energy. The first scale factor applies only when no subtraction hasbeen performed for a selected track. In this case the PFlow object includes both the full topo-cluster energyand the track momentum. To avoid double-counting the energy while maintaining the contribution from thecalorimeter measurement, the track momentum is scaled by a factor ( − (cid:104) E dep (cid:105)/ p trk ) . The resulting PFlowobject uses the desired information and has a final energy of approximately p trk , matching the response forthe subtracted case. The second scale factor is applied in both the subtracted and non-subtracted casesfor all PFlow objects created from selected tracks below 100 GeV. It smooths the transition betweenthe lower-energy PFlow objects which are at the scale of the tracks and the higher-energy objects at theelectromagnetic scale of the clusters. The energy of these PFlow objects is scaled by unity for p trkT below30 GeV, by ( − (cid:104) E dep (cid:105)/ p trk ) for objects with 60 GeV < p trkT <
100 GeV, and by a linearly descendingscale factor in between. This ensures that all objects are at the electromagnetic scale by 60 GeV.Tracks used in PFlow objects and in deriving calibrations for both EMtopo and PFlow jets are reconstructedwithin the full acceptance of the inner detector ( | η | < . p T >
500 MeV, and satisfyquality criteria based on the number of hits in the ID subdetectors. To suppress the effects of pile-up, tracksmust also be associated with the primary vertex. Tracks are matched to jets using ghost association [33],a procedure that treats them as four-vectors of infinitesimal magnitude during the jet reconstruction andassigns them to the jet with which they are clustered.MC simulation is used to determine the energy scale and resolution of jets by comparing PFlow andEMtopo jets with particle-level truth jets . Truth jets are reconstructed using stable final-state particles andexclude muons, neutrinos, and particles from pile-up interactions. Truth jets are selected with the same p T > | η | < . ∆ R with the requirement ∆ R < . The jet energy scale calibration restores the jet energy to that of jets reconstructed at the particle level. Thefull chain of corrections is illustrated in Figure 1. All stages correct the four-momentum, scaling the jet p T ,energy, and mass.At the beginning of the chain, the pile-up corrections remove the excess energy due to additional proton–proton interactions within the same (in-time) or nearby (out-of-time) bunch crossings. These correctionsconsist of two components: a correction based on the jet area and transverse momentum density of theevent, and a residual correction derived from MC simulation and parameterized as a function of the meannumber of interactions per bunch crossing ( µ ) and the number of reconstructed primary vertices in the7vent ( N PV ). These corrections are discussed in Section 5.1.1. The absolute JES calibration correctsthe jet so that it agrees in energy and direction with truth jets from dijet MC events, and is detailed inSection 5.1.2. Furthermore, the global sequential calibration (derived from dijet MC events) improves thejet p T resolution and associated uncertainties by removing the dependence of the reconstructed jet responseon observables constructed using information from the tracking, calorimeter, and muon chamber detectorsystems, as introduced in Section 5.1.3. Finally, a residual in situ calibration is applied to correct forremaining differences between data and MC simulation. It is derived using well-measured reference objects,including photons, Z bosons, and calibrated jets, and for the first time benefits from a low- p T measurementusing the missing- E T projection fraction method for better pile-up robustness. It is described in Section 5.2.The full treatment and reduction of the systematic uncertainties is discussed in Section 5.3. The derivation of the calibrations derived exclusively from MC simulation samples is described below.
As a result of the increase of the topo-clustering p T thresholds (to suppress electronic and pile-up noise)and in the instantaneous luminosity, the contribution from pile-up to the JES in the 2015–2017 data-takingperiod differs from the one observed in 2015. The pile-up corrections are therefore evaluated using updatedMC simulations of the software reconstruction and pile-up conditions. These corrections are derived usingthe same methods employed in 2015 [7] and are summarized in the following paragraphs.First, a jet p T -density-based subtraction of the per-event pile-up contribution to the jet p T is performed.The jet area A is a measure of the susceptibility of the jet to pile-up and is calculated by determining therelative number of ghost particles associated with a jet after clustering. Next, the pile-up contribution isestimated from the median p T density, ρ , of jets in the y – φ plane, (cid:104) p T / A (cid:105) . The calculation of ρ uses jetsreconstructed using the k t algorithm [34] with radius parameter R = . Applied as a function ofevent pile-up p T densityand jet area. Removes residual pile-updependence, as a function of μ and N PV . Reconstructedjets
Jet fi nding applied to tracking- and/or calorimeter-based inputs. Corrects jet 4-momentumto the particle-level energyscale. Both the energy anddirection are calibrated.Reduces fl avour dependenceand energy leakage e ff ectsusing calorimeter, track, andmuon-segment variables. A residual calibrationis applied only to data to correct for data/MCdi ff erences. p T -density-basedpile-up correction Residual pile-upcorrection Absolute MC-basedcalibrationGlobal sequentialcalibration Residual in situ calibration Figure 1: Stages of jet energy scale calibrations. Each one is applied to the four-momentum of the jet. | η | <
2. The computation of ρ in the central region of the detector gives a more meaningful measureof the pile-up activity than the median over the entire η range, and this is because ρ drops to nearly zerobeyond | η | ∼
2. This drop is due to the lower occupancy in the forward region relative to the central region,which is a result of a coarser segmentation in the forward region. The k t algorithm is chosen due to itstendency to naturally reconstruct jets including an uniform soft background [33], while ρ is used to reducethe bias from hard-scatter jets which populate the high- p T tails of the distribution. The distribution of ρ inMC simulation for representative N PV values is shown in Figure 2. The ratio of the ρ -subtracted jet p T tothe uncorrected jet p T is applied as a scale factor to the jet four-momentum and does hence not affect itsdirection. r N o r m a li z ed en t r i e s Simulation
ATLAS = 13 TeV, Pythia8 Dijets |<2.0 h PFlow-scale topo-clusters | < 38 m
37 < = 15 PV N = 25 PV N = 35 PV N Figure 2: Per-event median p T density, ρ , at N PV =
15 (solid), N PV =
25 (long dashed), and N PV =
35 (short dashed)for 37 < µ <
38 as found in MC simulation.
The ρ calculation is derived from the central, lower-occupancy regions of the calorimeter and does notfully describe the pile-up sensitivity in the forward calorimeter region or in the higher-occupancy core ofhigh- p T jets. It is therefore observed that after this correction some dependence of the anti- k t jet p T on thepile-up activity remains, and consequently, a residual correction is derived. This residual dependence isdefined as the difference between the reconstructed jet p T and truth jet p T and it is observed as a functionof both N PV and µ , which are sensitive to in-time and out-of-time pile-up respectively.The jet p T after all pile-up ( p T -density-based and residual) corrections is given by p corrT = p recoT − ρ × A − α × ( N PV − ) − β × µ , where p recoT refers to the p T of the reconstructed jet before any pile-up correction is applied. Reconstructedjets with p T > ∆ R = .
3. The residual p T dependenceson N PV ( α ) and µ ( β ) are observed to be fairly linear and independent of one another. Independent linearfits are used to derive α and β coefficients in bins of p trueT and | η det | , where p trueT is the p T of the truth jetthat matches the reconstructed jet. The jet η pointing from the geometric centre of the detector, η det , isused to remove any ambiguity as to which region of the detector is measuring the jet. Both the α and β coefficients are seen to have a logarithmic dependence on p trueT , and logarithmic fits are performed inthe range 20 GeV < p trueT <
200 GeV for each bin of | η det | . In each | η det | bin, the fitted values of the α and β coefficients at p trueT =
25 GeV are taken as their nominal values, reflecting their behaviour in the p T region where pile-up effects are most relevant. The differences between the logarithmic fits over the full p trueT range and the nominal fits are used for a p T -dependent systematic uncertainty in the residual pile-up9ependence. Finally, linear fits are performed to the binned coefficients as a function of | η det | . This reducesthe effects of statistical fluctuations and allows the α and β coefficients to be smoothly sampled in | η det | ,particularly in regions of varying dependence.The dependences of the p T -density-based and residual corrections on N PV and µ as a function of | η det | for PFlow jets are shown in Figure 3. The negative dependence on µ for out-of-time pile-up is a result ofthe liquid-argon calorimeter’s pulse shape, which is negative during the period shortly after registering asignal [35]. These corrections are similar to those derived for EMtopo jets, although the N PV -dependentcorrections for PFlow jets in the | η det | < . | η det | > . det h |0.8 - - - - [ G e V ] PV N ¶ / T p ¶ Before any correctionAfter area-based correctionAfter residual corrections
Simulation
ATLAS = 13 TeV, Pythia8 dijets = 0.4 (PFlow) R t Anti-k (a) In-time pile-up dependence det h |0.8 - - - - [ G e V ] m¶ / T p ¶ Before any correctionAfter area-based correctionAfter residual corrections
Simulation
ATLAS = 13 TeV, Pythia8 dijets = 0.4 (PFlow) R t Anti-k (b) Out-of-time pile-up dependenceFigure 3: Dependence of PFlow jet p T on (a) in-time pile-up ( N PV averaged over µ ) and (b) out-of-time pile-up ( µ averaged over N PV ) as a function of | η det | for p trueT =
25 GeV. Errors are taken from the fit results and are too small tobe visible on the scale of the plot.
Four systematic uncertainties are introduced to account for MC mis-modelling of N PV , µ , the ρ topology,and the p T dependence of the residual pile-up corrections. The last of these is derived from the fulllogarithmic fits to α and β , as discussed previously. Two in situ methods are used to estimate uncertaintiesin the modelling of N PV and µ . The first method uses jets reconstructed from tracks to provide a measureof the jet p T independent of pile-up. This is only used for | η | < p T balance between a reconstructed jet and a Z boson and is used for 2 . < | η | < .
5. These systematicuncertainties are described in more detail in Ref. [36]. Finally, the ρ topology uncertainty accounts for theuncertainty in the underlying event’s contribution to ρ , and is discussed in detail in Section 5.2.4. η calibration The absolute jet energy scale and η calibrations correct the reconstructed jet four-momentum to theparticle-level energy scale accounting for non-compensating calorimeter response, energy losses in deadmaterial, out-of-cone effects and biases in the jet η reconstruction. Such biases are primarily caused bythe transition between different calorimeter technologies and sudden changes in calorimeter granularity.10he calibration is derived for R = . k t jets from a Pythia8 MC simulation of dijet events after theapplication of the pile-up corrections. Reconstructed jets are geometrically matched to truth jets within ∆ R = .
3. In addition, reconstructed (truth) jets are required to have no other reconstructed (truth) jet of p T > ∆ R = . ∆ R = . R , defined as the mean of a Gaussian fit to the core of the E reco / E true distribution, is measured in E true and η det bins. The decision to calculate the response as a function of E true instead of E reco is motivated by the fact that for fixed E true ( E reco ) bins the response distribution is(not) Gaussian. The average response is parameterized as a function of E reco using a numerical inversionprocedure, as detailed in Ref. [2], and the jet calibration factor is taken as the inverse of the averageenergy response. The response is higher for PFlow jets than for EMtopo jets at low energies since trackinginformation is used. The response for PFlow jets as a function of E reco ( η det ) for representative η det ( E reco )bins is shown in Figure 4. Good closure (response equal to 1) is seen across the entire η det range after theJES calibration is applied, improving on the 2015 calibration thanks to advances in the fitting method andparameters. As in that calibration, a small non-closure of the order of a few percent is seen for low- p T jetsdue to a slightly non-Gaussian energy response and jet reconstruction threshold effects, both of whichimpact the response fits. - - - - det h J e t ene r g y r e s pon s e = 30 GeV reco E = 110 GeV reco E = 1200 GeV reco E ATLAS
Simulation = 13 TeV, Pythia8 dijet s = 0.4 (PFlow) R t k Anti- (a) · ·
2 [GeV] reco E J e t ene r g y r e s pon s e = 0 det h = 1 det h = 1.4 det h = 2.5 det h = 4 det h ATLAS
Simulation = 13 TeV, Pythia8 dijet s = 0.4 (PFlow) R t k Anti- (b)Figure 4: The average energy response as a function of reconstructed jet (a) η det and (b) energy E reco . Each valueis obtained from the corresponding parameterized function derived with the Pythia8 MC sample and only jetssatisfying p T >
20 GeV are shown.
A bias in the reconstructed jet η , defined as a significant deviation from zero in the signed differencebetween the reconstructed and truth jet η , denoted by η reco and η true respectively, is observed and shownin Figure 5 as a function of | η det | for PFlow jets. The bias for EMtopo jets is similar, showing the samefeatures. It is largest in jets that encompass two calorimeter regions with different energy responses causedby changes in calorimeter geometry or technology. This artificially increases the energy of one side of thejet relative to the other, altering the reconstructed four-momentum. The barrel–endcap ( | η det | ∼ .
4) andendcap–forward ( | η det | ∼ .
1) transition regions can be clearly seen in Figure 4(a) as susceptible to thiseffect. A second correction is therefore derived as the difference between the reconstructed and truth η ( η reco and η true respectively) parameterized as a function of E true and η det to remove such bias. A numericalinversion procedure is again used to derive corrections in E reco from E true . This calibration only alters thejet p T and η , not the full four-momentum. EMtopo and PFlow jets calibrated with the full jet energy scaleand η calibration are considered to be at the EM+JES scale and PFlow+JES scale, respectively.11 det h |0.04 - - ) t r ue h - r e c o h ( · ) r e c o h s gn ( = 30 GeV reco E = 60 GeV reco E = 110 GeV reco E = 400 GeV reco E = 1200 GeV reco E ATLAS
Simulation = 13 TeV, Pythia8 dijet s = 0.4 (PFlow) R t k Anti-
Figure 5: The signed difference between the reconstructed and truth jet η , denoted by η reco and η true respectively.Each value is obtained from the corresponding parameterized function derived with the Pythia8 MC sample andonly jets satisfying p T >
20 GeV are shown.
The absolute JES and η calibrations are also derived for a Pythia8 MC sample using AFII. An additionalsystematic uncertainty is considered for these samples to account for a small non-closure in the calibrationbeyond | η det | ∼ .
2, due to the approximate treatment of hadronic showers in the forward calorimeters.This uncertainty is below 0.5% for all central jets and is about 3% for a forward jet of p T =
20 GeV, fallingrapidly with increasing p T . Even after the application of the previous jet calibrations (from now on referred to as MCJES), for a given( p trueT , η det ) bin, the response can vary from jet to jet depending on the flavour and energy distributionof the constituent particles, their transverse distribution, and the fluctuations of the jet development inthe calorimeter. Furthermore, the average particle composition and shower shape of a jet varies betweeninitiating particles, most notably between quark- and gluon-initiated jets. A quark-initiated jet will ofteninclude hadrons with a higher fraction of the jet p T that penetrate further into the calorimeter, whilea gluon-initiated jet will typically contain more particles of softer p T , leading to a lower calorimeterresponse and a wider transverse profile. The global sequential calibration (GSC), a procedure used inthe 2012 [6] and 2015 [7] calibrations, is a series of multiplicative corrections to reduce the effects fromthese fluctuations and improve the jet resolution without changing the average jet energy response. The jetresolution σ R is given by the standard deviation of a Gaussian fit to the jet p T response distribution, wherethe p T response is defined similarly to jet energy response as the ratio of p recoT to p trueT .The GSC is based on global jet observables such as the longitudinal structure of the energy depositionswithin the calorimeters, tracking information associated with the jet, and information related to the activityin the muon chambers behind a jet. For these studies, reconstructed jets are geometrically matched totruth jets and a numerical inversion procedure is used, as explained in Section 5.1.2. Six observables areidentified that improve the resolution of the JES through the GSC. For each observable, an independent jetfour-momentum correction is derived as a function of p trueT and | η det | by inverting the reconstructed jetresponse in Pythia8 MC simulation events. Corrections for each observable are applied independently and12equentially to the jet four-momentum for jets with | η | < . f charged , the fraction of the jet p T measured from ghost-associated tracks with p T >
500 MeV( | η det | < . f Tile0 , the fraction of jet energy measured in the first layer of the hadronic Tile calorimeter( | η det | < . f LAr3 , the fraction of jet energy measured in the third layer of the electromagnetic LAr calorimeter( | η det | < . n trk , the number of tracks with p T > | η det | < . w trk , also known as track width, the average p T -weighted transverse distance in the η – φ plane betweenthe jet axis and all tracks of p T > | η det | < . n segments , the number of muon track segments ghost-associated with the jet ( | η det | < . n segments correction, also known as the punch-throughcorrection, reduces the tails of the response distribution caused by high- p T jets that are not fully contained inthe calorimeter. All corrections are derived as a function of jet p T , except for the punch-through correction,which is derived as a function of jet energy since this effect is more correlated with the energy escaping thecalorimeters.The underlying distributions of these observables are shown for PFlow jets in MC simulation and binsof equal statistics in Figure 6. Each observable has been studied in data and simulation and is foundto be well modelled [31]. The spike at zero in the f Tile0 distribution at low p trueT , shown in Figure 6(b),corresponds to jets that are fully contained in the electromagnetic calorimeter and do not deposit energyin the Tile calorimeter. The tail towards negative values in the f Tile0 and f LAr3 distributions at low p trueT ,shown in Figures 6(b) and 6(c), respectively, reflects calorimeter noise fluctuations. Slight differences withrespect to data have a negligible impact on the GSC since the dependence of the average jet response onthe observables is well modelled in MC simulation, as observed by an in situ dijet tag-and-probe methoddescribed in Ref. [2]. In this method, the average p T asymmetry between back-to-back jets is measured asa function of each observable.The average jet p T response for PFlow jets in MC simulation as a function of each of the GSC observablesis shown in Figure 6 for representative p trueT ranges. The dependence of the jet response on each observableis reduced to less than 2% after the full GSC is applied, with small deviations from unity reflecting thecorrelations between observables that are unaccounted for in the corrections.The fractional jet resolution, defined as σ R /R , is used to determine the size of the fluctuations in the jetenergy reconstruction and is shown for PFlow jets with 0 . < | η det | < . charged Charged fraction, f R e s pon s e T p J e t charged Charged fraction, f N o r m a li z ed en t r i e s < 25 GeV trueT p
20 < < 100 GeV trueT p
80 < < 250 GeV trueT p
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Simulation = 13 TeV, Pythia8 dijets = 0.4 (PFlow+JES) R t k Anti- | < 0.3 det h (d) n trk trk Track width, w R e s pon s e T p J e t trk Track width, w N o r m a li z ed en t r i e s < 25 GeV trueT p
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30 40 50 60 70 80 90 segments Number of muon segments, n R e s pon s e T p J e t
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Simulation = 13 TeV, Pythia8 dijets = 0.4 (PFlow+JES) R t k Anti-| < 1.3 det h | (f) n segments Figure 6: Jet response for PFlow jets in four broad p trueT ranges as a function of each of the six observables used in theGSC (a) the fraction of the jet p T carried by charged particles, (b) the fraction of energy in the first layer of the Tilecalorimeter, (c) the fraction of energy in the third layer of the electromagnetic calorimeter, (d) the number of tracks,(e) the track width, and (f) the number of muon spectrometer track segments associated with the jet. Jets at thePFlow+JES scale with 0 . < | η det | < . n segments which is shown for | η det | < . [GeV] truthT p / R ) s R e s o l u t i on ( T p J e t MCJES charged + f
Tile0 + f
LAr3 + f trk + n trk + w segments + n
ATLAS
Simulation = 13 TeV, Pythia8 dijets = 0.4 (PFlow+JES) R t k Anti- | < 0.3 det h · ·
2 [GeV] truthT p - w . r .t. M C J ES s - ' s ) s ' - s s gn ( Figure 7: Resolution of jets at the PFlow+JES scale with 0 . < | η det | < . b -jets, are considered. Thelower panel shows the difference in quadrature between the resolution before any GSC correction is applied ( σ ) andafter the corresponding GSC step is applied ( σ (cid:48) ). .2 In situ jet calibrations
Once jets are corrected to the particle level using the MCJES and GSC, they require one final calibrationstep to account for differences between the jet response in data and simulation. These differences arecaused by imperfect simulation of both the detector materials and the physics processes involved: the hardscatter and underlying event, jet formation, pile-up, and particle interactions with the detector. The final in situ calibration measures the jet response in data and MC simulation separately and uses the ratio as anadditional correction in data.Jet response is calculated by balancing the p T of a jet against that of a well-calibrated reference object orsystem. The response R in situ is defined as the average ratio of the jet p T to the reference object p T in bins ofreference object p T , where that average is taken from the peak location found by fitting the distribution witha Gaussian function. R in situ is sensitive to effects such as the presence of additional radiative jets or thetransition of energy into or out of the jet cone, although these effects can be mitigated through careful eventselection. A better method is to form the double ratio from the response in data and MC simulation: c = R data in situ R MC in situ , which is robust to secondary effects so long as they are well modelled in simulation and is therefore areliable measure of the jet energy scale difference between data and MC simulation. The double ratio c istransformed via numerical inversion from a function of reference object p T to a function of jet p T (and jet η where applicable). This is the final in situ calibration.There are three stages of in situ analyses. First, the η intercalibration analysis corrects the energy scaleof forward (0 . ≤ | η | < .
5) jets to match those of central ( | η | < .
8) jets using the p T balance in dijetevents. Second, the Z +jet and γ +jet analyses balance the hadronic recoil in an event against the p T of acalibrated Z boson or photon. The missing- E T projection fraction (MPF) method uses the full hadronicrecoil instead of a jet to compute the balance to help mitigate effects of pile-up and jet reconstructionthreshold which otherwise make low- p T measurements challenging [39]. Finally, the multijet balance(MJB) analysis uses a system of well-calibrated low- p T jets to calibrate a single high- p T jet [40]. The Z / γ +jet and MJB analyses are computed only for central jets, but are also applicable to forward jets due tothe effect of the η intercalibration. Each measurement is translated from a function of reference object p T into jet p T . A statistical combination of the Z / γ +jet and MJB analyses provides a single smooth calibrationapplicable across the full momentum range.Since the three in situ analyses ( η intercalibration, Z / γ +jet MPF, and MJB) are performed sequentially,systematic uncertainties are propagated from each to the next. Within each analysis, systematic uncertaintiesarise from three sources: modelling of physics processes in simulation, uncertainties in the measurement ofthe reference object, and uncertainties in the expected p T balance due to the event’s topology. Mis-modellingis accounted for by comparing the predictions of two MC generators and taking their difference as theuncertainty. Systematic uncertainties in the measurement of the reference object are taken from the ± σ uncertainties in each object’s calibration and propagated through the analysis. Event topology uncertaintiesare estimated by varying the event selections used and observing the impact on the final MC simulation todata ratio. Requirements on the angle between the leading jet and the well-calibrated reference object, as well as cuts on the maximum p T of the second jet in the event, help suppress additional radiation to ensure the events are as clean as possible.
16 rebinning procedure is applied to each systematic uncertainty to ensure that the features represented inthe final result are statistically significant and not the result of fluctuations in small numbers of simulated ordata events. This is only performed where the response does not vary sharply with p T , ensuring it does notobscure real physics effects. The rebinning procedure follows a bootstrapping method: pseudo-experimentdatasets are created by sampling from a Poisson distribution with a mean of one for each event in the dataor MC simulation [41]. The pseudo-experiments are therefore statistically correlated yet unique, and theroot mean square of the response distribution across the pseudo-experiments provides a measure of thestatistical uncertainty of the analysis. The measured result for each systematic uncertainty is then rebinnedas appropriate for each analysis to ensure that the final shapes are statistically significant.The Z / γ +jet and MJB calibrations and uncertainties are derived from the full 2015–2017 combined datasetswith a total luminosity of 80 fb − . The η intercalibration analysis uses a dataset of total size 81 fb − , butsince this analysis is more sensitive than the others to year-by-year fluctuations, the dataset is split intotwo blocks and a time-dependent result is computed instead. One η intercalibration is derived from andapplies to the 2015 + + + η intercalibration in all years, with only a small localized additional uncertaintyadded for 2018 as described in Section 5.2.1.Certain common selection criteria are applied to all three in situ analyses. Each event must have areconstructed vertex with at least two associated tracks of p T >
500 MeV. All jets must satisfy qualitycriteria to reject non-collision background, calorimeter noise, and cosmic rays [42]. Furthermore, eachjet with 20 GeV < p T <
60 GeV and | η | < . η using dijet events The η intercalibration analysis produces a correction which is applied to forward jets (0 . ≤ | η det | < .
5) tobring them to the same energy scale as central jets ( | η det | < . η regions of the detector. To maximizestatistics, neither jet need be in the central region: instead, all regions will be calibrated relative to oneanother.For these dijet events, momentum balance requires that the transverse momentum of the two jets mustbe equal and opposite. Therefore, the momentum asymmetry of the two jets is a metric for the responsedifference between the two detector regions ( left and right for simplicity): A = p leftT − p rightT p avgT , p avgT = ( p leftT + p rightT )/
2. The response ratio R of the two jets defines the calibration factor c for eachjet and is then: R = c left c right = + (cid:104)A(cid:105) − (cid:104)A(cid:105) (cid:27) p leftT p rightT . The average response ratio (cid:104)R ijx (cid:105) is measured in each bin i of η left , j of η right , and x of p avgT ; ∆ (cid:104)R ijx (cid:105) isthe statistical uncertainty in each bin. All η values are in detector coordinates rather than corrected jetcoordinates ( η det ) since the properties of interest correlate to specific regions of detector hardware. Thefollowing function relates the correction factors and responses in each of the N bins: S ( c x , . . . , c N x ) = N (cid:213) j = j − (cid:213) i = (cid:18) ∆ (cid:104)R ijx (cid:105) ( c ix (cid:104)R ijx (cid:105) − c jx ) (cid:19) + X ( c ix ) . Here, the function X ( c ix ) quadratically imposes a penalty on correction factors deviating from 1. Minimizing this function produces the correction factors to be used in the calibration.Previous iterations of the jet energy scale have used a fit in Minuit to minimize S ( c ix ) . The currentcalibration instead minimizes the function analytically. Suppressing the x indices for clarity and setting thederivative of S with respect to some correction factor c α equal to zero, the following equation defines thecorrection factor values which minimise S: α − (cid:213) i = (cid:18) (cid:16) −(cid:104)R i α (cid:105) ∆ (cid:104)R i α (cid:105) + λ N (cid:17) c i (cid:19) + (cid:32) α − (cid:213) i = ∆ (cid:104)R i α (cid:105) + N (cid:213) i = α + (cid:104)R α i (cid:105) ∆ (cid:104)R α i (cid:105) + λ N (cid:33) c α + N (cid:213) i = α + (cid:18) (cid:16) −(cid:104)R α i (cid:105) ∆ (cid:104)R α i (cid:105) + λ N (cid:17) c i (cid:19) − λ N = . (2)Here λ is a Lagrange multiplier arising from the penalty term whose value has no effect on the minimizationresult but prevents the trivial solution where all the c i are null.Equation (2) can then be expressed as a matrix system of linear equations. This matrix system is solvedindependently for each p avgT bin x to obtain values for the correction factors c ix for each η det bin i in thismomentum range. Solving analytically for the c ix in this way allows the result to be found approximately athousand times more quickly than using a fit. This large reduction in computational requirements in turnallows the analysis to use a finer binning in η det , capturing more details of the detector structure. Thetwo methods agree well and each shows good closure when tested in simulation. Finally, the full set ofcorrection factors are normalized such that the average correction factor in the central region | η det | < . p T range for which it is at least 99% efficient [13, 44]. Events may pass either a central jet trigger or aforward jet trigger, or both. In the case that a trigger is prescaled, the passing event is weighted by theappropriate amount. Jets with | η | < . This penalty function takes the form X ( c i ) = λ (cid:16) N (cid:205) Ni = c i − (cid:17) , where λ introduces the Lagrange multiplier visible in Eq. (2).The purpose of the penalty function is to ensure that the appropriate minimum is selected by suppressing local minima withlarge values of c ix , and as such its exact form is somewhat arbitrary. p T >
25 GeV and | η | < .
5. To ensure a clean dijet topology, events are further required to have nothird jet with significant p T : p thirdT / p avgT < .
25, where p avgT is the average momentum of the two leadingjets. The two leading jets are required to be back-to-back in the azimuthal plane such that ∆ φ > . in situ analyses, the goal of the η intercalibration is to correct for data–simulation differences,so the quantity of interest is the ratio of the measured calorimeter response in MC simulation to the responsein data. The nominal calibration is derived by comparison with Powheg+Pythia8 simulated events. Theanalysis binning in p avgT and η det is selected to balance the requirements of both sufficient statistics in sparseregions and resolution of narrow detector features. As such, it varies for different values of η det . Remainingstatistical fluctuations in the final calibration are smoothed using a two-dimensional Gaussian kernel withparameters selected to preserve significant structures.Figure 8 shows the measured response in data and Powheg+Pythia8 MC simulation for the 2017 datasetas a function of η det for three different p avgT ranges (Figures 8(a), 8(b), 8(c)) and as a function of p avgT forthree different η det ranges (Figures 8(d), 8(e), 8(f)). The simulation can be seen to approximately reproducethe η det -dependent features of the response observed in data, although the response in data is consistentlyhigher than the response in simulation. The simulation/data response ratio as directly measured is shownin discrete points in the bottom panel, while the calibration derived from smoothing the response ratio isoverlaid as the solid curve. The dashed curve shows the extrapolation to p T ranges beyond the availabledata, taken from the Gaussian smoothing results. Since the smoothing is stronger in the p T direction andweaker in η det to preserve detector features, this sets each extrapolated value to approximately the value ofthe last populated bin at lower p T . Above p T = η det and p T and account for mis-modelling of physics, detector,and event topology effects on the momentum balance of dijet events. The dominant uncertainty is in MCmis-modelling and is taken to be the difference between the smoothed calibration curves derived fromthe Powheg+Pythia8 and Sherpa dijet samples. Additional uncertainties in the physics and topologymodelling are assessed by varying the p thirdT , ∆ φ , and pile-up suppression cuts and using a bootstrappingmethod to ensure observed shapes are statistically significant as discussed in Section 5.2. Similarly, theJVT uncertainty is determined by comparison with tighter and looser working points. These uncertaintiescan take positive or negative values. The statistical uncertainty is strictly positive and is taken from the dataand MC simulation sample sizes. Finally, a non-closure uncertainty is assessed by comparing the responsein data with that in Powheg+Pythia8 after applying the derived η intercalibration. This uncertainty islargest for | η det | ∼ . .
6, where detector transitions make modelling of the LAr pulse shape particularlydifficult, and for jets near the kinematic limit, where they have the maximum possible p T for a given η subject to the constraint of a 13 TeV centre-of-mass energy. The non-closure uncertainty is treated as threeindependent nuisance parameters, two covering the regions around ± . η and one at the kinematiclimit, since these two non-closure uncertainties are uncorrelated.After being corrected each with their dedicated calibration, the 2015+2016 and 2017 datasets are in goodagreement, and therefore a single set of uncertainties is sufficient to cover both cases. The uncertaintiescalculated with the 2015+2016 dataset are selected for this role. The only dataset-dependent uncertainty isan additional small non-closure uncertainty used for 2018 data only. It covers the region around η = ± . p T values are selected. The uncertaintiesdecrease slightly as a function of p T and increase significantly as a function of η outside of the centraldetector region, while in the central region they are zero by construction. For practical use the various19 .911.11.21.3 R e l . j e t r e s pon s e ( / c ) ATLAS = 0.4 (PFlow+JES) R t k Anti- < 60 GeV avgT p £ -1 = 13 TeV, 44 fbs Data 2017Simulation - - - - det h M C / da t a (a) R e l . j e t r e s pon s e ( / c ) ATLAS = 0.4 (PFlow+JES) R t k Anti- < 115 GeV avgT p £ -1 = 13 TeV, 44 fbs Data 2017Simulation - - - - det h M C / da t a (b) R e l . j e t r e s pon s e ( / c ) ATLAS = 0.4 (PFlow+JES) R t k Anti- < 330 GeV avgT p £ -1 = 13 TeV, 44 fbs Data 2017Simulation - - - - det h M C / da t a (c) R e l . j e t r e s pon s e ( / c ) ATLAS = 0.4 (PFlow+JES) R t k Anti- < 1.4 det h £ -1 = 13 TeV, 44 fbs Data 2017Simulation
30 40 · · T jet p0.91 M C / da t a (d) R e l . j e t r e s pon s e ( / c ) ATLAS = 0.4 (PFlow+JES) R t k Anti- < 2.8 det h £ -1 = 13 TeV, 44 fbs Data 2017Simulation
30 40 · · T jet p0.91 M C / da t a (e) R e l . j e t r e s pon s e ( / c ) ATLAS = 0.4 (PFlow+JES) R t k Anti- < 3.2 det h £ -1 = 13 TeV, 44 fbs Data 2017Simulation
30 40 · · T jet p0.91 M C / da t a (f)Figure 8: Relative response of jets calibrated with PFlow+JES in data (black circles) and Powheg+Pythia8 MCsimulation (red squares). Response is shown as a function of η det for jets of (a) 40 GeV < p jetT <
60 GeV, (b)85 GeV < p jetT <
115 GeV, and (c) 270 GeV < p jetT <
330 GeV, and as a function of p T for jets of (d) 1 . < η det < . . < η det < .
8, and (f) 3 . < η det < .
2. The lower panel shows the response ratio of simulation to data (redsquares) as well as the smoothed in situ calibration factor derived from the ratio (solid curve) which is used to performthe η intercalibration. Dotted lines show the extrapolation of the in situ calibration to the regions without data points.The dashed red and blue horizontal lines provide reference points for the viewer. η . The non-closure uncertainties, not included in Figure 9 as they are not methoduncertainties, are instead shown in Figure 20 where it can be seen that they are kept asymmetric to reflectreal differences in the detector. - - - - det h - F r a c t i ona l U n c e r t a i n t y Total Systematics Statistics MC modelling down FD up FD JVT down
Tjet3 p up
Tjet3 p ATLAS -1 = 13 TeV, 37 fbs = 0.4 (PFlow+JES) R t k Anti- = 50 GeV T p (a) - - - - det h - F r a c t i ona l U n c e r t a i n t y Total Systematics Statistics MC modelling down FD up FD JVT down
Tjet3 p up
Tjet3 p ATLAS -1 = 13 TeV, 37 fbs = 0.4 (PFlow+JES) R t k Anti- = 100 GeV T p (b) - - - det h - F r a c t i ona l U n c e r t a i n t y Total Systematics Statistics MC modelling down FD up FD JVT down
Tjet3 p up
Tjet3 p ATLAS -1 = 13 TeV, 37 fbs = 0.4 (PFlow+JES) R t k Anti- = 300 GeV T p (c)Figure 9: Systematic uncertainties associated with the η intercalibration procedure as a function of η det for PFlow+JESjets of (a) p T =
50 GeV, (b) p T =
100 GeV, and (c) p T =
300 GeV. The solid purple band shows the total systematicuncertainty, while the grey band shows the statistical uncertainty alone. Individual sources of uncertainty are markedby coloured lines. These have been smoothed to remove the impact of statistical fluctuations. Thus the visible shapesare statistically significant. The MC modelling term is the dominant source of uncertainty.
The calibrations are similar in size and shape between PFlow and EMtopo jets. Systematic uncertainties arealso similar in size and shape since the dominant MC modelling component does not differ meaningfullybetween the two jet collections. Z +jet and γ +jet events The next stage of the in situ calibration corrects for the differences between data and MC simulation usingthe momentum balance between the measured hadronic activity in the event and the p T of a well-calibrated21hoton or Z boson. Only the central region of the detector ( | η | < .
8) is used for this analysis: the η intercalibration ensures that a correction derived centrally translates directly to forward jets as well.The Z / γ +jet analyses rely on the energy scale of the photon or the electrons and muons from the Z decaybeing well measured. All three objects are cleanly measured in the ATLAS detector and the uncertainties intheir energy scales are small [45, 46]. The response is calculated separately in Z → e + e − and Z → µ + µ − events since the sources of uncertainties propagated from e and µ calibration are independent, and the threechannels are combined at a later stage. The Z +jet response measurement is limited at moderate to high p T by low statistics and thus covers a range in jet p T from 17 GeV to 1 TeV with large uncertainties in the finalbin. The γ +jet response measurement benefits from much higher statistics and extends to 1.2 TeV withlittle loss in sensitivity. However, it is limited at low jet p T by both the trigger prescales and the prevalenceof soft jets misidentified as photons and so begins at 25 GeV.The missing- E T projection fraction technique is used for both of the Z / γ +jet analyses and balances thereference object p T against the full hadronic recoil in an event. By doing so, it is possible to computethe calorimeter response to hadronic showers directly. This approach is robust to both pile-up and theunderlying event, which each cancel out directionally on average over a large collection of events, and isnot strongly affected by jet definitions since these become relevant only in the application of the calibration.The showering and topology effects in moving from a recoil-level quantity to a jet-level quantity arestudied and found to be small, as discussed below. Taking (cid:174) p recoilT as the total transverse momentum of thehadronic activity in a clean Z / γ +jet event and p refT as the transverse momentum of the photon or Z boson,conservation of transverse momentum means that at the particle level: (cid:174) p refT,truth + (cid:174) p recoilT,truth = . (3)This balance could be altered by the presence of initial- or final-state radiation. To suppress the effects ofsuch additional radiation, a cut is placed on the azimuthal angle ∆ φ between the jet and the reconstructedphoton or Z boson in the event and an uncertainty due to the topology is evaluated by varying the eventselection requirements. If the calorimeter response to the hadronic activity in this event is r MPF and theresponse for the calibrated reference object is 1, and assuming any missing energy in the event is due to thelow response to the hadronic recoil ( r MPF < (cid:174) p refT + r MPF (cid:174) p recoilT = − (cid:174) E missT After taking the projection of each term in the direction of the reference object, defined by a unit vectorˆ n ref , the response to the hadronic recoil is then seen to depend only on the missing energy in the event andthe momentum of the reference object. The MPF response R MPF is defined by measuring the average of r MPF across events binned in the reference object p T . Thus, R MPF = (cid:42) + ˆ n ref · (cid:174) E missT p refT (cid:43) . This peak location is taken to be the average response in that bin, and the response is mapped from referenceto jet p T by finding the average jet p T in the events in each bin after η intercalibration but before theapplication of any other in situ steps. 22issing energy in each event is reconstructed from calorimeter topo-clusters in the case of EMtopo jetcalibration and from particle-flow objects in the case of PFlow jet calibration, ensuring that the energy scaleis consistent. The Z → ee events are required to pass a dielectron trigger with p e , e >
15 GeV; Z → µµ events must pass a similar dimuon trigger with p µ ,µ >
14 GeV [47, 48]. Electrons entering the analysismust have p T >
20 GeV, ensuring that the trigger is fully efficient, must be contained within the trackersuch that | η e | < .
47, and must not fall in the calorimeter transition region (1 . < | η | < . p T >
20 GeV and to fall within | η | < .
4. Both electron andmuon candidates must also pass loose identification and isolation requirements [45, 46]. All Z +jet eventsare selected such that the reconstructed mass calculated from the electron or muon pair must be close to the Z boson mass: 66 GeV < m ee / µµ <
116 GeV. A combination of single-photon triggers are used for the γ +jet analysis, with the lowest trigger threshold corresponding to E γ T >
15 GeV. Offline photons musthave E γ T >
25 GeV and | η γ | < .
37 and must satisfy tight identification and isolation criteria [45].Both the Z +jet and γ +jet analyses have further selection requirements on the jets and event topology tosuppress pile-up and initial- and final-state radiation. All jets within ∆ R = . ∆ R = .
35 ofa lepton are removed. Jets must satisfy basic cleaning requirements and pass the JVT selection to suppresspile-up. Selected events must have one jet with p T >
10 GeV and | η | < .
8. Additional event activityis suppressed by requiring that any subleading jet must have p T < max ( . × p refT , ) GeV and that theleading jet and reference object must be relatively back-to-back with ∆ φ ref, jet > .
9. The relatively loose p T cut on subleading jets is shown to be acceptable for the MPF analysis due to its intrinsic robustness topile-up effects.Figures 10 and 11 show the MPF response calculated in Z +jet and γ +jet events for data and for twoMC samples using different generators. The lower panels show the MC simulation to data ratio for bothgenerators. The results using Powheg+Pythia8 ( Z +jet) and Pythia8 ( γ +jet) constitute the nominalcalibration while Sherpa is used to define an uncertainty due to the generator choice. In the lowest p T binof the γ +jet measurement, the discrepancy between the MC predictions is caused by a generator-level cutat 35 GeV present in the Sherpa sample. This point is included in the final in situ combination, but due toits large generator uncertainty it contributes very little to the overall weighted-average-based result (seeSection 5.2.5 and Figure 18(a)) and the total effect is negligible. The γ +jet generator uncertainty at thispoint has therefore been set to its value in the second-lowest bin for display purposes in Figure 13 to betterreflect its actual contribution to the total systematic uncertainties.Two small correction factors are derived in simulation and use the true calorimeter response , defined as theratio of measured energy in the calorimeter deposited by particles belonging to a particle-level jet to thetotal energy of the particle-level jet. The topology correction accounts for the differences in calorimeterresponse for sparse energy depositions versus those in the dense cores of jets, and is found by taking theaverage of the ratio of R MPF to the true calorimeter response in each p T bin. The showering correction accounts for the flow of particles entering or exiting across the boundaries of the jet definition and iscalculated from the ratio of the true calorimeter response to the measured response of the reconstructedjet, therefore varying with the jet algorithm and size. The total correction factor is the product of thetwo and is found to be less than 2% for jets of p T <
50 GeV and negligible above that. This correctionfactor would in principle be applied identically to R MPF in both data and simulation to better estimatejet response, but since the ratio of R MPF in data and simulation is the quantity of interest for the in situ calibration, the correction would cancel out in the ratio and only the uncertainty in its derivation is relevant.This uncertainty is taken from a comparison of two different physics lists (FTFP BERT [27] and QGSPBIC [49]) in the simulation of the particle/detector interactions and is found to be ∼
2% for jets with p T <
20 GeV, ∼ .
5% for jets with 20 GeV < p T <
40 GeV and zero for jets with p T >
40 GeV.23 · M P F R DataPowheg+Pythia8Sherpa 2.2.1
ATLAS -1 = 13 TeV, 80 fb s ee fi Z +jet, Z = 0.4 (PFlow+JES) R t k Anti-| < 0.8 jet h |
20 30 100 200 1000 [GeV]
Tref p M C / D a t a (a) Z → ee + jet
20 30 40 50 · M P F R DataPowheg+Pythia8Sherpa 2.2.1
ATLAS -1 = 13 TeV, 80 fb s mmfi Z +jet, Z = 0.4 (PFlow+JES) R t k Anti-| < 0.8 jet h |
20 30 100 200 1000 [GeV]
Tref p M C / D a t a (b) Z → µµ + jetFigure 10: Average PFlow jet response as a function of reference p T for Z +jet events where the Z boson decays into(a) electrons and into (b) muons calculated using the MPF technique. Z → ee and Z → µµ events are combined at alater stage. The black points correspond to 2015–2017 data while the pink diamonds and blue triangles correspond toindependent Monte Carlo samples from two different generators, and their error bars show the statistical uncertainties.The ratio of MC simulation to data for both generators is shown in the bottom panel and defines the in situ correctionto be applied. The dotted lines at 1 and 1.05 serve as a reference. The full set of uncertainties is shown for the Z → ee + jet and Z → µµ + jet analyses in Figure 12 and forthe γ +jet analysis in Figure 13. The dominant systematic uncertainties are due to generator differences atlower p T and to the photon energy scale at higher p T . Uncertainties in the e , µ, and γ energy scales andresolutions are taken from the calibrations provided for each physics object and are propagated through theanalysis [45, 46]. The ∆ φ and second-jet veto uncertainties are estimated by varying the cuts and comparingthe resulting response measurements. As in the η intercalibration, the JVT uncertainty is determined bycomparison with tighter and looser working points. A photon purity uncertainty is estimated for the γ +jetanalysis using control regions dominated by dijet events where one of the jets can be misidentified as aphoton. The uncertainty on the final state modelling is taken, as discussed, from the generator comparison.Limited data and MC statistics contribute to the statistical uncertainty, which is largest for the lowestand highest bins of each analysis. A bootstrapping procedure is applied to the uncertainties to suppressstatistical fluctuations as previously described.Similar analyses in the Z / γ +jet final states but explicitly balancing the reference p T against the p T of areconstructed jet ( direct balance ) are used to cross-check the jet energy scale calibration. The JES resultscomputed using direct balance showed good agreement with those derived via MPF.The innate difference in response between EMtopo and PFlow jets can be seen by comparing their measuredMPF responses. Since the MPF method uses topo-clusters and PFlow objects in computing the missingenergy, the measured responses are independent of the MCJES calibration and reflect the precalibrationresponse for each jet input type. The MPF responses measured in the γ +jet analysis for EMtopo andPFlow jets are shown in Figure 14. The shape of the EMtopo measurement follows the form of the24 · M P F R DataPythia8Sherpa 2.1.1
ATLAS +jet g , -1 = 13 TeV, 80 fb s = 0.4 (PFlow+JES) R t k Anti-| < 0.8 jet h |
30 100 200 1000 [GeV]
Tref p M C / D a t a Figure 11: Average PFlow jet response as a function of reference p T for γ +jet events calculated using the MPFtechnique. The black points correspond to 2015–2017 data. The red and blue triangles correspond to independentMonte Carlo samples from two different generators. Error bars show the statistical uncertainties. The ratio of MCsimulation to data for both generators is shown in the bottom panel and defines the in situ correction to be applied.The dotted lines at 1 and 1.05 serve as a reference. Groom’s function, which corresponds to the response expected from a hadronic calorimeter [50]. ThePFlow measurement does not follow the same shape but instead shows an improvement over the baselinecalorimeter response at low p T thanks to the inclusion of information from tracks.25 Tref p F r a c t i ona l J ES un c e r t a i n t y Total uncertaintyElectron resolutionElectron scaleJVTSecond-jet veto fD StatisticalMC generator
ATLAS -1 = 13 TeV, 80 fb s ee fi Z +jet, Z = 0.4 R t k Anti-PFlow+JES| < 0.8 jet h | (a)
20 30 100 200 1000 [GeV]
Tref p F r a c t i ona l J ES un c e r t a i n t y Total uncertaintyMuon resolutionMuon scaleJVTSecond-jet veto fD StatisticalMC generator
ATLAS -1 = 13 TeV, 80 fb s mm fi Z +jet, Z = 0.4 R t k Anti-PFlow+JES| < 0.8 jet h | (b)Figure 12: Systematic uncertainties for PFlow jets as a function of reference p T for (a) Z → ee + jet events and(b) Z → µµ + jet events calculated using the MPF technique. Uncertainties due to the JVT, second-jet veto, and ∆ φ cuts derive from the analysis technique. Electron or muon (as appropriate) scale and resolution uncertaintiesare propagated through the analysis from the uncertainties associated with the individual objects. The statisticaluncertainties come from the MC simulation/data ratio and reach a maximum value of 0.083 in (b) while the differencebetween the Pythia8 and Sherpa samples defines the MC generator uncertainty. All uncertainties are smoothed toensure that the visible fluctuations are statistically significant. Tref p F r a c t i ona l J ES un c e r t a i n t y Total uncertaintyPhoton resolutionPhoton scaleJVTSecond-jet veto fD StatisticalMC generatorPhoton purity
ATLAS -1 = 13 TeV, 80 fb s | < 0.8 jet h +jet, | g = 0.4 R t k Anti-PFlow+JES
Figure 13: Systematic uncertainties on PFlow jets as a function of reference p T for γ +jet events calculated usingthe MPF technique. Uncertainties due to the JVT, second-jet veto, and ∆ φ cuts derive from the analysis technique.Photon scale and resolution uncertainties are propagated through the analysis from the uncertainties associated withthe individual objects. The statistical uncertainties come from the MC simulation/data ratio while the differencebetween the Pythia8 and Sherpa samples defines the MC generator uncertainty. All uncertainties are smoothed toensure that the visible fluctuations are statistically significant. · M P F R Data (PFlow+JES)Data (EM+JES)
ATLAS +jet g , -1 = 13 TeV, 80 fb s = 0.4 R t k Anti-| < 0.8 jet h |
30 100 200 1000 [GeV]
Tref p P F l o w / E M Figure 14: Average jet response as a function of reference p T for γ +jet events calculated using the MPF technique in2015–2017 data. The solid points correspond to PFlow jets while the hollow points correspond to EMtopo jets. Theratio of PFlow response to EMtopo response is shown in the bottom panel. .2.3 High- p T jet calibration using multijet balance The final stage of in situ calibration derives a correction for jets with p T above the range of the Z / γ +jetanalyses using the multijet balance (MJB) technique. Events are selected with a single high- p T jet balancedagainst a system of lower- p T jets (the recoil system ). The jets of the recoil system are selected to ensurethey are well calibrated using a combination of the Z / γ +jet results (Section 5.2.2), while the leading jet isleft at the scale of the η intercalibration. The response of the system is defined as: R MJB = (cid:42) p leadT p refT (cid:43) , where p refT is taken from the vector sum of all jets in the recoil system. In a procedure parallel to that usedfor the Z / γ +jet analyses, the response is measured in bins of p refT and the correction is then mapped to theuncalibrated leading jet by finding the average p leadT of the events in each bin.Since the MJB analysis can only include events where all jets of the recoil system can already be well-calibrated, events with very high p leadT are often excluded as their second and third leading jets can havemomenta outside the range of calibration by the Z / γ +jet analyses. To address this, MJB proceeds viatwo iterations. In the first iteration, a combination of the Z / γ +jet results is used to calibrate the recoilsystem, so only events with subleading jets of p T < . p T = . p leadT = . p leadT . To suppress dijet topologies and ensure that only true multijet events are used,events must have at least three jets with p T >
25 GeV and | η | < . . p leadT . Jets are as usual required to pass JVT selections, limiting the effects ofpile-up. Isolation of the leading jet from contamination by the recoil system is ensured by requiring thatthe azimuthal angle ∆ φ between the leading jet and the direction of the recoil system is at least 0.3 radiansand that the ∆ φ between the leading jet and any individual jet in the recoil system with a p T > . p leadT isat least 1.0 radians.The MJB response in data and in four MC samples with different generators is shown in Figure 15(a). Inboth data and MC simulation, the response decreases at lower p T due to the intrinsic bias in R MJB fromthe combined effects of the leading jet isolation and p T asymmetry requirements. This bias is greater forlower- p T leading jets, but is well modelled in simulation, leaving the calibration unbiased. The lower panelshows the ratio of the response of each MC sample to data. Here, the ratio of the Sherpa sample to datadefines the nominal correction while the envelope of the other three provides an uncertainty that accountsfor the generator choice. This response ratio is constant and approximately 2% for jets above 1 TeV; belowthis point the calculated correction is slightly smaller.All uncertainties in the MJB analysis are shown in Figure 15(b). The dominant term at low p leadT isthe uncertainty from jet flavour, derived in simulation and reflecting the difference in jet response forquark-initiated and gluon-initiated jets. Two terms contribute, one reflecting the uncertainty in thefraction of gluon-initiated jets in the sample, the other based on the difference in MC simulation-derivedgluon response between generators. Other independently derived uncertainties correspond to pile-up andpunch-through effects and are propagated through the MJB analysis via the recoil system. The Z +jet, γ +jet, and η intercalibration uncertainties are propagated from the previous stages of in situ analysis. Eventselection uncertainties are determined by varying each of the analysis cuts and determining the effects29
00 1000 1500 2000 2500 [GeV] refT p æ r e f T p / l ead j e t T p Æ , M J B R
500 1000 1500 2000 2500 [GeV] refT p M C / D a t a DataPythia8Sherpa 2.1.1Powheg+Pythia8Herwig
ATLAS -1 = 13 TeV, 80 fb s Multijet balance = 0.4 R t k Anti-PFlow+JES| < 0.8 lead jet h | (a) Response
500 1000 1500 2000 2500 [GeV] refT p F r a c t i ona l J ES un c e r t a i n t y Total uncertaintyFlavour+jet g Pile-upStatisticalMC generator+jet Z -intercalibration h Event selectionPunch-through
ATLAS -1 = 13 TeV, 80 fb s Multijet balance = 0.4 R t k Anti-PFlow+JES| < 0.8 j1 h | (b) UncertaintyFigure 15: (a) Response for the leading PFlow jet in multijet events as a function of p refT and (b) the systematicuncertainties on the response. Subleading jets in the event are calibrated using the Z / γ +jet MPF corrections, whilethe leading jet is calibrated only up to the η intercalibration. The response is shown for data and for simulation usingfour different MC generators, and the MC simulation-to-data response ratios in the bottom panel correspond to thederived in situ calibration. The error bars show the statistical uncertainties. The nominal calibration is defined by thecomparison with Sherpa; its difference from the other three generators is reflected in the ‘MC generator’ uncertaintyin (b). Other uncertainties come from the event selection and MC simulation/data statistics or are propagated fromthe Z +jet, γ +jet, flavour, pile-up, η intercalibration, and punch-through studies. on the measured response ratio. Finally, the MC generator uncertainty is derived as described above bycomparing Sherpa with alternative generators in the response ratio. All uncertainties are smoothed via thebootstrapping procedure to ensure statistical significance, and the total uncertainty is found to be below1 .
5% for all considered values of p leadT .For EMtopo jets the intrinsic bias at low p T is slightly smaller and more closely tracked by simulation,leading in turn to slightly reduced systematic uncertainties for jets below p T ∼
700 GeV. Above p T > in situ uncertainties propagated from lower- p T jets have a greater impact, and therefore the uncertainty issmaller for PFlow jets than for EMtopo jets. in situ analyses One of the primary changes in LHC run conditions over the course of Run 2 was an increase in pile-up. Theaverage number of interactions per crossing ( µ ) during 2015+2016 data taking was 23.7, which increasedto 37.8 in 2017. The data taken during 2018 and to which the calibrations in this paper are also appliedhas an average of 36.1 interactions per crossing [14]. The consistency of the calibrations for events withdifferent pile-up conditions is therefore an important feature of the methods.Figure 16 shows individual bins in the response ratios of the Z +jet and γ +jet analyses separated out as afunction of number of primary vertices in the event. The Z +jet results are shown for 25 GeV < p refT <
30 GeV30nd the γ +jet results for 45 GeV < p refT <
65 GeV, in the regions where each has appropriate statisticalsignificance. The multijet balance analysis is not shown: due to the higher p T regime in which it operates itis more robust to pile-up effects. A linear fit to the data/simulation ratio has a slope compatible with zerowithin the fit uncertainties in each plot, demonstrating the stability of the in situ calibration as a function of N PV . This in turn illustrates the efficacy of the pile-up corrections described in Section 5.1.1 and showsthat the inclusively derived calibration is applicable to events with a range of pile-up conditions. M P F R DataPowheg+Pythia8Sherpa 2.2.1
ATLAS +jet Z , -1 = 13 TeV, 80 fb s = 0.4 R t k Anti-PFlow+JES < 30 GeV
Tref p | < 0.8, 25 < jet h | PV N M C / D a t a (a) M P F R DataPythia8Sherpa 2.1.1
ATLAS +jet g , -1 = 13 TeV, 80 fb s = 0.4 (PFlow+JES) R t k Anti- < 65 GeV
Tref p | < 0.8, 45 < jet h | PV N M C / D a t a (b)Figure 16: Average PFlow MPF jet response as a function of N PV for (a) Z +jet events with reference p T derived fromthe reconstructed Z boson in the range 25 GeV < p refT <
30 GeV and for (b) γ +jet events with reference p T definedfrom the photon in the range 45 GeV < p refT <
65 GeV. For the Z +jet analysis, results from the Z → ee and Z → µµ channels are combined to reduce statistical fluctuations. The black points correspond to 2015–2017 data while thepink and blue points correspond to Monte Carlo samples from two different generators. The error bars reflect thestatistical uncertainties. The ratio of MC simulation to data for both generators is shown in the bottom panel. The in situ
JES measurements can be used to calculate the dependence of the measured median p T density ρ on the event topology in simulation and data and to derive an uncertainty, as mentioned in Section 5.1.1.The density ρ is computed as a function of µ for each of the Z +jet, γ +jet, and dijet topologies as shown inthe top panels of Figure 17. Taking ρ as the value of ρ for the average pile-up conditions during 2017data taking and t1 and t2 as any two in situ measurement topologies out of Z +jet, γ +jet, and dijet, then thefollowing metric of consistency can be defined: ∆ = (cid:16) ρ t12017 − ρ t22017 (cid:17) MC − (cid:16) ρ t12017 − ρ t22017 (cid:17) data . The quantity max (| ∆ |) is then the largest value of ∆ across the various topology comparisons. The total ρ topology systematic uncertainty is given by ∆ p T = max (| ∆ |) × C JES p T × π R , where C JES is the size of the MCJES correction for a jet with the relevant p T . The second panels inFigure 17 show ρ t12017 − ρ t22017 for the comparisons ( Z +jet, dijet) and ( γ +jet, dijet) in both MC simulation31nd data. The lower panels show the difference of these two quantities between data and MC simulation,that is, ∆ Z +jet, dijet and ∆ γ +jet, dijet . The input to the systematic uncertainty max (| ∆ |) is the most discrepantof the two lines in the lower panel evaluated at µ = .
8, the value in 2017 data. As Figure 17 illustrates,this uncertainty is larger for PFlow jets than for EMtopo jets. This is understood to be due to a greatersensitivity to the underlying event when tracking information is included, which leads to greater differencesamong the simulated samples.
15 20 25 30 35 40 45 50 55 m [ G e V ] r DijetZ+jet+jet g
15 20 25 30 35 40 45 50 55 - - ] - D ij e t g [ Z /
15 20 25 30 35 40 45 50 55 m - M C - D a t a ATLAS
Closed points: dataOpen points: MC -1 = 13 TeV, 44 fbs =0.4 (EM+JES) R t Anti-k (a)
15 20 25 30 35 40 45 50 55 m [ G e V ] r DijetZ+jet+jet g
15 20 25 30 35 40 45 50 55 - - ] - D ij e t g [ Z /
15 20 25 30 35 40 45 50 55 m - M C - D a t a ATLAS
Closed points: dataOpen points: MC -1 = 13 TeV, 44 fbs =0.4 (PFlow+JES) R t Anti-k (b)Figure 17: Inputs to the ρ topology uncertainty derived in the Z +jet, γ +jet, and dijet in situ analyses. The error barsshow the statistical uncertainties. The top panels relate the p T density ρ to the mean number of interactions per bunchcrossing µ in data and MC simulation for the three input analyses. The second panels show the difference between the Z +jet and dijet and between the γ +jet and dijet measurements. The lowermost panels show the difference betweenthe data and MC simulation lines in the second panels: this defines the size of the topology uncertainty. The two plotsshow (a) EMtopo and (b) PFlow jets, illustrating why this uncertainty is larger for PFlow jets than for EMtopo jets. In situ combination
The data/MC simulation response ratios, (cid:68) p jetT / p refT (cid:69) data (cid:46) (cid:68) p jetT / p refT (cid:69) MC , from the four different ‘absolute’ in situ measurements of Z (→ ee ) +jet, Z (→ µµ ) +jet, γ +jet, and themultijet balance must be combined to produce a single calibration covering the full range of jet p T from17 GeV to 2 . p T ranges, so this proceduremust account for their relative statistical power as well as the tension between different response ratiomeasurements in the same p T range. The Z (→ ee ) +jet and Z (→ µµ ) +jet channels, though compatiblewithin uncertainties, are treated as separate measurements for the sake of the combination since they areaffected by different systematic uncertainties. 32he combination procedure is briefly summarized here; for a detailed description see Ref. [5]. Each of theabsolute in situ measurements is converted from a parameterisation in terms of reference object p T intojet p T and divided into finer bins of 1 GeV using second-order polynomial splines. A χ minimization isperformed in each bin, taking as inputs the measurements available in that p T range and their uncertainties,to determine a weight for each measurement in each bin. In this way, the measurement with the smalleststatistical and systematic uncertainties dominates the estimate of the response ratio in that bin. The weightsof each input measurement after this combination are shown in Figure 18(a) as a function of jet p T . Thefinal calibration curve is determined by smoothing the outputs from the minimization with a Gaussiankernel.
20 30 · · jetT p R e l a t i v e w e i gh t i n c o m b i na t i on + jet g ee fi Z + jet, Z mm fi Z + jet, Z Multijet
ATLAS -1 = 13 TeV, 80 fb s = 0.4 (PFlow+JES) R t k Anti- (a)
20 30 · · jetT p / N do f c ATLAS -1 = 13 TeV, 80 fb s = 0.4 (PFlow+JES) R t k Anti- (b)Figure 18: (a) The weight assigned to different techniques in the combination of in situ measurements of the relative p T response of anti- k t R = . p T . For each p T bin, the weights of the Z +jet, γ +jet, and multijet balance methods are shown. (b) The χ / N dof metric, illustratingthe compatibility of the in situ measurements being combined, as a function of jet p T . In the low p T range, thecombination is between three measurements ( Z (→ ee ) + jet, Z (→ µµ ) + jet, and γ +jet) of which the two Z +jetmeasurements have several correlated uncertainties, resulting in increased tension compared to previous calibrations. The (cid:112) χ / N dof across the measurements, before any scaling is applied, is shown in Figure 18(b). FollowingPDG guidelines, in bins where tension between the input measurements, quantified by (cid:112) χ / N dof , is foundto be greater than 1, the uncertainties in the measurements in that bin are scaled by the same tension factorto ensure that the overall level of agreement between methods is acceptable within uncertainties for all p T values [51]. However, since the tensions visible at low p T are between the two Z +jet measurements,and since the MC generator and showering and topology uncertainties are fully correlated between thetwo channels and therefore cannot contribute to this tension, these two components are excluded from thescaling procedure. The components which are not scaled are the dominant uncertainties.Figure 19 shows the final in situ combination as a function of jet p T . To complete the calibration, the inverseof the curve ( R MC / R data ) is taken as the scaling factor and applied to data. The combined measurement(solid line) for PFlow+JES jets is compared with each of the four absolute in situ analyses (empty shapes)in Figure 19(a). The total size of the correction is approximately 3% at low p T and decreases to around 2%for jets above 200 GeV. A comparison between the results for EM+JES and PFlow+JES jets is shown inFigure 19(b), where the overall size of both the correction and its uncertainty is seen to be slightly largerfor EM+JES jets.Each uncertainty component from the in situ analyses is individually propagated through the combination33 · · jetT p M C R / da t a R ATLAS -1 = 13 TeV, 80 fb s = 0.4 (PFlow+JES) R t k Anti- +jet g + jet ee fi Z + jet mm fi Z MultijetTotal uncertaintyStatistical component (a)
20 30 · · jetT p M C R / da t a R ATLAS -1 = 13 TeV, 80 fb s = 0.4 R t k Anti- Total uncertainty, PFlow+JESTotal uncertainty, EM+JES (b)Figure 19: (a) Ratio of the PFlow+JES jet response in data to that in the nominal MC event generators as a functionof jet p T for Z +jet, γ +jet, and multijet in situ calibrations. The inner horizontal ticks in the error bars give the size ofthe statistical uncertainty while the outer horizontal ticks indicate the total uncertainty (statistical and systematicuncertainties added in quadrature). The final correction and its statistical and total uncertainty bands are also shown,although the statistical uncertainty is too small to be visible in most regions. (b) A comparison of the combinedcorrection and its uncertainty for PFlow+JES and EM+JES jets. procedure. First, the relevant measured response is varied by 1 σ in the uncertainty component within itsstandard binning. The finer rebinning, χ minimization, and combination procedure is repeated, althoughusing the weights as determined for the nominal result to prevent the varied uncertainty from decreasingthe contribution of the measurement. The difference between the combined calibration curve with thesystematically shifted input and the nominal calibration curve is taken as 1 σ in the varied uncertainty.Throughout this process, each individual uncertainty source is treated as fully correlated across η and p T but entirely uncorrelated with all other uncertainty sources. After this step, the uncertainties from the Z +jet analyses are taken to be fully correlated with the same uncertainties propagated through the multijetbalance. Other assumptions of correlation between components can similarly be made and altered aftertheir propagation, allowing multiple different assumptions. The full uncertainty in the jet energy scale consists of 125 individual terms derived from the in situ measurements, pile-up effects, flavour dependence, and estimates of additional effects as summarized inTable 2. The majority of the individual terms stem from the in situ measurements and cover the effects ofanalysis selection cuts, event topology dependence, and MC mis-modelling and statistical limitations, aswell as the uncertainties associated with the calibration of the electrons, muons, and photons.The η intercalibration analysis results in five nuisance parameters, with a sixth for 2018 data only, asdiscussed in Section 5.2.1: one covers systematic effects, one covers statistical uncertainty, and three (fourin 2018) are used to parameterize the non-closure. Pile-up effects are described by four nuisance parameterswhich account for offsets and p T dependence in (cid:104) µ (cid:105) and N PV as well as event topology dependence ofthe density metric ρ . The offset and p T dependence terms are derived in data using a combination of Z +jet measurements and measurements comparing reconstructed jets with track-jets. The ρ topology34erm is the largest of the pile-up uncertainties and is determined by the maximum deviation in measureddensity between different in situ measurements under the same pile-up conditions. The flavour dependenceuncertainties are derived from simulation and account for relative flavour fractions and differing responsesto quark- and gluon-initiated jets. These uncertainties are described in more detail in Refs. [5, 6] and werementioned in Section 5.2.3 in the context of the multijet balance analysis. An additional uncertainty appliedonly to b -initiated jets covers the difference in response between jets from light- versus heavy-flavourquarks. The punch-through uncertainty accounts for mis-modelling of the GSC correction to jets whichpass through the calorimeter and into the muon system, taking the difference in jet response between dataand MC simulation in bins of muon detector activity as the systematic uncertainty. Both are discussed inmore detail in Ref. [6]. Finally, the high- p T ‘single particle’ uncertainty is derived from studies of theresponse to individual hadrons and is used to cover the region beyond 2.4 TeV, where the MJB analysisno longer has statistical power [27]. When calibrating MC samples simulated using AFII, an additionalnon-closure uncertainty is applied to account for the difference in jet response between these samples andthose which used full detector simulation.The total jet energy scale uncertainty is shown in Figure 20(a) as a function of jet p T for fixed η jet = η for fixed p jetT =
60 GeV. A dijet-like composition of the sample (thatis, predominantly gluons) is assumed in computing the flavour uncertainties. The uncertainties in the η intercalibration analysis are labelled ‘relative in situ JES’ with the non-closure uncertainty creating theasymmetric peaks around η = ± .
5. Uncertainties in all other in situ measurements are combined into the‘absolute in situ
JES’ term, which also includes the single-particle uncertainty.
20 30 × × jetT p F r a c t i ona l J ES un c e r t a i n t y ATLAS = 0.4 (PFlow+JES) R t k Anti- = 13 TeV s Data 2015-2017, = 0.0 η Inclusive jets Total uncertainty JES in situ
Absolute JES in situ
Relative Flav. compositionFlav. responsePile-upPunch-through (a) − − − − η F r a c t i ona l J ES un c e r t a i n t y ATLAS = 0.4 (PFlow+JES) R t k Anti- = 13 TeV s Data 2015-2017, = 60 GeV jetT p Inclusive jets Total uncertainty JES in situ
Absolute JES in situ
Relative Flav. compositionFlav. responsePile-upPunch-through (b)Figure 20: Fractional jet energy scale systematic uncertainty components for anti- k t R = . p T at η = η at p T =
60 GeV, reconstructed from particle-flow objects. The totaluncertainty, determined as the quadrature sum of all components, is shown as a filled region topped by a solid blackline. Flavour-dependent components shown here assume a dijet flavour composition. able 2: Sources of uncertainty in the jet energy scale.Component Description η intercalibrationSystematic mis-modelling Envelope of the generator, pile-up, and event topology variationsStatistical component Statistical uncertainty (single component)Non-closure Three components describing non-closure at high energy and at η ∼ ± . η ∼ ± . Z + jetElectron scale Uncertainty in the electron energy scaleElectron resolution Uncertainty in the electron energy resolutionMuon scale Uncertainty in the muon momentum scaleMuon resolution (ID) Uncertainty in muon momentum resolution in the IDMuon resolution (MS) Uncertainty in muon momentum resolution in the MSMC generator Difference between MC event generatorsJVT cut Jet vertex tagger uncertainty ∆ φ cut Variation of ∆ φ between the jet and Z bosonSubleading jet veto Radiation suppression through second-jet vetoShowering & topology Modelling energy flow and distribution in and around a jetStatistical Statistical uncertainty in 28 discrete p T terms γ + jetPhoton scale Uncertainty in the photon energy scalePhoton resolution Uncertainty in the photon energy resolutionMC generator Difference between MC event generatorsJVT cut Jet vertex tagger uncertainty ∆ φ cut Variation of ∆ φ between the jet and photonSubleading jet veto Radiation suppression through second-jet vetoShowering & topology Modelling energy flow and distribution in and around a jetPhoton purity Purity of sample used for γ + jet balanceStatistical Statistical uncertainty in 16 discrete p T termsMultijet balance ∆ φ (lead, recoil system) Angle between leading jet and recoil system ∆ φ (lead, any sublead) Angle between leading jet and closest subleading jetMC generator Difference between MC event generators p asymT selection Second jet’s p T contribution to the recoil systemJet p T Jet p T thresholdStatistical Statistical uncertainty in 28 discrete p T termsPile-up µ offset Uncertainty in the µ modelling in MC simulation N PV offset Uncertainty in the N PV modelling in MC simulation ρ topology Uncertainty in the per-event p T density modelling in MC simulation p T dependence Uncertainty in the residual p T dependenceJet flavourFlavour composition Uncertainty in the proportional sample composition of quarks and gluonsFlavour response Uncertainty in the response of gluon-initiated jets b -jets Uncertainty in the response of b -quark-initiated jetsPunch-through Uncertainty in GSC punch-through correctionSingle-particle response High- p T jet uncertainty from single-particle and test-beam measurementsAFII non-closure Difference in the absolute JES calibration for simulations in AFII .3.1 Uncertainty correlations and reductions The detail contained in 125 independent nuisance parameters is far more than is required by most analyses,so it is necessary to reduce the uncertainty description to a smaller number of terms. One could imagine asingle ‘Jet energy scale’ nuisance parameter constructed by adding in quadrature all of the independentcomponents. However, a meaningful set of correlations exist between the jet energy scale uncertaintiesfor two jets at different η and p T as a result of the structures of the nuisance parameters. In the case ofreduction to a single component, the entirety of this correlation information would be lost and an unrealisticassumption – that of full correlation between the jet energy scale uncertainties for any values of η and p T –would be enforced. In practice, a variety of reduced uncertainty schemes are provided to allow simplifieddescriptions with a minimum loss of correlation information.The 98 uncertainty components stemming from the absolute in situ analyses are functions only of p T andthus their behaviour can be easily represented by a smaller number of orthogonal terms. An eigenvectordecomposition is performed on a covariance matrix of these uncertainty components and the largest ofthe resulting orthogonal terms are kept separate as new effective nuisance parameters [5]. The remainingterms are combined into a single residual nuisance parameter. To determine how many components tokeep independently and how many to combine in the residual term, the covariance matrix for the reducedset is also computed and the difference in correlation in each jet η and p T between the reduced set andthe full set is calculated. This difference is taken as a measure of the information loss and the number ofcombined terms is adjusted so that the difference is below an acceptable bound (usually 0 . global reduction combines all p T -dependent in situ uncertaintycomponents regardless of their sources and results in 8 reduced components for a total of 23 once thetwo-dimensional terms (not arising from the in situ analyses and not reduced) are included. The categoryreduction combines the p T -dependent in situ uncertainty components in separate groups based on theirorigin (detector, statistical, modelling, or mixed) and results in 15 reduced components for a total of30. The JES correlation matrix for the full set of nuisance parameters is shown in Figure 21(a). Thebin-by-bin correlation loss between the full set of nuisance parameters and the category reduction is shownin Figure 21(b) and is below 0.05 everywhere as required.While the same procedure could in principle be used for the components which depend on both p T and η ,the complexity added by the second dimension means that nearly as many eigenvectors would be neededto adequately describe the correlations as there were original terms and so the gain would be minimal.However, many analyses still require fewer than 25 nuisance parameters and are not affected by loss ofcorrelation information. To provide suitable uncertainties for these, a strong reduction procedure is used togroup the globally reduced versions of the absolute in situ uncertainties together with the two-dimensionaluncertainties into three effective nuisance parameters as detailed in Ref [7]. The three terms of the η intercalibration non-closure uncertainty are kept separate because their two-dimensional shapes areespecially difficult to reduce and would cause an unacceptably large correlation loss.Four different sets ( scenarios ) of the three effective nuisance parameters are created by varying thecombinations of terms they contain. The varied sets are chosen such that the correlation loss in eachis constrained to an η – p T range which is well described by a different set. The metric for assessingperformance of the four scenarios is the uncovered correlation loss , defined as the maximum difference incorrelation between any two reduced scenarios minus the minimum difference in correlation between anyreduced scenario and the full set of nuisance parameters. The uncovered correlation loss is calculated for afine grid of points in η and p T , ensuring no small-scale structures are missed. Contents of the effectivenuisance parameters are varied, keeping systematic uncertainties with similar behaviours mostly grouped37 c o rr e l a t i on
20 30 × × jetT p [ G e V ] j e t T p ATLAS = 0.4 (PFlow+JES) R t k Anti- ) = (0.0,0.0) jet2 η , jet1 η ( -1 = 13 TeV, 80 fbs (a) − − − − − c o rr e l a t i on d i ff e r en c e
20 30 × × jetT p [ G e V ] j e t T p ATLAS = 0.4 (PFlow+JES) R t k Anti- ) = (0.0,0.0) jet2 η , jet1 η ( -1 = 13 TeV, 80 fbsMean value -0.00, max -0.02 at (619,2504) GeV (b)Figure 21: (a) The jet energy scale correlation matrix for two PFlow+JES jets at η = p T -dependent in situ nuisance parameters and (b) the difference in correlation information between the full descriptionand the category reduction. The maximum loss of correlation information is 0.02 and occurs at the ( p j , p j ) locationspecified by the text at the bottom of the plot. together, until a set of scenarios is found in which the maximum uncovered correlation loss is kept below0.25 and confined to sufficiently small regions that the average correlation loss in an η – p T plane does notexceed 0.02.The uncovered correlation loss is shown for the final set of three strongly reduced scenarios in Figure 22.Here, each small square within the larger figure corresponds to the correlation matrix in ( p j T , p j T ) where thejet whose p T varies along the x direction has the η det value at the bottom of the column and the jet whose p T varies along the y direction has the η det value at the end of the row. Regions in white are kinematicallyforbidden. 38 igure 22: The uncovered correlation loss between the full set of nuisance parameters and the three strongly reducedscenarios in different regions of η and p T , where p T is varied along the x and y axes of each sub-plot within thelarger image. White regions correspond to kinematically forbidden regions of phase space. The top (bottom) numberin each sub-plot gives the maximum (mean) uncovered correlation loss, multiplied by a factor of 100 for visibility,with the mean excluding kinematically forbidden regions. Although the scale of individual calibrations may vary between EMtopo and PFlow jets, the finaluncertainties are similar in size. A slightly larger pile-up uncertainty contribution in PFlow jets due to theimpact of the underlying event is offset by smaller in situ uncertainties, leading to a comparable total overalluncertainty. Figure 23 shows the total uncertainty in EMtopo and PFlow jets for a range of p T values atfixed η = η values at fixed p T =
60 GeV. The level of agreement is representative ofother p T and η ranges. 39 × × jetT p F r a c t i ona l J ES un c e r t a i n t y ATLAS = 0.4 R t k Anti- = 13 TeV s Data 2015-2017, = 0.0 η Total uncertaintyPFlow+JESEM+JES (a) − − − − η F r a c t i ona l J ES un c e r t a i n t y ATLAS = 0.4 R t k Anti- = 13 TeV s Data 2015-2017, = 60 GeV jetT p Total uncertaintyPFlow+JESEM+JES (b)Figure 23: Fractional jet energy scale systematic uncertainty summed across all components for anti- k t R = . p T at η = η at p T =
60 GeV. The total uncertainty is shown for bothEMtopo and PFlow jets. Contributions from topology-dependent components are calculated assuming a dijet flavourcomposition.
Precise knowledge of the jet energy resolution (JER) is important for detailed measurements of SM jetproduction, measurements and studies of the properties of the SM particles that decay to jets (e.g. W / Z bosons, top quarks), as well as searches for physics beyond the SM involving jets. The JER also affects themissing transverse momentum, which plays an indispensable role in many searches for new physics andmeasurements involving particles that decay into neutrinos, and thus rely on well-reconstructed missingmomentum.The dependence of the relative JER on the transverse momentum of the jet may be parameterized using afunctional form expected for calorimeter-based resolutions, with three independent contributions, namelythe noise ( N ), stochastic ( S ) and constant ( C ) terms [52]: σ ( p T ) p T = Np T ⊕ S √ p T ⊕ C . (4)The noise ( N ) term is due to the contribution of electronic noise to the signal measured by the detectorfront-end electronics, as well as that due to pile-up. Since both contribute directly to the energy measuredin the calorimeter but are approximately independent of the energy deposited by the showing particles, thecontribution to the JER scales like 1 / p T . The noise term is expected to be significant in the low- p T region,below ∼
30 GeV. Statistical fluctuations in the amount of energy deposited are captured by the stochastic( S ) term, which represents the limiting term in the resolution up to several hundred GeV in jet p T . The S term contribution to the JER scales like 1 /√ p T . The constant ( C ) term corresponds to fluctuations that area constant fraction of the jet p T , such as energy depositions in passive material (e.g. cryostats and solenoidcoil), the starting point of the hadron showers, and non-uniformities of response across the calorimeter.The constant term is expected to dominate the high- p T region, above approximately 400 GeV.In order to measure the JER, jet momentum must be measured precisely. This implies that the jets musteither recoil against a reference object whose momentum can be measured precisely, or be balanced against40ne another in a well-defined dijet system [5, 6]. Measurements using the latter approach are presentedhere, as well as a method for measuring the contributions to the resolution from the noise term ( N ) due toboth pile-up and electronics. The 2017 data, corresponding to an integrated luminosity of 44 fb − is usedfor these measurements. Dijet events are both plentiful and produced via a set of 2 → dijet balance method rely on the approximatescalar balance between the transverse momenta of the two leading jets. Deviations from exact balance,measured via the asymmetry, given by A ≡ p probeT − p refT p avgT , (5)are due to a combination of experimental resolution, the presence of additional radiation in the event, andbiases due to the event selection used in the measurement. In Eq. (5), p refT is the p T of a reference jetwhich is required to be located in a well-calibrated region of the detector ( η refdet ), taken here to be the centralregion of the calorimeter 0 . ≤ | η refdet | < .
7, where the seam at η det = p probeT , may belocated either within this central reference region or beyond it, with | η probedet | < .
5. The probe jet is the jetfor which the resolution is to be measured and p avgT is the mean of the probe and reference jet momenta, p avgT = ( p probeT + p refT )/
2. The standard deviation of A for a particular ( p avgT , η probedet ) bin is denoted by σ A ,and in the case of a measurement of the probe jet asymmetry may be expressed as σ probe A = σ probe p T ⊕ σ ref p T (cid:104) p avgT (cid:105) = (cid:28) σ p T p T (cid:29) probe ⊕ (cid:28) σ p T p T (cid:29) ref , where σ probe p T and σ ref p T are the standard deviations of p probeT and p refT , respectively, and are used to denotethe JER for each of the relevant objects. For calibrated jets, (cid:104) p probeT (cid:105) = (cid:104) p refT (cid:105) = (cid:104) p avgT (cid:105) in the referenceregion. The reference jet relative resolution, (cid:104) σ p T / p T (cid:105) ref , must therefore be subtracted from the measuredasymmetry distribution in order to extract the resolution of the probe jet as (cid:28) σ p T p T (cid:29) probe = σ probe A (cid:9) (cid:28) σ p T p T (cid:29) ref . (6)Equation (6) is valid in the probe region as well, up to a correction factor that accounts for the potentialoverall imbalance between the reference jet and the probe jet in that region. This correction factor is foundto be negligible ( < p T balance of the measuredjets, and thus the measured asymmetry distribution, is measurably affected on an event-by-event basisby physics effects such as additional radiation, non-perturbative processes including hadronization andmulti-parton interactions, and others that may lead to particle losses and additions in the measured jets.Consequently, the measured dijet balance asymmetry distribution represents a convolution of the intrinsicdetector resolution and the particle-level balance affected by the aforementioned effects. The determination41f σ probe A must therefore account for such effects, for example by subtracting the particle-level quantityfrom the measured quantity in quadrature: (cid:16) σ probe A (cid:17) det = (cid:16) σ probe A (cid:17) meas (cid:9) σ truth A . The results presented here use an iterative fitting procedure to extract the impact of these effects andto isolate the intrinsic detector resolution, (cid:16) σ probe A (cid:17) det , by assuming a Gaussian convolution of detectoreffects with the particle-level balance. First, the asymmetry distribution measured at particle level in MCsimulation is fitted with an ad hoc function A truth based on exponential curves and found to describe itwell. Second, the measured asymmetry distribution, A meas , is fitted by the function A meas = A truth ⊗ R( µ det A , σ det A ) , taking A truth from the particle-level fit and where R( µ det A ) is a Gaussian distribution with width σ det A representing the detector resolution for the probe jet and offset µ det A accounting for any residual non-closurein the JES calibration.Collision data used for the dijet balance measurement are collected using specific combinations of centraland forward jet triggers for each of the 11 p avgT ranges used in the measurement. Trigger selections arerequired to be at least 99% efficient in the range of p avgT in which a particular combination is used. Jetsmust also pass JVT selection requirements as described in Section 5.2.2.Topology criteria are applied to select well-defined dijet production processes with minimal contributionsdue to additional radiation or higher-order processes. The azimuthal angle, ∆ φ , between the two leadingjets in the event and the maximum p T of a potential third jet, p j T , are constrained by the following twocriteria: ∆ φ ( j , j ) ≥ . , p j T < max (
25 GeV , . · p avgT ) . Example asymmetry distributions are shown in Figure 24 in two representative bins of p avgT and η det . Aniterative Gaussian fit to the core of the asymmetry distribution is used to extract the JER. The result of themeasurement of the relative JER and its systematic uncertainty is shown in Figure 25 for a single narrowrange of η and as a function of p avgT . The JER is observed to be slightly underestimated by MC simulationin this central region of the detector.Systematic uncertainties are dominated by imprecise knowledge of the scale of the jets at low p T , whichresults in an approximate 1.5% uncertainty at 40 GeV, whereas the non-closure of the dijet balance methoditself is largely dominant at higher p T . The non-closure uncertainty is evaluated as the difference betweenthe resolution measured using the in situ procedure applied to MC simulation and the particle-levelresolution, σ ( R )/ R , where R = p recoT / p trueT . Good agreement is found, resulting in an uncertainty in therelative resolution that is approximately 0.4% and generally increases with p T due to the non-Gaussianjet response. At lower p T the uncertainties propagated from the JES dominate. The increase in JESuncertainty around 800 GeV is a result of the single-particle uncertainty (see Section 5.3): the jet energyscale calibration used for the dijet energy resolution measurement is necessarily based on a smaller datasetthan the one presented in this paper, allowing the two measurements to converge simultaneously, and as aresult the statistics were lower and the single-particle uncertainty became dominant at a lower p jetT valuethan in Figure 20(a). Additional systematic uncertainties are estimated by varying the analysis cuts and theJVT selection and by comparing the result with one obtained from an alternative MC generator (Sherpa2.1.1). 42 .6 − − − T p Dijet 00.511.522.53 A r b . un i t s (2017 data)Reconstructed(Pythia8)Particle-level Preliminary ATLAS , dijets -1 = 13 TeV, 43.6 fb s = 0.4 (PFlow+JES) R t k Anti- [GeV] < 110 avgT p ≤
80 < 0.7 probedet η ≤ − − − − T p Dijet 012345 A r b . un i t s (2017 Data)Reconstructed(Pythia8)Particle-level Internal ATLAS , dijets -1 = 13 TeV, 44 fb s = 0.4 (PFlow+JES) R t k Anti- [GeV] < 400 avgT p
300 < < 1.8 det η (a) - - - - T p Dijet 012345 A r b . un i t s (2017 Data)Reconstructed(Pythia8)Particle-level ATLAS , dijets -1 = 13 TeV, 44 fb s = 0.4 (PFlow+JES) R t k Anti- [GeV] < 400 avgT p
300 < < 1.8 det h (b)Figure 24: Asymmetry distribution measured in data and particle-level Pythia8 for PFlow jets in two example p T and η ranges. Error bars represent the statistical uncertainty. (a) Jets with 80 GeV < p avgT <
110 GeV areshown in the range 0 . < | η det | < .
7, where the distributions are symmetric by construction. (b) Jets with300 GeV < p avgT <
400 GeV are shown in the range 1 . < | η det | < .
8. In this η det range the distributions can beasymmetric. Two fits are performed iteratively: the particle-level asymmetry is modelled with an ad hoc functionwhich is subsequently convolved with a Gaussian function in order to describe the reconstructed asymmetry. Thedetector resolution is then extracted from the Gaussian fit parameter. R e l a t i v e j e t ene r g y r e s o l u t i on
50 100 200 300 1000 [GeV] jetT p D a t a / M C DataPythia8
ATLAS , dijets -1 = 13 TeV, 44 fb s = 0.4 (PFlow+JES) R t k Anti- < 0.7 det h £ (a)
50 100 200 300 1000 [GeV] T p Jet 00.010.020.030.04 U n c e r t a i n t y on r e l a t i v e j e t ene r g y r e s o l u t i on ATLAS , dijets -1 = 13 TeV, 44 fb s = 0.4 (PFlow+JES) R t k Anti- = 0.4 det h Jet energy scale j3T p & jj fD Physics modellingPile-up jet rejectionNon-closureTotal systematic uncertainty (b)Figure 25: (a) Relative jet energy resolution and (b) absolute uncertainty in the relative resolution as a function of p T for PFlow jets in the central region of the detector, measured using the dijet balance method. The resolution in data isshown in black points with error bars indicating statistical uncertainties; the resolution in detector-level simulatedevents is shown by the blue curve with total systematic uncertainty given by the blue band. The systematic uncertaintyis dominated by terms propagated from the JES uncertainty, while additional terms arise from the analysis selection,pile-up rejection (JVT), physics modelling (comparison with alternative generator), and non-closure effects. Thebump in uncertainty around 800 GeV comes from the single-particle uncertainty. .2 Noise measurement using random cones Direct estimates of the noise term of Eq. (4) are obtained by measuring the fluctuations in the energy depositsdue to pile-up using data samples that are collected by random unbiased triggers. These measurements areperformed using the random cones method in which energy deposits in the calorimeter are summed at theenergy scale of the constituents in circular areas analogous to the jet area for anti- k t R = . p c1T and p c2T , are obtained at random φ values and within opposite ± ∆ η regions and the difference between them, ∆ p RCT , provides a measure of the random fluctuations ofdeposited energy. Multiple non-overlapping cones are selected within each event to maximize statisticalpower; this is demonstrated to cause no bias in the overall result. This random cone balance is given by ∆ p RCT = p c1T − p c2T , and the estimated pile-up noise is determined by the central 68% confidence interval of the distributionof ∆ p RCT , σ RC , sampled over many events as a function of both η and pile-up levels, as indexed by µ .Specifically, the noise term due to pile-up, N PU , is determined as N PU = σ RC √ , (7)where the width of the distribution is divided by 2 to obtain the half-width of the distribution, and by √ ∆ p RCT is shown inFigure 26(a).The energy scale of the noise estimated by N PU in Eq. (7) is the constituent energy scale and not that ofthe jets measured in Section 6.1. In order to compare the measurement of the noise term N PU using therandom cone method with the JER measured at the fully calibrated scale (e.g. PFlow+JES) a conversionfactor is required. The nominal JES calibration factor is used to perform this conversion to the appropriateenergy scale. The result is an estimate of the noise due to pile-up that may be directly compared with themeasured JER.A closure test of the random cone measurements is performed by comparing the in situ measurement ofthe calibrated N PU with the expectation from MC simulation. Results are reported here for PFlow jets.To isolate the contribution to the JER from pile-up noise in the MC simulation, the JER is determined insimulated events both with and without pile-up and a subtraction in quadrature is performed between theextracted resolutions. The two JER determinations in MC simulation events with and without pile-up areshown in Figure 26(b) and their quadratic difference is compared directly to the in situ measurement fromthe random cones method. Each is fitted, as shown by the dotted lines in Figure 26(b): the random conemeasurement is fitted with N / p T while the quadratic difference is fitted with N / p T ⊕ S /√ p T to account fornon-negligible stochastic contributions. The non-closure of the method is largely due to the differencesin topo-cluster formation sensitivity to pile-up and electronic noise in the presence versus absence ofhard-scatter particles, and is taken as a systematic uncertainty in the result. This non-closure uncertaintyis the dominant uncertainty in the JER noise term, ranging from approximately 17% in the most centralregion to 75% in the endcap transition region (2 . < | η | < . µ = − − − − [GeV] cone 2T p - cone 1T p A r b . un i t s ATLAS
Preliminary = 13 TeV s , zero-bias data -1 R Random cones (| < 0.7 det η | objectsParticle flowinterval68% conf. (a)
20 30 100 200 1000 [GeV] truthT p Jet 00.050.10.150.20.250.30.35 ) t r u t h T p / c a li b T p ( m ) / t r u t h T p / c a li b T p ( s Random cones (MC)2017 pile-upNo pile-upQuadratic difference
ATLAS = 13 TeV s zero bias and dijets = 0.4 (PFlow+JES) R t k Anti-| < 0.7 det h | (b)Figure 26: (a) The difference in the random cone sums, ∆ p RCT , measured in the central region ( | η | < .
7) in randomlytriggered data using PFlow objects. (b) Comparison between the pile-up noise term N PU determined using therandom cone method (black solid circles) and the expectation from MC simulation (orange squares) as extracted fromthe difference in quadrature of MC simulation with (red downward triangles) and without (blue upward triangles)pile-up. Results are shown at the PFlow+JES energy scale for jets in the central region of the detector ( | η | < . noise term is extracted as N µ = . The total noise term used in the JER combination is therefore taken tobe N = N PU ⊕ N µ = and is shown as a function of η in Figure 27 along with its systematic uncertainties.The dominant systematic uncertainty in the random cone measurement of N PU is the previously discussednon-closure uncertainty, but additional terms arise from varying the quantile of the confidence intervalused to extract σ RC and from using a different estimate of the conversion factor to the calibrated JES scale.Two systematic uncertainties apply to N µ = : a 20% relative uncertainty conservatively estimating thedifferences in JER between data and MC simulation and an uncertainty due to the fit parameterization andstability. The systematic uncertainties enter the combined JER fit unsymmetrized in η but are symmetrizedduring the statistical combination, and so the one-sided components are symmetrized in Figure 27 toillustrate their final contribution to the total uncertainty.46 h |24681012 J E R N o i s e T e r m [ G e V ] Full noise term non-closure PU N JES conv. factor definition RC s =0 MC vs. data m =0 fit instability m Total uncertainty
ATLAS -1 = 13 TeV, 2.4 pbs = 0.4 R t k Anti-PFlow+JES
Figure 27: Noise term due to pile-up estimated using the random cone method and its uncertainties as a function of η .The dominant uncertainty is due to non-closure in the method. Additional uncertainties address the σ RC definition,the JER conversion factor, the differences in JER between data and MC simulation, and the fit stability in extractingthe µ = σ RC definition uncertainty and µ = in situ jet energy resolution A combined measurement of the JER is obtained by performing a fit to the dijet balance measurements(Section 6.1) using a constraint on the noise term ( N ) derived from the random cones measurement and µ = p jetT , the JER combination uses the functionalform from Eq. (4). A fit to the dijet measurement data is performed, fixing the noise term to the valuemeasured by the random cone analysis. Dijet measurement uncertainties are taken to be fully correlatedbetween η bins. Uncertainties due to the random cones measurements are determined by propagating thenoise term uncertainties and repeating the fit with different values of N . These uncertainties are taken to bedecorrelated between central ( | η | < .
5) and forward ( | η | > .
5) regions.The resulting combined measurement of the JER for PFlow+JES jets is shown in Figure 28(a). The dijet47 · · jetT p T p ) / T p ( s J e t ene r g y r e s o l u t i on , in situ Dijet T p / N , in situ Noise term C ¯ T p / S ¯ T p / N combination, In situ
MC prediction = 0.4 (PFlow+JES) R t k Anti- | < 0.7 h | £ ATLAS -1 = 13 TeV, 44 fb s (a)
20 30 × × jetT p T p ) / T p ( σ A b s o l u t e un c e r t a i n t y on ATLAS = 0.4 (PFlow+JES) R t k Anti- = 13 TeV s Data 2017, = 0.2 η Total uncertainty MC σ < data σ Method closure where Noise term, random cones method JER (systematics) in situ
Dijet JER (statistics) in situ
Dijet (b)Figure 28: (a) The relative jet energy resolution as a function of p T for fully calibrated PFlow+JES jets. The errorbars on points indicate the total uncertainties on the derivation of the relative resolution in dijet events, adding inquadrature statistical and systematic components. The expectation from Monte Carlo simulation is compared withthe relative resolution as evaluated in data through the combination of the dijet balance and random cone techniques.(b) Absolute uncertainty on the relative jet energy resolution as a function of jet p T . Uncertainties from the two in situ measurements and from the data/MC simulation difference are shown separately. measurement data points are shown along with the total in situ combination, while the constraint on thenoise term derived from random cones and included in that combination is demonstrated by plotting N / p T and its uncertainties as a separate curve for illustrative purposes. Figure 28(b) shows the absoluteuncertainties on the combined JER measurement. For each value of p jetT and η det a toy jet is created and thesize of each JER nuisance parameter corresponding to it is retrieved and plotted.Comparisons of the JER measurements for PFlow+JES and EM+JES jets, as a function of both p jetT and η ,are provided in Figure 29. The fit to the resolution as a function of p T for the PFlow+JES jets shows animprovement in resolution over EM+JES jets at low p T .Figure 30 shows the total JER uncertainty in EMtopo and PFlow jets for a range of p T values at fixed η = . η values at fixed p T =
60 GeV. The level of agreement is representative of other p T and η ranges. 48 · · jetT p T p ) / T p ( s J e t ene r g y r e s o l u t i on , in situ EM+JES EM+JES total uncertainty in situ
PFlow+JES PFlow+JES total uncertainty = 0.4 R t k Anti- | < 0.7 h | £ ATLAS -1 = 13 TeV, 44 fb s (a) jet h |00.050.10.150.20.25 T p ) / T p ( s J e t ene r g y r e s o l u t i on , EM+JES total uncertaintyPFlow+JES total uncertainty = 0.4 R t k Anti- = 60 GeV jetT p ATLAS -1 = 13 TeV, 44 fb s (b)Figure 29: The relative jet energy resolution for fully calibrated PFlow+JES jets (green curve) and EM+JES jets(blue curve) (a) as a function of p jetT and (b) as a function of η . The fit to the resolution as a function of p jetT for thePFlow+JES jets shows an improvement in resolution over EM+JES jets at low- p T .
20 30 × × jetT p T p ) / T p ( σ A b s o l u t e un c e r t a i n t y on ATLAS = 0.4 R t k Anti- = 13 TeV s Data 2017, = 0.2 η Total uncertaintyEM+JESPFlow+JES (a) − − − − η T p ) / T p ( σ A b s o l u t e un c e r t a i n t y on ATLAS = 0.4 R t k Anti- = 13 TeV s Data 2017, = 60 GeV jetT p Total uncertaintyEM+JESPFlow+JES (b)Figure 30: Fractional jet energy resolution systematic uncertainty summed across all components for anti- k t R = . p T at η = . η at p T =
60 GeV. The total JER uncertainty isshown for both EM+JES and PFlow+JES jets. .4 Application of JER and its systematic uncertainties In order to ensure that the resolution of the jet energy scale in simulation matches that in data whereverpossible, a smearing procedure is recommended. For regions of jet p T in which the resolution in data islarger than in MC simulation, the simulation sample should be smeared until its average resolution matchesthat of data. In regions of jet p T where resolution is smaller in data than in MC simulation, no smearing isperformed, since the data should remain unaltered.JER systematic uncertainties are propagated through physics analyses by smearing jets according to aGaussian function with width σ smear . If σ nom is the nominal JER of the sample, after MC simulationsmearing if necessary, and σ NP is the one-standard-deviation variation in the uncertainty component to beevaluated, then: σ = ( σ nom + | σ NP |) − σ . Application of JER systematic uncertainties must account for two factors: first, anti-correlations across asingle uncertainty component, and second, differences in resolution between data and MC simulation.Anti-correlation becomes an issue when a single JER component is positive in some regions of phase spaceand negative in others. To propagate such systematic uncertainties to analyses, smearing should be appliedto the simulation when σ NP > σ NP <
0. It should be noted that the nominaldata remains unchanged as this applies only to the application of systematic uncertainties. In the case thatdata statistics are too low to safely smear, pseudo-data may be smeared instead.Differences in resolution between data and MC simulation are already accounted for by the application ofadditional smearing when the resolution in simulation is better than in data. When the JER in smaller indata, this difference is accounted for by applying its full value as an additional systematic uncertainty: σ NP = σ datanom − σ MCnom . This term is zero everywhere for the toy jets used to create Figure 28(b), but for circumstances where dataand MC simulation are being compared it can be non-zero for some p T ranges. The calibration of the jet energy scale and resolution for jets reconstructed with the anti- k t algorithm withradius parameter R = . − of data recorded with the ATLAS detector during 2015–2017in pp collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider.A sequence of simulation-based corrections removes the contribution to the jet energy from additionalproton–proton interactions in the same or nearby bunch crossings, corrects the jet so that it agrees in energyand direction with particle-level jets and, improves the jet energy resolution. Any remaining differencebetween simulation and data is removed with in situ techniques using well-measured reference objects,including photons, Z bosons, and other jets, such that the energy scale of fully calibrated jets is unity50ithin uncertainties. The jet energy resolution is measured in a dijet balance system, and the contributionto the resolution from the noise term due to pile-up and electronics is also measured. The relative jetenergy resolution ranges from 0.25 (0.35) to 0.04 for PFlow (EMtopo) jets as a function of jet p T .Systematic uncertainties in the jet energy scale for central jets ( | η | < .
2) vary from 1% for a large range ofhigh- p T jets (250 < p T < p T (20 GeV) and 3.5% at very high p T ( > . Acknowledgements
We thank CERN for the very successful operation of the LHC, as well as the support staff from ourinstitutions without whom ATLAS could not be operated efficiently.We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFWand FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC andCFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia;MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS andCEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC andHong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST,Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA,Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRSand MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden;SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, UnitedKingdom; DOE and NSF, United States of America. In addition, individual groups and members havereceived support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon2020, Marie Skłodowska-Curie Actions and COST, European Union; Investissements d’Avenir Labex,Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thalesand Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel;CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain;Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.The crucial computing support from all WLCG partners is acknowledged gratefully, in particular fromCERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3(France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC(Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resourceproviders. Major contributors of computing resources are listed in Ref. [53].
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García Navarro , J.A. García Pascual , C. Garcia-Argos ,57. Garcia-Sciveres , R.W. Gardner , N. Garelli , S. Gargiulo , C.A. Garner , V. Garonne ,S.J. Gasiorowski , P. Gaspar , A. Gaudiello , G. Gaudio , I.L. Gavrilenko , A. Gavrilyuk ,C. Gay , G. Gaycken , E.N. Gazis , A.A. Geanta , C.M. Gee , C.N.P. Gee , J. Geisen ,M. Geisen , C. Gemme , M.H. Genest , C. Geng , S. Gentile , S. George , T. Geralis ,L.O. Gerlach , P. Gessinger-Befurt , G. Gessner , S. Ghasemi , M. Ghasemi Bostanabad ,M. Ghneimat , A. Ghosh , A. Ghosh , B. Giacobbe , S. Giagu , N. Giangiacomi ,P. Giannetti , A. Giannini , G. Giannini , S.M. Gibson , M. Gignac , D.T. Gil , B.J. Gilbert ,D. Gillberg , G. Gilles , D.M. Gingrich , M.P. Giordani , P.F. Giraud , G. Giugliarelli ,D. Giugni , F. Giuli , S. Gkaitatzis , I. Gkialas , E.L. Gkougkousis , P. Gkountoumis ,L.K. Gladilin , C. Glasman , J. Glatzer , P.C.F. Glaysher , A. Glazov , G.R. Gledhill ,I. Gnesi , M. Goblirsch-Kolb , D. Godin , S. Goldfarb , T. Golling , D. Golubkov ,A. Gomes , R. Goncalves Gama , R. Gonçalo , G. Gonella , L. Gonella ,A. Gongadze , F. Gonnella , J.L. Gonski , S. González de la Hoz , S. Gonzalez Fernandez ,R. Gonzalez Lopez , C. Gonzalez Renteria , R. Gonzalez Suarez , S. Gonzalez-Sevilla ,G.R. Gonzalvo Rodriguez , L. Goossens , N.A. Gorasia , P.A. Gorbounov , H.A. Gordon ,B. Gorini , E. Gorini , A. Gorišek , A.T. Goshaw , M.I. Gostkin , C.A. Gottardo ,M. Gouighri , A.G. Goussiou , N. Govender , C. Goy , I. Grabowska-Bold , E.C. Graham ,J. Gramling , E. Gramstad , S. Grancagnolo , M. Grandi , V. Gratchev , P.M. Gravila ,F.G. Gravili , C. Gray , H.M. Gray , C. Grefe , K. Gregersen , I.M. Gregor , P. Grenier ,K. Grevtsov , C. Grieco , N.A. Grieser , A.A. Grillo , K. Grimm , S. Grinstein , J.-F. Grivaz ,S. Groh , E. Gross , J. Grosse-Knetter , Z.J. Grout , C. Grud , A. Grummer , J.C. Grundy ,L. Guan , W. Guan , C. Gubbels , J. Guenther , A. Guerguichon , J.G.R. Guerrero Rojas ,F. Guescini , D. Guest , R. Gugel , A. Guida , T. Guillemin , S. Guindon , U. Gul , J. Guo ,W. Guo , Y. Guo , Z. Guo , R. Gupta , S. Gurbuz , G. Gustavino , M. Guth , P. Gutierrez ,C. Gutschow , C. Guyot , C. Gwenlan , C.B. Gwilliam , E.S. Haaland , A. Haas , C. Haber ,H.K. Hadavand , A. Hadef , M. Haleem , J. Haley , J.J. Hall , G. Halladjian , G.D. Hallewell ,K. Hamano , H. Hamdaoui , M. Hamer , G.N. Hamity , K. Han , L. Han , S. Han ,Y.F. Han , K. Hanagaki , M. Hance , D.M. Handl , M.D. Hank , R. Hankache , E. Hansen ,J.B. Hansen , J.D. Hansen , M.C. Hansen , P.H. Hansen , E.C. Hanson , K. Hara ,T. Harenberg , S. Harkusha , P.F. Harrison , N.M. Hartman , N.M. Hartmann , Y. Hasegawa ,A. Hasib , S. Hassani , S. Haug , R. Hauser , L.B. Havener , M. Havranek , C.M. Hawkes ,R.J. Hawkings , S. Hayashida , D. Hayden , C. Hayes , R.L. Hayes , C.P. Hays , J.M. Hays ,H.S. Hayward , S.J. Haywood , F. He , Y. He , M.P. Heath , V. Hedberg , S. Heer ,A.L. Heggelund , C. Heidegger , K.K. Heidegger , W.D. Heidorn , J. Heilman , S. Heim ,T. Heim , B. Heinemann , J.J. Heinrich , L. Heinrich , J. Hejbal , L. Helary , A. Held ,S. Hellesund , C.M. Helling , S. Hellman , C. Helsens , R.C.W. Henderson , Y. Heng ,L. Henkelmann , A.M. Henriques Correia , H. Herde , Y. Hernández Jiménez , H. Herr ,M.G. Herrmann , T. Herrmann , G. Herten , R. Hertenberger , L. Hervas , T.C. Herwig ,G.G. Hesketh , N.P. Hessey , H. Hibi , A. Higashida , S. Higashino , E. Higón-Rodriguez ,K. Hildebrand , J.C. Hill , K.K. Hill , K.H. Hiller , S.J. Hillier , M. Hils , I. Hinchliffe ,F. Hinterkeuser , M. Hirose , S. Hirose , D. Hirschbuehl , B. Hiti , O. Hladik , D.R. Hlaluku ,J. Hobbs , N. Hod , M.C. Hodgkinson , A. Hoecker , D. Hohn , D. Hohov , T. Holm ,T.R. Holmes , M. Holzbock , L.B.A.H. Hommels , T.M. Hong , J.C. Honig , A. Hönle ,B.H. Hooberman , W.H. Hopkins , Y. Horii , P. Horn , L.A. Horyn , S. Hou , A. Hoummada ,J. Howarth , J. Hoya , M. Hrabovsky , J. Hrdinka , J. Hrivnac , A. Hrynevich , T. Hryn’ova ,P.J. Hsu , S.-C. Hsu , Q. Hu , S. Hu , Y.F. Hu , D.P. Huang , Y. Huang , Y. Huang ,Z. Hubacek , F. Hubaut , M. Huebner , F. Huegging , T.B. Huffman , M. Huhtinen ,58. Hulsken , R.F.H. Hunter , P. Huo , N. Huseynov , J. Huston , J. Huth , R. Hyneman ,S. Hyrych , G. Iacobucci , G. Iakovidis , I. Ibragimov , L. Iconomidou-Fayard , P. Iengo ,R. Ignazzi , O. Igonkina , R. Iguchi , T. Iizawa , Y. Ikegami , M. Ikeno , N. Ilic ,F. Iltzsche , H. Imam , G. Introzzi , M. Iodice , K. Iordanidou , V. Ippolito ,M.F. Isacson , M. Ishino , W. Islam , C. Issever , S. Istin , F. Ito , J.M. Iturbe Ponce ,R. Iuppa , A. Ivina , H. Iwasaki , J.M. Izen , V. Izzo , P. Jacka , P. Jackson , R.M. Jacobs ,B.P. Jaeger , V. Jain , G. Jäkel , K.B. Jakobi , K. Jakobs , T. Jakoubek , J. Jamieson ,K.W. Janas , R. Jansky , M. Janus , P.A. Janus , G. Jarlskog , A.E. Jaspan , N. Javadov ,T. Javůrek , M. Javurkova , F. Jeanneau , L. Jeanty , J. Jejelava , P. Jenni , N. Jeong ,S. Jézéquel , H. Ji , J. Jia , H. Jiang , Y. Jiang , Z. Jiang , S. Jiggins , F.A. Jimenez Morales ,J. Jimenez Pena , S. Jin , A. Jinaru , O. Jinnouchi , H. Jivan , P. Johansson , K.A. Johns ,C.A. Johnson , R.W.L. Jones , S.D. Jones , T.J. Jones , J. Jongmanns , J. Jovicevic , X. Ju ,J.J. Junggeburth , A. Juste Rozas , A. Kaczmarska , M. Kado , H. Kagan , M. Kagan ,A. Kahn , C. Kahra , T. Kaji , E. Kajomovitz , C.W. Kalderon , A. Kaluza ,A. Kamenshchikov , M. Kaneda , N.J. Kang , S. Kang , Y. Kano , J. Kanzaki , L.S. Kaplan ,D. Kar , K. Karava , M.J. Kareem , I. Karkanias , S.N. Karpov , Z.M. Karpova ,V. Kartvelishvili , A.N. Karyukhin , E. Kasimi , A. Kastanas , C. Kato , J. Katzy ,K. Kawade , K. Kawagoe , T. Kawaguchi , T. Kawamoto , G. Kawamura , E.F. Kay ,S. Kazakos , V.F. Kazanin , R. Keeler , R. Kehoe , J.S. Keller , E. Kellermann ,D. Kelsey , J.J. Kempster , J. Kendrick , K.E. Kennedy , O. Kepka , S. Kersten ,B.P. Kerševan , S. Ketabchi Haghighat , M. Khader , F. Khalil-Zada , M. Khandoga ,A. Khanov , A.G. Kharlamov , T. Kharlamova , E.E. Khoda , A. Khodinov ,T.J. Khoo , G. Khoriauli , E. Khramov , J. Khubua , S. Kido , M. Kiehn , C.R. Kilby ,E. Kim , Y.K. Kim , N. Kimura , A. Kirchhoff , D. Kirchmeier , J. Kirk , A.E. Kiryunin ,T. Kishimoto , D.P. Kisliuk , V. Kitali , C. Kitsaki , O. Kivernyk , T. Klapdor-Kleingrothaus ,M. Klassen , C. Klein , M.H. Klein , M. Klein , U. Klein , K. Kleinknecht , P. Klimek ,A. Klimentov , T. Klingl , T. Klioutchnikova , F.F. Klitzner , P. Kluit , S. Kluth , E. Kneringer ,E.B.F.G. Knoops , A. Knue , D. Kobayashi , M. Kobel , M. Kocian , T. Kodama , P. Kodys ,D.M. Koeck , P.T. Koenig , T. Koffas , N.M. Köhler , M. Kolb , I. Koletsou , T. Komarek ,T. Kondo , K. Köneke , A.X.Y. Kong , A.C. König , T. Kono , V. Konstantinides ,N. Konstantinidis , B. Konya , R. Kopeliansky , S. Koperny , K. Korcyl , K. Kordas ,G. Koren , A. Korn , I. Korolkov , E.V. Korolkova , N. Korotkova , O. Kortner , S. Kortner ,V.V. Kostyukhin , A. Kotsokechagia , A. Kotwal , A. Koulouris ,A. Kourkoumeli-Charalampidi , C. Kourkoumelis , E. Kourlitis , V. Kouskoura , R. Kowalewski ,W. Kozanecki , A.S. Kozhin , V.A. Kramarenko , G. Kramberger , D. Krasnopevtsev ,M.W. Krasny , A. Krasznahorkay , D. Krauss , J.A. Kremer , J. Kretzschmar , P. Krieger ,F. Krieter , A. Krishnan , M. Krivos , K. Krizka , K. Kroeninger , H. Kroha , J. Kroll ,J. Kroll , K.S. Krowpman , U. Kruchonak , H. Krüger , N. Krumnack , M.C. Kruse ,J.A. Krzysiak , A. Kubota , O. Kuchinskaia , S. Kuday , J.T. Kuechler , S. Kuehn , T. Kuhl ,V. Kukhtin , Y. Kulchitsky , S. Kuleshov , Y.P. Kulinich , M. Kuna , T. Kunigo , A. Kupco ,T. Kupfer , O. Kuprash , H. Kurashige , L.L. Kurchaninov , Y.A. Kurochkin , A. Kurova ,M.G. Kurth , E.S. Kuwertz , M. Kuze , A.K. Kvam , J. Kvita , T. Kwan , F. La Ruffa ,C. Lacasta , F. Lacava , D.P.J. Lack , H. Lacker , D. Lacour , E. Ladygin , R. Lafaye ,B. Laforge , T. Lagouri , S. Lai , I.K. Lakomiec , J.E. Lambert , S. Lammers , W. Lampl ,C. Lampoudis , E. Lançon , U. Landgraf , M.P.J. Landon , M.C. Lanfermann , V.S. Lang ,J.C. Lange , R.J. Langenberg , A.J. Lankford , F. Lanni , K. Lantzsch , A. Lanza ,A. Lapertosa , J.F. Laporte , T. Lari , F. Lasagni Manghi , M. Lassnig , T.S. Lau ,59. Laudrain , A. Laurier , M. Lavorgna , S.D. Lawlor , M. Lazzaroni , B. Le ,E. Le Guirriec , A. Lebedev , M. LeBlanc , T. LeCompte , F. Ledroit-Guillon , A.C.A. Lee ,C.A. Lee , G.R. Lee , L. Lee , S.C. Lee , S. Lee , B. Lefebvre , H.P. Lefebvre , M. Lefebvre ,C. Leggett , K. Lehmann , N. Lehmann , G. Lehmann Miotto , W.A. Leight , A. Leisos ,M.A.L. Leite , C.E. Leitgeb , R. Leitner , D. Lellouch , K.J.C. Leney , T. Lenz , S. Leone ,C. Leonidopoulos , A. Leopold , C. Leroy , R. Les , C.G. Lester , M. Levchenko , J. Levêque ,D. Levin , L.J. Levinson , D.J. Lewis , B. Li , B. Li , C-Q. Li , F. Li , H. Li , H. Li ,J. Li , K. Li , L. Li , M. Li , Q. Li , Q.Y. Li , S. Li , X. Li , Y. Li , Z. Li ,Z. Li , Z. Li , Z. Liang , M. Liberatore , B. Liberti , A. Liblong , K. Lie , S. Lim ,C.Y. Lin , K. Lin , R.A. Linck , R.E. Lindley , J.H. Lindon , A. Linss , A.L. Lionti , E. Lipeles ,A. Lipniacka , T.M. Liss , A. Lister , J.D. Little , B. Liu , B.L. Liu , H.B. Liu , J.B. Liu ,J.K.K. Liu , K. Liu , M. Liu , P. Liu , X. Liu , Y. Liu , Y. Liu , Y.L. Liu , Y.W. Liu ,M. Livan , A. Lleres , J. Llorente Merino , S.L. Lloyd , C.Y. Lo , E.M. Lobodzinska ,P. Loch , S. Loffredo , T. Lohse , K. Lohwasser , M. Lokajicek , J.D. Long , R.E. Long ,I. Longarini , L. Longo , K.A. Looper , I. Lopez Paz , A. Lopez Solis , J. Lorenz ,N. Lorenzo Martinez , A.M. Lory , P.J. Lösel , A. Lösle , X. Lou , X. Lou , A. Lounis , J. Love ,P.A. Love , J.J. Lozano Bahilo , M. Lu , Y.J. Lu , H.J. Lubatti , C. Luci , F.L. Lucio Alves ,A. Lucotte , F. Luehring , I. Luise , L. Luminari , B. Lund-Jensen , M.S. Lutz , D. Lynn ,H. Lyons , R. Lysak , E. Lytken , F. Lyu , V. Lyubushkin , T. Lyubushkina , H. Ma , L.L. Ma ,Y. Ma , D.M. Mac Donell , G. Maccarrone , A. Macchiolo , C.M. Macdonald ,J.C. Macdonald , J. Machado Miguens , D. Madaffari , R. Madar , W.F. Mader ,M. Madugoda Ralalage Don , N. Madysa , J. Maeda , T. Maeno , M. Maerker , V. Magerl ,N. Magini , J. Magro , D.J. Mahon , C. Maidantchik , T. Maier , A. Maio ,K. Maj , O. Majersky , S. Majewski , Y. Makida , N. Makovec , B. Malaescu , Pa. Malecki ,V.P. Maleev , F. Malek , D. Malito , U. Mallik , D. Malon , C. Malone , S. Maltezos ,S. Malyukov , J. Mamuzic , G. Mancini , I. Mandić , L. Manhaes de Andrade Filho ,I.M. Maniatis , J. Manjarres Ramos , K.H. Mankinen , A. Mann , A. Manousos , B. Mansoulie ,I. Manthos , S. Manzoni , A. Marantis , G. Marceca , L. Marchese , G. Marchiori ,M. Marcisovsky , L. Marcoccia , C. Marcon , C.A. Marin Tobon , M. Marjanovic ,Z. Marshall , M.U.F. Martensson , S. Marti-Garcia , C.B. Martin , T.A. Martin , V.J. Martin ,B. Martin dit Latour , L. Martinelli , M. Martinez , P. Martinez Agullo ,V.I. Martinez Outschoorn , S. Martin-Haugh , V.S. Martoiu , A.C. Martyniuk , A. Marzin ,S.R. Maschek , L. Masetti , T. Mashimo , R. Mashinistov , J. Masik , A.L. Maslennikov ,L. Massa , P. Massarotti , P. Mastrandrea , A. Mastroberardino , T. Masubuchi ,D. Matakias , A. Matic , N. Matsuzawa , P. Mättig , J. Maurer , B. Maček ,D.A. Maximov , R. Mazini , I. Maznas , S.M. Mazza , J.P. Mc Gowan , S.P. Mc Kee ,T.G. McCarthy , W.P. McCormack , E.F. McDonald , J.A. Mcfayden , G. Mchedlidze ,M.A. McKay , K.D. McLean , S.J. McMahon , P.C. McNamara , C.J. McNicol ,R.A. McPherson , J.E. Mdhluli , Z.A. Meadows , S. Meehan , T. Megy , S. Mehlhase ,A. Mehta , B. Meirose , D. Melini , B.R. Mellado Garcia , J.D. Mellenthin , M. Melo ,F. Meloni , A. Melzer , E.D. Mendes Gouveia , L. Meng , X.T. Meng , S. Menke ,E. Meoni , S. Mergelmeyer , S.A.M. Merkt , C. Merlassino , P. Mermod , L. Merola ,C. Meroni , G. Merz , O. Meshkov , J.K.R. Meshreki , J. Metcalfe , A.S. Mete , C. Meyer ,J-P. Meyer , M. Michetti , R.P. Middleton , L. Mijović , G. Mikenberg , M. Mikestikova ,M. Mikuž , H. Mildner , A. Milic , C.D. Milke , D.W. Miller , A. Milov , D.A. Milstead ,R.A. Mina , A.A. Minaenko , I.A. Minashvili , A.I. Mincer , B. Mindur , M. Mineev ,Y. Minegishi , L.M. Mir , M. Mironova , A. Mirto , K.P. Mistry , T. Mitani ,60. Mitrevski , V.A. Mitsou , M. Mittal , O. Miu , A. Miucci , P.S. Miyagawa , A. Mizukami ,J.U. Mjörnmark , T. Mkrtchyan , M. Mlynarikova , T. Moa , S. Mobius , K. Mochizuki ,P. Mogg , S. Mohapatra , R. Moles-Valls , K. Mönig , E. Monnier , A. Montalbano ,J. Montejo Berlingen , M. Montella , F. Monticelli , S. Monzani , N. Morange ,A.L. Moreira De Carvalho , D. Moreno , M. Moreno Llácer , C. Moreno Martinez ,P. Morettini , M. Morgenstern , S. Morgenstern , D. Mori , M. Morii , M. Morinaga ,V. Morisbak , A.K. Morley , G. Mornacchi , A.P. Morris , L. Morvaj , P. Moschovakos ,B. Moser , M. Mosidze , T. Moskalets , J. Moss , E.J.W. Moyse , S. Muanza , J. Mueller ,R.S.P. Mueller , D. Muenstermann , G.A. Mullier , D.P. Mungo , J.L. Munoz Martinez ,F.J. Munoz Sanchez , P. Murin , W.J. Murray , A. Murrone , J.M. Muse , M. Muškinja ,C. Mwewa , A.G. Myagkov , A.A. Myers , G. Myers , J. Myers , M. Myska ,B.P. Nachman , O. Nackenhorst , A.Nag Nag , K. Nagai , K. Nagano , Y. Nagasaka , J.L. Nagle ,E. Nagy , A.M. Nairz , Y. Nakahama , K. Nakamura , T. Nakamura , H. Nanjo ,F. Napolitano , R.F. Naranjo Garcia , R. Narayan , I. Naryshkin , T. Naumann , G. Navarro ,P.Y. Nechaeva , F. Nechansky , T.J. Neep , A. Negri , M. Negrini , C. Nellist , C. Nelson ,M.E. Nelson , S. Nemecek , M. Nessi , M.S. Neubauer , F. Neuhaus , M. Neumann ,R. Newhouse , P.R. Newman , C.W. Ng , Y.S. Ng , Y.W.Y. Ng , B. Ngair , H.D.N. Nguyen ,T. Nguyen Manh , E. Nibigira , R.B. Nickerson , R. Nicolaidou , D.S. Nielsen , J. Nielsen ,M. Niemeyer , N. Nikiforou , V. Nikolaenko , I. Nikolic-Audit , K. Nikolopoulos , P. Nilsson ,H.R. Nindhito , Y. Ninomiya , A. Nisati , N. Nishu , R. Nisius , I. Nitsche , T. Nitta ,T. Nobe , D.L. Noel , Y. Noguchi , I. Nomidis , M.A. Nomura , M. Nordberg , J. Novak ,T. Novak , O. Novgorodova , R. Novotny , L. Nozka , K. Ntekas , E. Nurse , F.G. Oakham ,H. Oberlack , J. Ocariz , A. Ochi , I. Ochoa , J.P. Ochoa-Ricoux , K. O’Connor , S. Oda ,S. Odaka , S. Oerdek , A. Ogrodnik , A. Oh , C.C. Ohm , H. Oide , M.L. Ojeda ,H. Okawa , Y. Okazaki , M.W. O’Keefe , Y. Okumura , T. Okuyama , A. Olariu ,L.F. Oleiro Seabra , S.A. Olivares Pino , D. Oliveira Damazio , J.L. Oliver , M.J.R. Olsson ,A. Olszewski , J. Olszowska , Ö.O. Öncel , D.C. O’Neil , A.P. O’neill , A. Onofre ,P.U.E. Onyisi , H. Oppen , R.G. Oreamuno Madriz , M.J. Oreglia , G.E. Orellana ,D. Orestano , N. Orlando , R.S. Orr , V. O’Shea , R. Ospanov , G. Otero y Garzon ,H. Otono , P.S. Ott , G.J. Ottino , M. Ouchrif , J. Ouellette , F. Ould-Saada , A. Ouraou ,Q. Ouyang , M. Owen , R.E. Owen , V.E. Ozcan , N. Ozturk , J. Pacalt , H.A. Pacey ,K. Pachal , A. Pacheco Pages , C. Padilla Aranda , S. Pagan Griso , G. Palacino , S. Palazzo ,S. Palestini , M. Palka , P. Palni , C.E. Pandini , J.G. Panduro Vazquez , P. Pani , G. Panizzo ,L. Paolozzi , C. Papadatos , K. Papageorgiou , S. Parajuli , A. Paramonov , C. Paraskevopoulos ,D. Paredes Hernandez , S.R. Paredes Saenz , B. Parida , T.H. Park , A.J. Parker , M.A. Parker ,F. Parodi , E.W. Parrish , J.A. Parsons , U. Parzefall , L. Pascual Dominguez , V.R. Pascuzzi ,J.M.P. Pasner , F. Pasquali , E. Pasqualucci , S. Passaggio , F. Pastore , P. Pasuwan ,S. Pataraia , J.R. Pater , A. Pathak , J. Patton , T. Pauly , J. Pearkes , B. Pearson ,M. Pedersen , L. Pedraza Diaz , R. Pedro , T. Peiffer , S.V. Peleganchuk , O. Penc ,H. Peng , B.S. Peralva , M.M. Perego , A.P. Pereira Peixoto , L. Pereira Sanchez ,D.V. Perepelitsa , E. Perez Codina , F. Peri , L. Perini , H. Pernegger , S. Perrella ,A. Perrevoort , K. Peters , R.F.Y. Peters , B.A. Petersen , T.C. Petersen , E. Petit , V. Petousis ,A. Petridis , C. Petridou , P. Petroff , F. Petrucci , M. Pettee , N.E. Pettersson ,K. Petukhova , A. Peyaud , R. Pezoa , L. Pezzotti , T. Pham , F.H. Phillips ,P.W. Phillips , M.W. Phipps , G. Piacquadio , E. Pianori , A. Picazio , R.H. Pickles ,R. Piegaia , D. Pietreanu , J.E. Pilcher , A.D. Pilkington , M. Pinamonti , J.L. Pinfold ,C. Pitman Donaldson , M. Pitt , L. Pizzimento , A. Pizzini , M.-A. Pleier , V. Plesanovs ,61. Pleskot , E. Plotnikova , P. Podberezko , R. Poettgen , R. Poggi , L. Poggioli ,I. Pogrebnyak , D. Pohl , I. Pokharel , G. Polesello , A. Poley , A. Policicchio ,R. Polifka , A. Polini , C.S. Pollard , V. Polychronakos , D. Ponomarenko , L. Pontecorvo ,S. Popa , G.A. Popeneciu , L. Portales , D.M. Portillo Quintero , S. Pospisil , K. Potamianos ,I.N. Potrap , C.J. Potter , H. Potti , T. Poulsen , J. Poveda , T.D. Powell , G. Pownall ,M.E. Pozo Astigarraga , P. Pralavorio , S. Prell , D. Price , M. Primavera , M.L. Proffitt ,N. Proklova , K. Prokofiev , F. Prokoshin , S. Protopopescu , J. Proudfoot , M. Przybycien ,D. Pudzha , A. Puri , P. Puzo , D. Pyatiizbyantseva , J. Qian , Y. Qin , A. Quadt ,M. Queitsch-Maitland , M. Racko , F. Ragusa , G. Rahal , J.A. Raine , S. Rajagopalan ,A. Ramirez Morales , K. Ran , D.M. Rauch , F. Rauscher , S. Rave , B. Ravina ,I. Ravinovich , J.H. Rawling , M. Raymond , A.L. Read , N.P. Readioff , M. Reale ,D.M. Rebuzzi , G. Redlinger , K. Reeves , J. Reichert , D. Reikher , A. Reiss , A. Rej ,C. Rembser , A. Renardi , M. Renda , M.B. Rendel , A.G. Rennie , S. Resconi ,E.D. Resseguie , S. Rettie , B. Reynolds , E. Reynolds , O.L. Rezanova , P. Reznicek ,E. Ricci , R. Richter , S. Richter , E. Richter-Was , M. Ridel , P. Rieck , O. Rifki ,M. Rijssenbeek , A. Rimoldi , M. Rimoldi , L. Rinaldi , T.T. Rinn , G. Ripellino , I. Riu ,P. Rivadeneira , J.C. Rivera Vergara , F. Rizatdinova , E. Rizvi , C. Rizzi , S.H. Robertson ,M. Robin , D. Robinson , C.M. Robles Gajardo , M. Robles Manzano , A. Robson ,A. Rocchi , E. Rocco , C. Roda , S. Rodriguez Bosca , A.M. Rodríguez Vera , S. Roe ,J. Roggel , O. Røhne , R. Röhrig , R.A. Rojas , B. Roland , C.P.A. Roland , J. Roloff ,A. Romaniouk , M. Romano , N. Rompotis , M. Ronzani , L. Roos , S. Rosati , G. Rosin ,B.J. Rosser , E. Rossi , E. Rossi , E. Rossi , L.P. Rossi , L. Rossini , R. Rosten ,M. Rotaru , B. Rottler , D. Rousseau , G. Rovelli , A. Roy , D. Roy , A. Rozanov ,Y. Rozen , X. Ruan , T.A. Ruggeri , F. Rühr , A. Ruiz-Martinez , A. Rummler , Z. Rurikova ,N.A. Rusakovich , H.L. Russell , L. Rustige , J.P. Rutherfoord , E.M. Rüttinger , M. Rybar ,G. Rybkin , E.B. Rye , A. Ryzhov , J.A. Sabater Iglesias , P. Sabatini , L. Sabetta ,S. Sacerdoti , H.F-W. Sadrozinski , R. Sadykov , F. Safai Tehrani , B. Safarzadeh Samani ,M. Safdari , P. Saha , S. Saha , M. Sahinsoy , A. Sahu , M. Saimpert , M. Saito , T. Saito ,H. Sakamoto , D. Salamani , G. Salamanna , A. Salnikov , J. Salt , A. Salvador Salas ,D. Salvatore , F. Salvatore , A. Salvucci , A. Salzburger , J. Samarati , D. Sammel ,D. Sampsonidis , D. Sampsonidou , J. Sánchez , A. Sanchez Pineda , H. Sandaker ,C.O. Sander , I.G. Sanderswood , M. Sandhoff , C. Sandoval , D.P.C. Sankey , M. Sannino ,Y. Sano , A. Sansoni , C. Santoni , H. Santos , S.N. Santpur , A. Santra , K.A. Saoucha ,A. Sapronov , J.G. Saraiva , O. Sasaki , K. Sato , F. Sauerburger , E. Sauvan , P. Savard ,R. Sawada , C. Sawyer , L. Sawyer , I. Sayago Galvan , C. Sbarra , A. Sbrizzi ,T. Scanlon , J. Schaarschmidt , P. Schacht , D. Schaefer , L. Schaefer , S. Schaepe , U. Schäfer ,A.C. Schaffer , D. Schaile , R.D. Schamberger , E. Schanet , C. Scharf , N. Scharmberg ,V.A. Schegelsky , D. Scheirich , F. Schenck , M. Schernau , C. Schiavi , L.K. Schildgen ,Z.M. Schillaci , E.J. Schioppa , M. Schioppa , K.E. Schleicher , S. Schlenker ,K.R. Schmidt-Sommerfeld , K. Schmieden , C. Schmitt , S. Schmitt , J.C. Schmoeckel ,L. Schoeffel , A. Schoening , P.G. Scholer , E. Schopf , M. Schott , J.F.P. Schouwenberg ,J. Schovancova , S. Schramm , F. Schroeder , A. Schulte , H-C. Schultz-Coulon ,M. Schumacher , B.A. Schumm , Ph. Schune , A. Schwartzman , T.A. Schwarz ,Ph. Schwemling , R. Schwienhorst , A. Sciandra , G. Sciolla , M. Scornajenghi , F. Scuri ,F. Scutti , L.M. Scyboz , C.D. Sebastiani , P. Seema , S.C. Seidel , A. Seiden , B.D. Seidlitz ,T. Seiss , C. Seitz , J.M. Seixas , G. Sekhniaidze , S.J. Sekula , N. Semprini-Cesari , S. Sen ,C. Serfon , L. Serin , L. Serkin , M. Sessa , H. Severini , S. Sevova , F. Sforza ,62. Sfyrla , E. Shabalina , J.D. Shahinian , N.W. Shaikh , D. Shaked Renous , L.Y. Shan ,M. Shapiro , A. Sharma , A.S. Sharma , P.B. Shatalov , K. Shaw , S.M. Shaw , M. Shehade ,Y. Shen , A.D. Sherman , P. Sherwood , L. Shi , S. Shimizu , C.O. Shimmin , Y. Shimogama ,M. Shimojima , I.P.J. Shipsey , S. Shirabe , M. Shiyakova , J. Shlomi , A. Shmeleva ,M.J. Shochet , J. Shojaii , D.R. Shope , S. Shrestha , E.M. Shrif , E. Shulga , P. Sicho ,A.M. Sickles , E. Sideras Haddad , O. Sidiropoulou , A. Sidoti , F. Siegert , Dj. Sijacki ,M.Jr. Silva , M.V. Silva Oliveira , S.B. Silverstein , S. Simion , R. Simoniello ,C.J. Simpson-allsop , S. Simsek , P. Sinervo , V. Sinetckii , S. Singh , M. Sioli , I. Siral ,S.Yu. Sivoklokov , J. Sjölin , A. Skaf , E. Skorda , P. Skubic , M. Slawinska , K. Sliwa ,R. Slovak , V. Smakhtin , B.H. Smart , J. Smiesko , N. Smirnov , S.Yu. Smirnov ,Y. Smirnov , L.N. Smirnova , O. Smirnova , E.A. Smith , H.A. Smith , M. Smizanska ,K. Smolek , A. Smykiewicz , A.A. Snesarev , H.L. Snoek , I.M. Snyder , S. Snyder ,R. Sobie , A. Soffer , A. Søgaard , F. Sohns , C.A. Solans Sanchez , E.Yu. Soldatov ,U. Soldevila , A.A. Solodkov , A. Soloshenko , O.V. Solovyanov , V. Solovyev , P. Sommer ,H. Son , W. Song , W.Y. Song , A. Sopczak , A.L. Sopio , F. Sopkova , S. Sottocornola ,R. Soualah , A.M. Soukharev , D. South , S. Spagnolo , M. Spalla ,M. Spangenberg , F. Spanò , D. Sperlich , T.M. Spieker , G. Spigo , M. Spina , D.P. Spiteri ,M. Spousta , A. Stabile , B.L. Stamas , R. Stamen , M. Stamenkovic , E. Stanecka ,B. Stanislaus , M.M. Stanitzki , M. Stankaityte , B. Stapf , E.A. Starchenko , G.H. Stark ,J. Stark , P. Staroba , P. Starovoitov , S. Stärz , R. Staszewski , G. Stavropoulos , M. Stegler ,P. Steinberg , A.L. Steinhebel , B. Stelzer , H.J. Stelzer , O. Stelzer-Chilton , H. Stenzel ,T.J. Stevenson , G.A. Stewart , M.C. Stockton , G. Stoicea , M. Stolarski , S. Stonjek ,A. Straessner , J. Strandberg , S. Strandberg , M. Strauss , T. Strebler , P. Strizenec ,R. Ströhmer , D.M. Strom , R. Stroynowski , A. Strubig , S.A. Stucci , B. Stugu , J. Stupak ,N.A. Styles , D. Su , W. Su , X. Su , V.V. Sulin , M.J. Sullivan , D.M.S. Sultan ,S. Sultansoy , T. Sumida , S. Sun , X. Sun , K. Suruliz , C.J.E. Suster , M.R. Sutton ,S. Suzuki , M. Svatos , M. Swiatlowski , S.P. Swift , T. Swirski , A. Sydorenko , I. Sykora ,M. Sykora , T. Sykora , D. Ta , K. Tackmann , J. Taenzer , A. Taffard , R. Tafirout ,E. Tagiev , R. Takashima , K. Takeda , T. Takeshita , E.P. Takeva , Y. Takubo , M. Talby ,A.A. Talyshev , K.C. Tam , N.M. Tamir , J. Tanaka , R. Tanaka , S. Tapia Araya ,S. Tapprogge , A. Tarek Abouelfadl Mohamed , S. Tarem , K. Tariq , G. Tarna ,G.F. Tartarelli , P. Tas , M. Tasevsky , T. Tashiro , E. Tassi , A. Tavares Delgado ,Y. Tayalati , A.J. Taylor , G.N. Taylor , W. Taylor , H. Teagle , A.S. Tee ,R. Teixeira De Lima , P. Teixeira-Dias , H. Ten Kate , J.J. Teoh , S. Terada , K. Terashi ,J. Terron , S. Terzo , M. Testa , R.J. Teuscher , S.J. Thais , N. Themistokleous ,T. Theveneaux-Pelzer , F. Thiele , D.W. Thomas , J.O. Thomas , J.P. Thomas , E.A. Thompson ,P.D. Thompson , E. Thomson , E.J. Thorpe , R.E. Ticse Torres , V.O. Tikhomirov ,Yu.A. Tikhonov , S. Timoshenko , P. Tipton , S. Tisserant , K. Todome ,S. Todorova-Nova , S. Todt , J. Tojo , S. Tokár , K. Tokushuku , E. Tolley , R. Tombs ,K.G. Tomiwa , M. Tomoto , L. Tompkins , P. Tornambe , E. Torrence , H. Torres ,E. Torró Pastor , C. Tosciri , J. Toth , D.R. Tovey , A. Traeet , C.J. Treado , T. Trefzger ,F. Tresoldi , A. Tricoli , I.M. Trigger , S. Trincaz-Duvoid , D.A. Trischuk , W. Trischuk ,B. Trocmé , A. Trofymov , C. Troncon , F. Trovato , L. Truong , M. Trzebinski , A. Trzupek ,F. Tsai , J.C-L. Tseng , P.V. Tsiareshka , A. Tsirigotis , V. Tsiskaridze , E.G. Tskhadadze ,M. Tsopoulou , I.I. Tsukerman , V. Tsulaia , S. Tsuno , D. Tsybychev , Y. Tu , A. Tudorache ,V. Tudorache , T.T. Tulbure , A.N. Tuna , S. Turchikhin , D. Turgeman , I. Turk Cakir ,R.J. Turner , R. Turra , P.M. Tuts , S. Tzamarias , E. Tzovara , K. Uchida , F. Ukegawa ,63. Unal , M. Unal , A. Undrus , G. Unel , F.C. Ungaro , Y. Unno , K. Uno , J. Urban ,P. Urquijo , G. Usai , Z. Uysal , V. Vacek , B. Vachon , K.O.H. Vadla , T. Vafeiadis ,A. Vaidya , C. Valderanis , E. Valdes Santurio , M. Valente , S. Valentinetti , A. Valero ,L. Valéry , R.A. Vallance , A. Vallier , J.A. Valls Ferrer , T.R. Van Daalen , P. Van Gemmeren ,S. Van Stroud , I. Van Vulpen , M. Vanadia , W. Vandelli , M. Vandenbroucke ,E.R. Vandewall , A. Vaniachine , D. Vannicola , R. Vari , E.W. Varnes , C. Varni ,T. Varol , D. Varouchas , K.E. Varvell , M.E. Vasile , G.A. Vasquez , F. Vazeille ,D. Vazquez Furelos , T. Vazquez Schroeder , J. Veatch , V. Vecchio , M.J. Veen , L.M. Veloce ,F. Veloso , S. Veneziano , A. Ventura , A. Verbytskyi , V. Vercesi , M. Verducci ,C.M. Vergel Infante , C. Vergis , W. Verkerke , A.T. Vermeulen , J.C. Vermeulen , C. Vernieri ,P.J. Verschuuren , M.C. Vetterli , N. Viaux Maira , T. Vickey , O.E. Vickey Boeriu ,G.H.A. Viehhauser , L. Vigani , M. Villa , M. Villaplana Perez , E.M. Villhauer , E. Vilucchi ,M.G. Vincter , G.S. Virdee , A. Vishwakarma , C. Vittori , I. Vivarelli , M. Vogel ,P. Vokac , S.E. von Buddenbrock , E. Von Toerne , V. Vorobel , K. Vorobev , M. Vos ,J.H. Vossebeld , M. Vozak , N. Vranjes , M. Vranjes Milosavljevic , V. Vrba , M. Vreeswijk ,N.K. Vu , R. Vuillermet , I. Vukotic , S. Wada , P. Wagner , W. Wagner , J. Wagner-Kuhr ,S. Wahdan , H. Wahlberg , R. Wakasa , V.M. Walbrecht , J. Walder , R. Walker ,S.D. Walker , W. Walkowiak , V. Wallangen , A.M. Wang , A.Z. Wang , C. Wang ,C. Wang , F. Wang , H. Wang , H. Wang , J. Wang , P. Wang , Q. Wang , R.-J. Wang ,R. Wang , R. Wang , S.M. Wang , W.T. Wang , W. Wang , W.X. Wang , Y. Wang ,Z. Wang , C. Wanotayaroj , A. Warburton , C.P. Ward , D.R. Wardrope , N. Warrack ,A.T. Watson , M.F. Watson , G. Watts , B.M. Waugh , A.F. Webb , C. Weber , M.S. Weber ,S.A. Weber , S.M. Weber , A.R. Weidberg , J. Weingarten , M. Weirich , C. Weiser ,P.S. Wells , T. Wenaus , B. Wendland , T. Wengler , S. Wenig , N. Wermes , M. Wessels ,T.D. Weston , K. Whalen , A.M. Wharton , A.S. White , A. White , M.J. White , D. Whiteson ,B.W. Whitmore , W. Wiedenmann , C. Wiel , M. Wielers , N. Wieseotte , C. Wiglesworth ,L.A.M. Wiik-Fuchs , H.G. Wilkens , L.J. Wilkins , H.H. Williams , S. Williams , S. Willocq ,P.J. Windischhofer , I. Wingerter-Seez , E. Winkels , F. Winklmeier , B.T. Winter , M. Wittgen ,M. Wobisch , A. Wolf , R. Wölker , J. Wollrath , M.W. Wolter , H. Wolters ,V.W.S. Wong , N.L. Woods , S.D. Worm , B.K. Wosiek , K.W. Woźniak , K. Wraight ,S.L. Wu , X. Wu , Y. Wu , J. Wuerzinger , T.R. Wyatt , B.M. Wynne , S. Xella , L. Xia ,J. Xiang , X. Xiao , X. Xie , I. Xiotidis , D. Xu , H. Xu , H. Xu , L. Xu , T. Xu ,W. Xu , Z. Xu , Z. Xu , B. Yabsley , S. Yacoob , K. Yajima , D.P. Yallup , N. Yamaguchi ,Y. Yamaguchi , A. Yamamoto , M. Yamatani , T. Yamazaki , Y. Yamazaki , J. Yan , Z. Yan ,H.J. Yang , H.T. Yang , S. Yang , T. Yang , X. Yang , Y. Yang , Z. Yang , W-M. Yao ,Y.C. Yap , Y. Yasu , E. Yatsenko , H. Ye , J. Ye , S. Ye , I. Yeletskikh , M.R. Yexley ,E. Yigitbasi , P. Yin , K. Yorita , K. Yoshihara , C.J.S. Young , C. Young , J. Yu , R. Yuan ,X. Yue , M. Zaazoua , B. Zabinski , G. Zacharis , E. Zaffaroni , J. Zahreddine ,A.M. Zaitsev , T. Zakareishvili , N. Zakharchuk , S. Zambito , D. Zanzi , D.R. Zaripovas ,S.V. Zeißner , C. Zeitnitz , G. Zemaityte , J.C. Zeng , O. Zenin , T. Ženiš , D. Zerwas ,M. Zgubič , B. Zhang , D.F. Zhang , G. Zhang , J. Zhang , Kaili. Zhang , L. Zhang ,L. Zhang , M. Zhang , R. Zhang , S. Zhang , X. Zhang , X. Zhang , Y. Zhang ,Z. Zhang , Z. Zhang , P. Zhao , Z. Zhao , A. Zhemchugov , Z. Zheng , D. Zhong , B. Zhou ,C. Zhou , H. Zhou , M.S. Zhou , M. Zhou , N. Zhou , Y. Zhou , C.G. Zhu , C. Zhu ,H.L. Zhu , H. Zhu , J. Zhu , Y. Zhu , X. Zhuang , K. Zhukov , V. Zhulanov ,D. Zieminska , N.I. Zimine , S. Zimmermann , Z. Zinonos , M. Ziolkowski , L. Živković ,G. Zobernig , A. Zoccoli , K. Zoch , T.G. Zorbas , R. Zou , L. Zwalinski .64 Department of Physics, University of Adelaide, Adelaide; Australia. Physics Department, SUNY Albany, Albany NY; United States of America. Department of Physics, University of Alberta, Edmonton AB; Canada. ( a ) Department of Physics, Ankara University, Ankara; ( b ) Istanbul Aydin University, Application andResearch Center for Advanced Studies, Istanbul; ( c ) Division of Physics, TOBB University of Economicsand Technology, Ankara; Turkey. LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France. High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America. Department of Physics, University of Arizona, Tucson AZ; United States of America. Department of Physics, University of Texas at Arlington, Arlington TX; United States of America. Physics Department, National and Kapodistrian University of Athens, Athens; Greece. Physics Department, National Technical University of Athens, Zografou; Greece. Department of Physics, University of Texas at Austin, Austin TX; United States of America. ( a ) Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; ( b ) Istanbul BilgiUniversity, Faculty of Engineering and Natural Sciences, Istanbul; ( c ) Department of Physics, BogaziciUniversity, Istanbul; ( d ) Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey. Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan. Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona;Spain. ( a ) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; ( b ) Physics Department,Tsinghua University, Beijing; ( c ) Department of Physics, Nanjing University, Nanjing; ( d ) University ofChinese Academy of Science (UCAS), Beijing; China. Institute of Physics, University of Belgrade, Belgrade; Serbia. Department for Physics and Technology, University of Bergen, Bergen; Norway. Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA;United States of America. Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany. Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University ofBern, Bern; Switzerland. School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom. ( a ) Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño,Bogotá; ( b ) Departamento de Física, Universidad Nacional de Colombia, Bogotá, Colombia; Colombia. ( a ) INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica; ( b ) INFN Sezione di Bologna; Italy. Physikalisches Institut, Universität Bonn, Bonn; Germany. Department of Physics, Boston University, Boston MA; United States of America. Department of Physics, Brandeis University, Waltham MA; United States of America. ( a ) Transilvania University of Brasov, Brasov; ( b ) Horia Hulubei National Institute of Physics and NuclearEngineering, Bucharest; ( c ) Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; ( d ) NationalInstitute for Research and Development of Isotopic and Molecular Technologies, Physics Department,Cluj-Napoca; ( e ) University Politehnica Bucharest, Bucharest; ( f ) West University in Timisoara, Timisoara;Romania. ( a ) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; ( b ) Department ofSubnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; SlovakRepublic. Physics Department, Brookhaven National Laboratory, Upton NY; United States of America. Departamento de Física, Universidad de Buenos Aires, Buenos Aires; Argentina. California State University, CA; United States of America.65 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom. ( a ) Department of Physics, University of Cape Town, Cape Town; ( b ) iThemba Labs, WesternCape; ( c ) Department of Mechanical Engineering Science, University of Johannesburg,Johannesburg; ( d ) University of South Africa, Department of Physics, Pretoria; ( e ) School of Physics,University of the Witwatersrand, Johannesburg; South Africa. Department of Physics, Carleton University, Ottawa ON; Canada. ( a ) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - UniversitéHassan II, Casablanca; ( b ) Faculté des Sciences, Université Ibn-Tofail, Kénitra; ( c ) Faculté des SciencesSemlalia, Université Cadi Ayyad, LPHEA-Marrakech; ( d ) Faculté des Sciences, Université MohamedPremier and LPTPM, Oujda; ( e ) Faculté des sciences, Université Mohammed V, Rabat; Morocco. CERN, Geneva; Switzerland. Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America. LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France. Nevis Laboratory, Columbia University, Irvington NY; United States of America. Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark. ( a ) Dipartimento di Fisica, Università della Calabria, Rende; ( b ) INFN Gruppo Collegato di Cosenza,Laboratori Nazionali di Frascati; Italy. Physics Department, Southern Methodist University, Dallas TX; United States of America. Physics Department, University of Texas at Dallas, Richardson TX; United States of America. National Centre for Scientific Research "Demokritos", Agia Paraskevi; Greece. ( a ) Department of Physics, Stockholm University; ( b ) Oskar Klein Centre, Stockholm; Sweden. Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany. Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund; Germany. Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany. Department of Physics, Duke University, Durham NC; United States of America. SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom. INFN e Laboratori Nazionali di Frascati, Frascati; Italy. Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany. II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany. Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland. ( a ) Dipartimento di Fisica, Università di Genova, Genova; ( b ) INFN Sezione di Genova; Italy. II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany. SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom. LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France. Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States ofAmerica. ( a ) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics,University of Science and Technology of China, Hefei; ( b ) Institute of Frontier and Interdisciplinary Scienceand Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University,Qingdao; ( c ) School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC,Shanghai; ( d ) Tsung-Dao Lee Institute, Shanghai; China. ( a ) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; ( b ) PhysikalischesInstitut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany. Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan. ( a ) Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; ( b ) Department ofPhysics, University of Hong Kong, Hong Kong; ( c ) Department of Physics and Institute for Advanced Study,Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China.66 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan. IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France. Department of Physics, Indiana University, Bloomington IN; United States of America. ( a ) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; ( b ) ICTP, Trieste; ( c ) DipartimentoPolitecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy. ( a ) INFN Sezione di Lecce; ( b ) Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy. ( a ) INFN Sezione di Milano; ( b ) Dipartimento di Fisica, Università di Milano, Milano; Italy. ( a ) INFN Sezione di Napoli; ( b ) Dipartimento di Fisica, Università di Napoli, Napoli; Italy. ( a ) INFN Sezione di Pavia; ( b ) Dipartimento di Fisica, Università di Pavia, Pavia; Italy. ( a ) INFN Sezione di Pisa; ( b ) Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy. ( a ) INFN Sezione di Roma; ( b ) Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy. ( a ) INFN Sezione di Roma Tor Vergata; ( b ) Dipartimento di Fisica, Università di Roma Tor Vergata, Roma;Italy. ( a ) INFN Sezione di Roma Tre; ( b ) Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy. ( a ) INFN-TIFPA; ( b ) Università degli Studi di Trento, Trento; Italy. Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria. University of Iowa, Iowa City IA; United States of America. Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America. Joint Institute for Nuclear Research, Dubna; Russia. ( a ) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz deFora; ( b ) Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; ( c ) Universidade Federal deSão João del Rei (UFSJ), São João del Rei; ( d ) Instituto de Física, Universidade de São Paulo, São Paulo;Brazil. KEK, High Energy Accelerator Research Organization, Tsukuba; Japan. Graduate School of Science, Kobe University, Kobe; Japan. ( a ) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science,Krakow; ( b ) Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland. Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland. Faculty of Science, Kyoto University, Kyoto; Japan. Kyoto University of Education, Kyoto; Japan. Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka ;Japan. Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina. Physics Department, Lancaster University, Lancaster; United Kingdom. Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom. Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics,University of Ljubljana, Ljubljana; Slovenia. School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom. Department of Physics, Royal Holloway University of London, Egham; United Kingdom. Department of Physics and Astronomy, University College London, London; United Kingdom. Louisiana Tech University, Ruston LA; United States of America. Fysiska institutionen, Lunds universitet, Lund; Sweden. Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3),Villeurbanne; France. Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain.
Institut für Physik, Universität Mainz, Mainz; Germany.
School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom.67 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France.
Department of Physics, University of Massachusetts, Amherst MA; United States of America.
Department of Physics, McGill University, Montreal QC; Canada.
School of Physics, University of Melbourne, Victoria; Australia.
Department of Physics, University of Michigan, Ann Arbor MI; United States of America.
Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States ofAmerica.
B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus.
Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus.
Group of Particle Physics, University of Montreal, Montreal QC; Canada.
P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia.
National Research Nuclear University MEPhI, Moscow; Russia.
D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow;Russia.
Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany.
Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany.
Nagasaki Institute of Applied Science, Nagasaki; Japan.
Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan.
Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States ofAmerica.
Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef,Nijmegen; Netherlands.
Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam;Netherlands.
Department of Physics, Northern Illinois University, DeKalb IL; United States of America. ( a ) Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk; ( b ) Novosibirsk State UniversityNovosibirsk; Russia.
Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia.
Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National ResearchCentre "Kurchatov Institute", Moscow; Russia.
Department of Physics, New York University, New York NY; United States of America.
Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan.
Ohio State University, Columbus OH; United States of America.
Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; UnitedStates of America.
Department of Physics, Oklahoma State University, Stillwater OK; United States of America.
Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic.
Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America.
Graduate School of Science, Osaka University, Osaka; Japan.
Department of Physics, University of Oslo, Oslo; Norway.
Department of Physics, Oxford University, Oxford; United Kingdom.
LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris; France.
Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America.
Konstantinov Nuclear Physics Institute of National Research Centre "Kurchatov Institute", PNPI, St.Petersburg; Russia.
Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States ofAmerica. 68 ( a ) Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa; ( b ) Departamento deFísica, Faculdade de Ciências, Universidade de Lisboa, Lisboa; ( c ) Departamento de Física, Universidade deCoimbra, Coimbra; ( d ) Centro de Física Nuclear da Universidade de Lisboa, Lisboa; ( e ) Departamento deFísica, Universidade do Minho, Braga; ( f ) Departamento de Física Teórica y del Cosmos, Universidad deGranada, Granada (Spain); ( g ) Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia,Universidade Nova de Lisboa, Caparica; ( h ) Instituto Superior Técnico, Universidade de Lisboa, Lisboa;Portugal.
Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic.
Czech Technical University in Prague, Prague; Czech Republic.
Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic.
Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom.
IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France.
Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; UnitedStates of America. ( a ) Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; ( b ) Universidad AndresBello, Department of Physics, Santiago; ( c ) Instituto de Alta Investigación, Universidad deTarapacá; ( d ) Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile.
Department of Physics, University of Washington, Seattle WA; United States of America.
Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom.
Department of Physics, Shinshu University, Nagano; Japan.
Department Physik, Universität Siegen, Siegen; Germany.
Department of Physics, Simon Fraser University, Burnaby BC; Canada.
SLAC National Accelerator Laboratory, Stanford CA; United States of America.
Physics Department, Royal Institute of Technology, Stockholm; Sweden.
Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States ofAmerica.
Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom.
School of Physics, University of Sydney, Sydney; Australia.
Institute of Physics, Academia Sinica, Taipei; Taiwan. ( a ) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; ( b ) HighEnergy Physics Institute, Tbilisi State University, Tbilisi; Georgia.
Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel.
Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel.
Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece.
International Center for Elementary Particle Physics and Department of Physics, University of Tokyo,Tokyo; Japan.
Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan.
Department of Physics, Tokyo Institute of Technology, Tokyo; Japan.
Tomsk State University, Tomsk; Russia.
Department of Physics, University of Toronto, Toronto ON; Canada. ( a ) TRIUMF, Vancouver BC; ( b ) Department of Physics and Astronomy, York University, Toronto ON;Canada.
Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and AppliedSciences, University of Tsukuba, Tsukuba; Japan.
Department of Physics and Astronomy, Tufts University, Medford MA; United States of America.
Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States ofAmerica. 69 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden.
Department of Physics, University of Illinois, Urbana IL; United States of America.
Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain.
Department of Physics, University of British Columbia, Vancouver BC; Canada.
Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada.
Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany.
Department of Physics, University of Warwick, Coventry; United Kingdom.
Waseda University, Tokyo; Japan.
Department of Particle Physics, Weizmann Institute of Science, Rehovot; Israel.
Department of Physics, University of Wisconsin, Madison WI; United States of America.
Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische UniversitätWuppertal, Wuppertal; Germany.
Department of Physics, Yale University, New Haven CT; United States of America. a Also at Borough of Manhattan Community College, City University of New York, New York NY; UnitedStates of America. b Also at Centro Studi e Ricerche Enrico Fermi; Italy. c Also at CERN, Geneva; Switzerland. d Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France. e Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève;Switzerland. f Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain. g Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece. h Also at Department of Physics and Astronomy, Michigan State University, East Lansing MI; UnitedStates of America. i Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States ofAmerica. j Also at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel. k Also at Department of Physics, California State University, East Bay; United States of America. l Also at Department of Physics, California State University, Fresno; United States of America. m Also at Department of Physics, California State University, Sacramento; United States of America. n Also at Department of Physics, King’s College London, London; United Kingdom. o Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia. p Also at Department of Physics, University of Fribourg, Fribourg; Switzerland. q Also at Dipartimento di Matematica, Informatica e Fisica, Università di Udine, Udine; Italy. r Also at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russia. s Also at Giresun University, Faculty of Engineering, Giresun; Turkey. t Also at Graduate School of Science, Osaka University, Osaka; Japan. u Also at Hellenic Open University, Patras; Greece. v Also at IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France. w Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain. x Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany. y Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud UniversityNijmegen/Nikhef, Nijmegen; Netherlands. z Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy ofSciences, Sofia; Bulgaria. aa Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest;Hungary. 70 b Also at Institute of Particle Physics (IPP), Vancouver; Canada. ac Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan. ad Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain. ae Also at Joint Institute for Nuclear Research, Dubna; Russia. a f
Also at Louisiana Tech University, Ruston LA; United States of America. ag Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia. ah Also at National Research Nuclear University MEPhI, Moscow; Russia. ai Also at Physics Department, An-Najah National University, Nablus; Palestine. aj Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany. ak Also at The City College of New York, New York NY; United States of America. al Also at TRIUMF, Vancouver BC; Canada. am Also at Universita di Napoli Parthenope, Napoli; Italy. an Also at University of Chinese Academy of Sciences (UCAS), Beijing; China. ∗∗