The jet energy scale, jet energy resolution, and their systematic uncertainties are measured for jets reconstructed with the ATLAS detector in 2012 using proton–proton data produced at a centre-of-mass energy of 8 TeV with an integrated luminosity of
Collimated sprays of energetic hadrons, known as jets, are the dominant final-state objects of high-energy proton–proton (
Calorimeter jets, which are reconstructed from calorimeter energy depositions, are calibrated to the energy scale of jets created with the same jet clustering algorithm from stable interacting particles. This calibration accounts for the following effects:
A first estimate of the jet energy scale (JES) uncertainty of
This paper describes the derivation of the ATLAS jet calibration and jet energy resolution using the full 2012
The outline of the paper is as follows. Section
The ATLAS detector consists of an inner tracking detector, sampling electromagnetic and hadronic calorimeters, and muon chambers in a toroidal magnetic field. A detailed description of the ATLAS detector is in Ref. [
The inner detector (ID) has complete azimuthal coverage and spans the pseudorapidity range of
Jets are reconstructed from energy deposited in the ATLAS calorimeter system. Electromagnetic calorimetry is provided by high-granularity liquid argon (LAr) sampling calorimeters, using lead as an absorber, which are split into barrel (
The muon spectrometer surrounds the ATLAS calorimeter. A system of three large air-core toroids with eight coils each, a barrel and two endcaps, generates a magnetic field in the pseudorapidity range
Events are retained for analysis using a trigger system [
The dataset consists of
The typical electron drift time within the ATLAS LAr calorimeters is 450 ns [
Monte Carlo event generators simulate the type, energy, and direction of particles produced in Summary of the simulated samples used to derive the jet calibration and to assess systematic uncertainties Process Event generator PDF set MPI/shower tune set Dijet & multijet CT10 [ AU2 [ CTEQ6L1 [ EE3 MRST LO** [ CT10 AU2 CT10 AUET2 [ CT10 CT10 AU2 CT10 CTEQ6L1 AU2 CTEQ6L1 UE-EE-3 [ MSTW2008LO [ AM2 [
The baseline simulation samples used to obtain the MC-based jet calibration were produced using
Separate samples produced using other generators were used to derive the final jet calibration and resolution and associated uncertainties using in situ techniques. The
The generated stable particles, defined as those with a lifetime
Jets are reconstructed with the anti-
The generated stable particles used to define truth-particle jets are required to originate (either directly or via a decay chain) from the hard-scatter vertex, and hence do not include particles from
Calorimeter jets are built from clusters of adjacent calorimeter read-out cells that contain a significant energy signal above noise levels, referred to as topological clusters or
The calorimeter jet four-momentum directly after jet finding is referred to as the
For each in situ analysis, jets within the full calorimeter acceptance
Jets with a radius parameter of
To derive a calibration based on MC simulation, it is necessary to match a truth-particle jet to a reconstructed jet. Two methods are used for this: a simple, angular matching as well as a more sophisticated approach known as
The simulated jet energy response is defined by Overview of the ATLAS jet calibration described in this paper. All steps are derived and applied separately for jets built from EM-scale and LCW calibrated calorimeter clusters, except for the global sequential calibration, which is only partially applied to LCW-jets (Sect.
An overview of the ATLAS jet calibration applied to the
The four-momentum of the initial jet is defined according to Eq. (
The reconstruction of the jet kinematics is affected by Jet angular resolution as a function of transverse momentum for anti- Jet energy and mass responses as a function of
After subtracting the
After the origin and Jet energy,
For the large-
A small, additive correction
For small-
The calibration procedure is slightly different for the large-
The global sequential (GS) calibration scheme exploits the topology of the energy deposits in the calorimeter as well as tracking information to characterize fluctuations in the jet particle content of the hadronic shower development. Correcting for such fluctuations can improve the jet energy resolution and reduce response dependence on the so-called “jet flavour”, meaning dependence on the underlying physics process in which the jet was produced. Jets produced in dijet events tend to have more constituent particles, a wider transverse profile and a lower calorimeter energy response than jets with the same
Any variable
Each correction is performed separately in bins of
Several variables can be used sequentially to achieve the optimal resolution. The jet
The GS calibration relies on five jet properties that were identified empirically to have a significant effect on the jet energy response. This empirical study was conducted primarily using EM jets, while a reduced scan was performed for LCW jets given that they already exploit some of the following variables as part of the LCW procedure. Two of the variables characterize the longitudinal shower structure of a jet, namely the fractions of energy deposited in the third electromagnetic calorimeter layer, Normalized distributions of
Figures Normalized distributions of
The jet observables used for the GS calibration and their order of application are summarized in Table
The calorimeter response for EM + JES calibrated anti-
Figure Sequence of GS corrections used to improve the jet performance in each Correction 1 Correction 2 Correction 3 Correction 4 Correction 5 [0, 1.7] [1.7, 2.5] [2.5, 2.7] [[2.7, 3.5]
The improvement observed for jets initially calibrated with the LCW + JES scheme is found to be smaller, which is expected as only tracking and non-contained jet corrections are applied to these jets. For both EM + JES and LCW + JES calibrated jets, improvements to the JER is observed across the full
The conclusions from this section can generally be extended to the whole
Only a small improvement is observed after applying the last GS correction for uncontained calorimeter jets in the inclusive jet sample since only a small fraction of energetic jets are uncontained. Figure Jet Jet Jet Standard deviation over arithmetic mean of the jet energy response as a function of
The internal structure of a jet, and thereby also its calorimeter response, depends on how the jet was produced. Jets produced in dijet events are expected to originate from gluons more often than jets with the same The
The flavour dependence of the response is studied in simulated dijet events by assigning a flavour label to each calorimeter jet using an angular matching to the particles in the MC event record. If the jet matches a
The GS correction is validated in situ with dijet events using the tag-and-probe technique, using the event selection described in Sect. Dijet in situ validation of jet response as a function of
Results for all variables used in the GS calibration are shown in Fig. Jet
Figure
Following the determination and application of MC-based jet calibration factors, it is important to measure the jet response and resolution in situ, quantify the level of agreement between data and simulation, and correct for any discrepancy. The first step is to investigate the jet response dependence across the detector in terms of pseudorapidity. All results presented in this section are obtained with jets calibrated with the calibration chain up to, and including, the GS calibration (Sect.
The jet energy resolution (JER) and the relative response of the calorimeter as a function of pseudorapidity are determined using events with dijet topologies [
Two versions of the analysis are performed. In the
The asymmetry distribution also probes the jet energy resolution. The standard deviation of the asymmetry distribution
The standard deviation of the probe jet
The
The bisector method attempts to separate the desired part of the dijet
Although the bisector observables in Eqs. ( Illustration of observables used in the dijet bisector technique. The
Dijet events are selected using a combination of central (
The in situ techniques rely on assumptions that are only approximately fulfilled, and simulation is used to account for these approximations. For example, the momentum balance between the jet and the reference object is altered to varying degrees by the presence of additional radiation. The impact from such radiation is reduced by event topology selection criteria. Since the choice of the exact threshold values is arbitrary, systematic uncertainties are evaluated by rederiving the final result, which is a data-to-MC ratio, after varying these selection criteria. Other systematic uncertainties are evaluated by altering choices used by the method, such as a parameter used in a fit or changing the MC generator. In the case of the
Many potential effects are considered as systematic uncertainty sources. As explained above, each of these is evaluated by introducing a variation to the analysis. However, due to limited statistics in both the data and MC samples, these variations have an associated statistical uncertainty (i.e. an “uncertainty on the uncertainty”). For example, an introduced variation that has no impact on the measured calibration factor (or resolution) still produces changes consistent with statistical fluctuations. Thus, it is important to only include statistically significant variations as systematic uncertainties. This is achieved with a two-step procedure outlined below.
In the first step, the statistical uncertainty of the systematic variations is evaluated in each
In a second step, adjacent
For some systematic variations, there are physics reasons for the response to depend on
The use of the pseudo-experiments and the bin merging procedure strongly reduces the effect of statistical fluctuations when evaluating systematic uncertainties. This procedure is used for all the in situ methods discussed in this paper.
The following sections detail the determination of the intercalibration aimed at achieving a uniform scale for jets as a function of pseudorapidity.
Figure Relative jet response measured using the matrix and central reference methods for anti-
Figure Relative jet response, 1/ Relative jet response, 1/
The residual calibration factor
All intercalibration systematic uncertainties are derived as a function of
The difference between
The physics modelling uncertainty in the relative response is cross-checked using truth-particle jets by varying the
The event topology selection requires
The total systematic uncertainty is the sum in quadrature of the various components mentioned. Figure Summary of uncertainties in the intercalibration as a function of the jet
Figure
The JER is determined in data by subtracting the truth-particle jet asymmetry from the measured asymmetry as discussed in Sect.
Non-closure is defined as the difference between the jet resolution measured by the in situ method and the truth-particle jet resolution obtained by matching truth-particle and calorimeter jets (Sect.
Finally, there are a number of systematic uncertainties that arise from experimental sources. The uncertainty in the JES calibration is investigated by shifting the energy of the jets by the
The total systematic uncertainty, which is taken as the sum in quadrature of all sources discussed above, is shown as a dashed band around the points in Fig. Relative jet energy resolution obtained for EM + JES calibrated jets using the bisector (filled circles) and dijet balance (filled squares) methods, respectively. The MC simulated resolution derived from matching truth-particle jets with calorimeter jets is presented by the open triangles connected by dashed lines. The error bars reflect the statistical uncertainty while the hashed band indicates the total systematic uncertainty. Results are shown as a function of the jet
This section describes the determination of the final jet calibration that corrects the absolute energy scale of the jets to achieve a data-to-MC agreement within the associated uncertainties. The jet calibration is based on measurements conducted by in situ techniques that exploit the transverse momentum balance between a well-calibrated object and the hadronic recoil (jet). The well-calibrated object is either a photon or a
Due to the steeply falling
Both the DB and MPF methods exploit the momentum balance in events with
The DB response
The MPF method [
From conservation of transverse momentum, the
The MPF response
The
The MPF and DB methods probe the calorimeter response to jets in a different way and are sensitive to different systematic effects. They therefore provide complementary measurements of the jet-energy scale. The explicit use of jets in the measurement of the jet response from DB makes this technique dependent on the jet reconstruction algorithm while the MPF technique is mostly independent of the jet algorithm, as explained above. Thus, in the following, when presenting MPF results, no jet algorithm is explicitly mentioned.
This section outlines the event selection used for the DB and MPF analyses separately for the
The
Reconstructed photons are required to satisfy strict identification criteria ensuring that the pattern of energy deposition in the calorimeter is consistent with that expected for a photon [
A typical
Jets are reconstructed and a preselection is applied, including standard Distributions of
The leading jet is required to have
Measurements of
The
Figure
The value of
For the
The derived intercalibration in Sect.
To validate this calibration, the DB analysis is repeated for the jets with
Figure
For the The MPF response
The final JES calibration that is described in Sect.
7.3.4.1 Suppression of additional radiation
As explained in Sect.
The
7.3.4.2 Systematic uncertainties due to out-of-cone radiation
For the DB method, the
In principle, the MPF technique does not depend on the OOC correction because the calorimeter response is integrated over the whole detector. However, two effects related to the OOC contribution must be considered. The “showering correction” quantifies the migration of energy across the jet boundary of the calorimeter jet relative to the truth-particle jet and is difficult to measure with data. This effect is included in the DB analysis by design since it is based on reconstructed jets but is not included for the MPF method since it measures the entire hadronic recoil. In addition, the hadronic response in the periphery of the jet is different than in the core because of the different energy densities and particle compositions. This “topology correction” is also difficult to extract from data but is expected to be small since the average
7.3.4.3 Impact of the Monte Carlo generator
For each final state, predictions of the response observables (
7.3.4.4 Uncertainties associated with the reference objects
The definitions of
The EES is measured in data [
Each of the 11 uncertainty sources are propagated to the simulated samples by adjusting the four-momenta of the reconstructed electron, muon, or photon. The uncertainties in
7.3.4.5 Impact of additional
Jets produced in additional
Studies of the dependence of MPF response
The GS correction (Sect.
7.3.4.7 Impact of background in the
The
7.3.4.8 Summary of the systematic uncertainties
Figure Summary of the JES statistical and systematic uncertainties evaluated for the
Figure Summary of the JES statistical and systematic uncertainties evaluated for the
For analyses based on pre-2012 data, the JES uncertainty of large-
The DB analysis is performed for large-
Figure
Most of the systematic uncertainties are evaluated in the same way as for small- Rather than using the difference of the data-to-MC ratio of The OOC uncertainty is evaluated only for large- As mentioned in Sect. Since the small- An additional dependence of the jet response for large-
As discussed in Sect. Large-
Within a given track jet The gradients
Based on the Systematic uncertainties due to the Statistical and systematic uncertainties in the data-to-MC ratio of
7.4.3.1 Summary of systematic uncertainties
Figure
The width of the DB distribution in a given Comparison of the jet energy resolution determined in situ (triangles) with the MC jet energy resolution (circles) measured for anti-
Figure
The truth-particle jet DB width Summary of systematic uncertainties of the JER measured using the DB method in
The
The multijet balance observable
As mentioned above, the jets used in the construction of
Multijet events were obtained using single-jet triggers that are fully efficient for a given bin of
Figure
Since the jets entering
Also, the event selection criteria and the modelling in the event generators affect the
The unknown flavour of each jet is also a source of systematic uncertainty. The uncertainty in
Examples of the impact of systematic uncertainties are shown in Fig.
The uncertainty accounting for the difference of the jet energy resolution between data and simulation was not propagated to the recoil system of the multi-jet balance as the scale was derived before the resolution. However, the impact of this effect on the multi-jet balance was checked after the resolution was derived, and was found to introduce per-mille level differences on the extraction of the scale. This effect is therefore negligible compared to the existing uncertainties on the multi-jet balance shown in Fig.
As detailed in Sects.
Figure Ratio of response measured in data to response measured in MC samples for
The separate measurements from
Table detector description (det.), physics modelling (model), statistics and method (stat./meth.), and mixed detector and modelling (mixed). Summary of the uncertainty components propagated through to the combination of absolute in situ jet energy scale measurements from Name Description Category Material uncertainty in electron energy scale Det. Presampler uncertainty in electron energy scale Det. Baseline uncertainty in electron energy scale Mixed Uncertainty in electron energy smearing Mixed Baseline uncertainty in muon energy scale Det. Uncertainty in muon ID momentum smearing Det. Uncertainty in muon MS momentum smearing Det. MC generator Difference between MC generators Model JVF JVF choice Mixed Extrapolation in Model Out-of-cone Contribution of particles outside the jet cone Model Subleading jet veto Variation in subleading jet veto Model Statistical components Statistical uncertainty Stat./meth. Material uncertainty in photon energy scale Det. Presampler uncertainty in photon energy scale Det. Baseline uncertainty in photon energy scale Det. Uncertainty in photon energy smearing Det. MC generator Difference between MC generators Model Extrapolation in Model Out-of-cone Contribution of particles outside the jet cone Model Subleading jet veto Variation in subleading jet veto Model Photon purity Purity of sample in Det. Statistical components Statistical uncertainty Stat./meth. Multijet balance Angle between leading jet and recoil system Model Angle between leading et and closest subleading jet Model MC generator Difference between MC generators (fragmentation) Mixed Asymmetry selection between leading and subleading jet Model Jet Jet Mixed Statistical components Statistical uncertainty Stat./meth.
The combination is carried out using the absolute in situ measurements (Eq. (
Each uncertainty source in the combination is treated as being fully correlated across
One exception is the jet flavour uncertainty of the recoil in the multijet balance method (Sect.
To take tensions between measurements into account, each uncertainty source is increased by rescaling it by
Figure
The combined jet response, shown as a line in Fig.
As mentioned a spline-based combination procedure, with a local averaging within fine Individual uncertainty sources used in the combination for the three absolute in situ calibration methods. The systematic uncertainties displayed correspond to those in Table
In addition to the uncertainties coming from the combination of in situ methods detailed above, there are several other uncertainties that account for other potential systematic effects or expand the kinematic reach. These additional uncertainties are described below, and summarized in Sect.
The jet energy response measured by the in situ methods can also be compared with results from a method where the jet energy scale is estimated from the calorimeter response to single hadrons measured in test beam studies. This provides a cross-check of the direct balance in situ methods, albeit with a larger uncertainty, and also allows the extension of the in situ measurements of the jet energy scale to higher energies beyond the reach of balance methods due to limited data. In this “single hadron” method, jets are treated as a superposition of the individual energy deposits of their constituent particles [
In the previous jet energy scale measurements based on data taken in 2011 [
There are four uncertainties sources associated with the mitigation of the
The in situ methods used to derive final corrections and uncertainties of the jet energy scale make use of event samples with particular fractions of jets initiated by quarks and gluons. The event samples in physics analyses may have jet flavour compositions which differ from that of the calibration sample.
The response for quark-initiated jets is considerably higher than that for gluon-initiated jets (Sect.
While the response for light-quark-initiated jets is found to be in good agreement between different generators, shifts are seen in the gluon jet response for different generators due to differences in the jet fragmentation. There is therefore an additional uncertainty for gluon-initiated jets, which is subdominant in the
Further details of this uncertainty are given in Ref. [
The total jet energy scale uncertainty is compiled from multiple sources: 22 systematic sources from absolute in situ methods, 34 statistical sources from absolute in situ methods, a single-hadron response uncertainty which only affects the highest- two four sources from uncertainties associated with the two sources due to jet flavour (Sect. The total jet energy scale uncertainty as a function of The total jet energy scale uncertainty as a function of
The last two terms are assumed to be independent, resulting in a jet energy scale uncertainty defined in terms of 65 components (nuisance parameters). The resulting, total jet energy scale uncertainty is shown as a function of jet
as outlined in Ref. [
All uncertainties discussed in the previous section apply to MC samples produced using either the full or fast simulation. However, a small non-closure of the jet calibration was observed in fast simulation compared with full simulation. To account for this, an additional systematic uncertainty must be included in analyses using fast simulation since relative and absolute in situ methods are not used to validate this simulation. The size of this uncertainty compared with other systematic uncertainties is generally small for Total uncertainty in the calibration of anti- Total uncertainty in the calibration of anti-
The list of uncertainties described in Sect.
As detailed in Ref. [
Two reduction schemes are provided. The first scheme reduces the number of central absolute in situ nuisance parameters, those shown in Fig. The
A method has been developed for evaluating the correlations between the full set of 56 in situ JES uncertainty terms and a reduced set. This is especially useful for evaluating the correlations between the uncertainties obtained for two physics analyses that use different uncertainty configurations (e.g. the full set and a reduced set of JES uncertainty terms). In this method, each JES uncertainty term in the full set is projected, in the space of uncertainties, onto the direction of each uncertainty term in the reduced set. The corresponding projection coefficients allow expression of the uncertainties propagated by one analysis using a given configuration in terms of the components corresponding to another configuration. Therefore, this allows correlations to be assessed between analyses using different uncertainty configurations.
Many physics analyses use “profiling” of uncertainties in the statistical analysis, such as the profile log-likelihood method, which improves the precision of the associated physics results. These methods may make significant use of the uncertainty amplitudes and correlation in different kinematic regions, and the exact parameterization of the JES systematic uncertainties might impact the result. Since the correlation between uncertainty sources often is unknown, the nominal uncertainty parameterization discussed in the previous sections corresponds to a “best guess”. Certain analyses could erroneously benefit from somewhat arbitrary choices made during the construction of this uncertainty scheme. To allow analyses to test if their results depend on these choices, two alternative uncertainty parameterizations are provided, one that results in stronger JES uncertainty correlations and one that gives weaker correlations. These are constructed by making alternative assumptions about the correlation between different effects and by employing a different rebinning prescription when propagating absolute in situ derived uncertainties to the combination.
In both the strong and weak correlation scenarios, a change is made in the rebinning procedure described in Sect.
For the strong correlations alternative, certain uncertainty components that are treated as being uncorrelated with each other in the nominal parametrization are combined into a correlated component. This is only done for components that are suspected to have some correlation. The flavour composition uncertainty is also switched from using
For the weak correlation alternative, several “2-point” systematic uncertainties are split into two subcomponents [
Uncertainties in the large-
In the double-ratio method, track jets are used as reference objects since charged-particle tracks are both well measured and independent of the calorimeter and are associated with calorimeter jets using a geometrical matching in the
This approach was widely used in the measurement of the jet mass and substructure properties of jets in the 2011 data [ Jet mass scale (JMS) uncertainties for anti-
Similarly to the jet flavour composition uncertainty for small- One minus the jet
The jet Combination of the uncertainties in the jet
The measurement of the jet energy resolution (JER) in data is a multi-step process. As detailed in Sects.
The jet energy resolution is parameterized as a function of three terms [
In Sect.
The jet energy resolution is measured in simulated event samples as described in Sect.
Noise, both from the calorimeter electronics and from
The JER noise term receives contributions from the cells inside the topo-clusters created by the actual truth-particle jet as well as from Jet energy resolution measured in dijet MC samples as a function of
In the random cone method, a cone of given size is formed at a random values of
The
The growth of this noise term at the constituent scale as a function of the average number of interactions per bunch crossing is shown in Fig.
To extract the The balance of random cones of size 0.4 ( The magnitude of the expected fluctuations in different jet radii at the constituent scale derived using the random cone procedure as a function of The magnitude of the expected fluctuations within different jet radii at the constituent scale as a function of
As explained in Sect. Jets are reconstructed using the Cambridge–Aachen algorithm [ For each jet, the quantity The observable Extracted values of Measurements of EM LCW EM LCW 1.81 3.25 1.81 3.25 2.09 3.72 2.09 3.72 1.28 2.30 1.92 3.46 Random cone, data ( 1.52 2.61 2.42 4.19 Difference (%) 16 12 21 17 1.48 2.64 2.22 3.96 Random cone, MC ( 1.60 2.73 2.61 4.49 Difference (%) 7.5 4.4 15 12
The size of the expected fluctuations at the constituent scale of a given jet is given by
As described in the previous two sections, the random cone and the soft jet momentum methods can both be used to measure the noise term of the jet energy resolution. It is useful to compare their results and to contrast the two methods. As well as using different data samples, these methods make quite different assumptions about the underlying physics: The soft jet momentum method implicitly assumes the The random cone method measures the noise in several The symmetry assumption of the two cones back-to-back in azimuth in the zero bias events is not required by the soft jet momentum method.
Further, while the soft jet momentum method gives an estimate of the noise term in each event (as is done for the calculation of
A closure test is performed on the
The same closure test was performed for the Comparison between the
The random cone and soft jet momentum methods provide measurements of the part of the noise term arising from The noise term EM + JES R LCW + JES R EM + JES R LCW + JES R
The total jet energy resolution (Eq. (
The total JER noise term The The three eigenvectors after the eigenvector reduction of the nuisance parameters in the jet energy resolution measurement for LCW + JES The jet resolution as a function of The jet resolution as a function of
The JER measurements based on the bisector method in dijet events reported in Sect.
When propagating the uncertainty in the noise term to the fit the resulting changes in the fitted values of
To reduce the number of parameters which need to be propagated, the full set of eigenvectors is built according to the total effect on the JER measurement of each uncertainty component (rather than the effect of each component on the
When considering the more forward
Finally, to account for correlations between the measurements at different Extracted values of the EM + JES, EM + JES, (0, 0.8) (0.8, 1.2) (1.2, 2.1) (2.1, 2.8) Extracted values of the LCW + JES, LCW + JES, (0, 0.8) (0.8, 1.2) (1.2, 2.1) (2.1, 2.8)
This article describes the determination of the jet energy scale (JES) and jet energy resolution (JER) for data recorded by the ATLAS experiment in 2012 at
The calibration scheme used for anti-
Following these MC-based calibration steps, the data taken in 2012 are used to perform a residual calibration that constrains the uncertainties. This is performed for anti-
The jet energy scale of trimmed anti-
The JER is measured in 2012 data using several in situ methods. The JER
We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, 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 and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong 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; ARRS and 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, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, 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, Thales and 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 from CERN, 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 resource providers. Major contributors of computing resources are listed in Ref. [
This manuscript has no associated data or the data will not be deposited. [Authors’ comment: All ATLAS scientific output is published in journals, and preliminary results are made available in Conference Notes. All are openly available, without restriction on use by external parties beyond copyright law and the standard conditions agreed by CERN. Data associated with journal publications are also made available: tables and data from plots (e.g. cross section values, likelihood profiles, selection efficiencies, cross section limits, ...) are stored in appropriate repositories such as HEPDATA (
ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the
The ATLAS calorimeters have three electromagnetic layers in the pseudorapidity interval
The relative improvement in the jet
The full difference between the generators is considered the uncertainty amplitude of a two-sided systematic uncertainty. All uncertainty components discussed in this paper are treated as two-sided uncertainties.
The hadronic recoil is reconstructed at the constituent scale, for which the calorimeter response can have a significant energy dependance as can be seen in Fig.
The noise is