## 13.5 Comparison of the γ-jet calibration methods

As discussed in Sect. 10, two different techniques exploiting the transverse momentum balance in γ-jet events are used to probe the jet response, the direct balance (DB) and the missing momentum fraction (MPF) method. These methods have different sensitivities to parton radiation, pile-up interactions and photon background contamination, and hence different systematic uncertainties, as explored in Sect. 10.4.

Since the MPF method uses the full hadronic recoil and not only the jet, a systematic uncertainty due to the possible difference in data and MC simulation of the calorimeter response to particles inside and outside of the jet needs to be taken into account. This systematic uncertainty contribution is estimated to be small compared to other considered uncertainties. However, in the absence of a more quantitative estimation, the full energy of all particles produced outside of the jet as estimated in the DB technique is taken as the systematic uncertainty. A comparison between the two results is shown in Fig. 39. The results are compatible within their uncorrelated uncertainties.

As the methods use similar datasets, the measurements are highly correlated and cannot easily be included together in the combination of the in situ techniques. In order to judge which method results in the most precise calibration, the combination described in Sect. 13.2 is performed twice, both for Z-jet, γ-jet DB and multijet balance, and separately for Z-jet, γ-jet MPF and multijet balance. The resulting combined calibration that includes the MPF method has slightly smaller uncertainties, by up to about 0.1%, and is therefore used as the main result.

## 13.6 Simplified description of the correlations

For some applications like parameterised likelihood fits it is preferable to have the JES uncertainties and correlations described by a reduced set of uncertainty components. This can be achieved by combining the least significant (weakest) nuisance parameters into one component while maintaining a sufficient accuracy for the JES uncertainty correlations.

The total covariance matrix $C^{\text{tot}}$ of the JES correction factors can be derived from the individual components of the statistical and systematic uncertainties:

$$C^{\text{tot}} = \sum_{k=1}^{N_{\text{sources}}} C^k, \qquad (14)$$

where the sum goes over the covariance matrices of the individual uncertainty components $C^k$. Each uncertainty component $s^k$ is treated as fully correlated in $p_T$ and the covariance of the $p_T$ bins $i$ and $j$ is given by $C^k_{ij} = s^k_i s^k_j$. All the uncertainty components are treated as independent of one another, except for the photon and electron energy scales which are treated as correlated.<fnref n="16" />

A reduction of the number of nuisance parameters while retaining the information on the correlations can be achieved by

deriving the total covariance matrix in Eq. (14) and diagonalising it:

$$C^{\text{tot}} = S^T D S.$$

Here $D$ is a (positive definite) diagonal matrix, containing the eigenvalues $\sigma_k^2$ of the total covariance matrix, while the $S$ matrix contains on its columns the corresponding (orthogonal) unitary eigenvectors $V^k$. A new set of independent uncertainty sources can then be obtained by multiplying each eigenvector by the corresponding eigenvalue. The covariance matrix can be re-derived from these uncertainty sources using:

$$C^{\text{tot}}_{ij} = \sum_{k=1}^{N_{\text{bins}}} \sigma_k^2 V_i^k V_j^k,$$

where $N_{\text{bins}}$ is the number of bins used in the combination.

A good approximation of the covariance matrix can be obtained by separating out only a small subset of $N_{\text{eff}}$ eigenvectors that have the largest corresponding eigenvalues. From the remaining $N_{\text{bins}} - N_{\text{eff}}$ components, a residual, left-over uncertainty source is determined, with an associated covariance matrix $C'$. The initial covariance matrix can now be approximated as:

$$C^{\text{tot}}_{ij} \approx \sum_{k=1}^{N_{\text{eff}}} \sigma_k^2 V_i^k V_j^k + C'$$

This approximation conserves the total uncertainty, while the precision on the description of the correlations can be directly determined by comparing the original full correlation matrix and the approximate one. The last residual uncertainty could in principle be treated either as correlated or as uncorrelated between the $p_T$ bins. It is observed that treating this uncertainty source as uncorrelated in $p_T$ provides a better approximation of the correlation matrix. This is expected, as this residual uncertainty source includes many orthogonal eigenvectors with small amplitudes and many oscillations, hence the small correlations. The original exact covariance matrix is thus decomposed into a part with strong correlations and another one with much smaller correlations. It is this residual uncertainty source that incorporates the part with small correlations.

Figure 40 shows the obtained five eigenvectors $\sigma_k V^k$ and the residual sixth component, as a function of the jet $p_T$. The $p_T$-dependent sign of these eigenvectors allows to keep track of the (anti-)correlations of each component in different phase-space regions. This is necessary for a good description of the correlations of the total JES uncertainty. These six nuisance parameters are enough to describe the correlation matrix with sufficient precision at the level of percent. As explained above, the quadratic sum of these six components is identical to the quadratic sum of the original uncertainties shown in Fig. 37. In the high-$p_T$ region above 300 GeV, one eigenvector has a significantly larger amplitude than all the others, see the black curve in Fig. 40, hence the strong correlations between the bins. Approximately 60% to 80% of this component is due to the photon and electron energy scale uncertainties up to about 700 GeV (see Figs. 37(c) and 37(d)), while some other uncertainties contribute to it at higher $p_T$.

<footnote n="16">¹⁶ A single systematic uncertainty source is assigned to account for both the photon and electron energy scales by first adding the photon and electron scales linearly, deriving the full covariance matrix, and add it linearly to the covariance matrix of the other uncertainty components.</footnote>
<figure><img src="image_60.png" /><figcaption>Fig. 40: Systematic (effective) relative uncertainties displayed as a function of jet $p_T$ for anti-$k_t$ jets with $R = 0.4$ calibrated with the (a) EM+JES and the (b) LCW+JES calibration schemes for the reduced scheme with six nuisance parameters. Each curve can be interpreted as a $1\sigma$ JES systematic nuisance parameter, symmetric around zero. They represent eigenvectors of the covariance matrix (continuous lines) and the residual component (dashed line).</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>Fig. 41: In (a), the nominal JES correlation matrix is shown. The difference between the correlation matrices of the interpretations resulting in stronger and weaker correlations for anti-$k_t$ jets with $R = 0.4$ calibrated using the EM+JES calibration scheme in the central calorimeter region ($\eta = 0.5$) is depicted in (b).</figcaption></figure>

## 13.7 Jet energy scale correlation scenarios

The JES uncertainty and its correlations discussed so far can play a crucial role in physics analyses. In order to quantify these correlations, knowledge of the interdependence of the systematic uncertainty sources is needed. The limitations in this knowledge lead to uncertainties on the correlations.

The variation of the systematic uncertainty sources as a function of $p_T$ and $\eta$ can be described as a nuisance parameter, as explained before. The total set of correlations can be expressed in the form of a correlation matrix calculated from the full set of nuisance parameters as presented in Sect. 13.4. The correlation matrix, derived assuming that the nuisance pa-

rameters are independent from each other, is shown in Fig. 41 (a).

The nuisance parameters are affected by the strength of the correlations between uncertainty components, which can be difficult to estimate. The investigation of alternative correlation scenarios for the components thus allows to determine the uncertainty on the global correlations shown in Fig. 41 (a).

Two additional configurations are specifically designed to weaken and to strengthen the global correlations. They cover the space of reasonable JES component dependencies. In a given physics analysis these scenarios can be used to examine how the final results are affected by variations of the correlation
<figure><img src="image_62.png" /><figcaption>Fig. 42: Relative uncertainties for reduced (effective) components within a single category displayed as a fraction of jet $p_T$ for anti-$k_t$ jets with $R=0.4$, calibrated with the EM+JES scheme. The convention from Fig. 40 is followed here. The 54 nuisance parameters that are input to the reduction for each of the categories are listed in Table 10. The reduction is performed for all nuisance parameters belonging to any given category, which are statistical and method components (a), detector components (b), modelling components (c), and mixed detector and modelling components (d). Each of the curves can be interpreted as an effective $1\sigma$ JES systematic nuisance parameter, symmetric around zero. They represent eigenvectors of the covariance matrix (continuous lines) and the residual component (dashed line), for the specified category.</figcaption></figure>

strengths. This allows propagation of the uncertainties on the correlations. The difference between the weaker and stronger correlation matrices is shown in Figure Fig. 41(b).

## 13.8 Alternative reduced configurations

A global reduction of nuisance parameters, irrespective of the uncertainty source, is performed in order to reduce the number of these parameters required to represent the full correlation matrix, see Sect. 13.6. However, it is also useful to keep track of the physical meaning of the uncertainty components, e.g. for a proper combination of measurements from different experiments. In Sect. 13.3 each JES systematic uncertainty component is assigned to a representative category, as given in Table 10.

The same reduction technique discussed in Sect. 13.6 is applied independently to each set of uncertainty components within each individual category. The resulting reduced set of uncertainty components for the nominal configuration are shown in Fig. 42. This category reduction approach generally results in a larger number of nuisance parameters than the global reduction. This is because two components from different categories with very similar shapes can be globally combined without significant loss of information for the correlations. However, when the reduction is performed in categories, components may require a nuisance parameter not lose significant precision for the description of the global correlation.

This technique is applied to each of the correlation scenarios. Category reduction configurations are derived for the set of all parameters, the stronger correlation scenario, and the weaker correlation scenario. In each case, correlation matrices
<table><caption>Table 11: A summary of the various explored JES configurations. The precision of the reduction is defined by the largest deviation in correlations between the original full set of parameters and the reduced version. The full $p_T$ phase space is considered in this determination. The values quoted are for jets in the region $|\eta| < 1.2$ for anti-$k_t$ with $R = 0.4$ calibrated with EM+JES scheme. The number of nuisance parameters quoted refers only to the parameters entering the reduction procedure, which are relevant to the in situ techniques.</caption><thead><tr><th>Configuration type</th><th>Reduction</th><th>N<sub>params</sub></th><th>Reduction precision (%)</th></tr></thead><tbody><tr><td rowspan="3">All parameters</td><td>none</td><td>54</td><td>100</td></tr><tr><td>global</td><td>6</td><td>97</td></tr><tr><td>category</td><td>11</td><td>95</td></tr><tr><td rowspan="3">Stronger correlations</td><td>none</td><td>45</td><td>100</td></tr><tr><td>global</td><td>6</td><td>97</td></tr><tr><td>category</td><td>12</td><td>96</td></tr><tr><td rowspan="3">Weaker correlations</td><td>none</td><td>56</td><td>100</td></tr><tr><td>global</td><td>6</td><td>97</td></tr><tr><td>category</td><td>12</td><td>95</td></tr></tbody></table>

are compared to ensure that the reduction preserved correlation information to within a few percent. Table 11 lists the various configurations evaluated, together with the accuracy achieved with the reduction procedure.

## 14 Comparison to jet energy scale uncertainty from single-hadron response measurements

The JES correction and uncertainty derived from in situ techniques exploiting the $p_T$ balance between a jet and a reference object can be compared to the method where the jet energy scale is estimated from single-hadron response measurements, as described in Ref. [3]. In this method, jets are treated as a superposition of energy deposits of single particles. For each calorimeter energy deposition within the jet cone, the type of the particle inside the jet is determined, and the expected mean shift and the systematic uncertainty of the calorimeter response between data and MC simulation is evaluated. The corresponding uncertainty is derived from in situ measurements or systematic MC variations. This deconvolution method is described in Ref. [3, 4] and is used for the derivation of the JES uncertainty for the ATLAS 2010 data analysis.

Measurements of the calorimeter response to pions in the combined test-beam [90] are used for pions with momenta between 20 and 350 GeV.<fnref n="17" /> Single isolated hadrons with momenta up to 20 GeV are selected in a minimum bias sample produced in proton-proton collisions at $\sqrt{s} = 7$ TeV taken in 2011 and the calorimeter energy (E) in a narrow cone around

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /><figcaption>Fig. 43: Relative calorimeter jet response ratio between data and MC simulations, as estimated from the single-hadron response measurements as a function of the jet transverse momentum, is shown in (a). The total systematic uncertainty together with the uncertainty from the individual components is shown as a lighter band. The black circles denote the estimated mean shift of the calorimeter response to jets in data over the one in MC simulations. In (b), the uncertainty from the single-hadron response measurements is shown as a lighter (yellow) band, while the JES uncertainty, as derived from the in situ methods based on $p_T$ balance, is shown as a dark (gray) band. The closed markers denote the estimated shift of the calorimeter response to jets in data over the one in MC simulations, and the line shows the JES correction derived from the $p_T$ balance in situ methods.</figcaption></figure>

an isolated track is compared to the track momentum (p) (see Refs. [4,91] for more details). Effects from the noise thresholds and from the calorimeter acceptance are estimated by comparing the energy measured in calorimeter cells to the one measured in topo-clusters. In addition, the uncertainty on the absolute electromagnetic energy scale is considered and the response uncertainty of protons, anti-protons and neutral hadrons is evaluated using different hadronic shower models, again as

<footnote n="17">¹⁷ The MC simulation was updated from the version used for the combined test-beam studies to the version used for the collision data simulation.</footnote>
described in Refs. [4, 91]. For hadrons with $p > 400$ GeV, for which no measurements are available in the combined test-beam, the uncertainty is conservatively estimated as 10% to account for possible calorimeter non-linearities or longitudinal leakage.

The mean $E/p$ is well described by the MC simulation for $p > 6$ GeV. However, for lower momenta ($1 \lesssim p < 6$ GeV) the data are shifted down with respect to the MC simulation by about 4%. This is in contrast to the 2010 measurement, where an agreement within 3% is found [4]. The worse data-to-MC agreement is due to the new corrections in the absolute electromagnetic energy scale obtained in situ using the Z boson mass constraint reconstructed from $Z \to e^+e^-$, the increased topocluster thresholds, and the use of a new GEANT4 version.

Figure 43(a) shows the estimated calorimeter jet response ratio between data and MC simulation as estimated from the single-hadron response measurements as a function of the jet transverse momentum. A lower calorimeter response to jets in data than in the MC simulations is observed (black circles), consistent with that obtained using in situ techniques. The uncertainty on this ratio is about 4% at very low and very high $p_T$. It decreases to about 2% between $100 \le p_T < 600$ GeV. The individual uncertainty components are also shown. The dominant uncertainties at low $p_T$ are those from noise threshold effects, which can be different for single isolated hadrons and hadrons inside jets. At high $p_T$ the response differences between data and MC simulation as measured in the ATLAS combined test-beam and the uncertainty for hadrons with $p > 400$ GeV are largest. The uncertainty on the global electromagnetic energy scale and the response uncertainty for neutral hadrons contribute about 1%.

Figure 43(b) compares the JES uncertainty as obtained from single hadron response measurements to the one obtained from the in situ method based on the $p_T$ balance between a jet and a well-measured reference object. For both methods the mean jet calorimeter response in data is observed to be shifted down by about 2% with respect to the one in the MC-simulated events. However, the $p_T$ balance methods give a considerably smaller uncertainty.

## 15 Jet energy scale uncertainty from the W boson mass constraint

The mass of the W boson ($m_W$) provides a stable reference for the determination jet energy scale uncertainty. In events where a top pair ($t\bar{t}$) is produced, the hadronically decaying W bosons give rise to two jets that can be well identified. A dedicated event reconstruction is developed in order to find the jets from the W decay. The jet energy measurement can be assessed by measuring the residual difference between the observed and the simulated invariant W mass spectrum.

W provide a pure source of jets induced by quarks. A sizeable fraction of these jets are induced by charm quarks and contain charm hadrons. Given that an unbiased sample of charm jets can not be selected in data, all jets from W decays are treated in the same way.

### 15.1 Event samples

The dataset is selected using single-electron or single-muon triggers. Jets are reconstructed with the anti-$k_t$ algorithm with $R = 0.4$ starting from topo-clusters and are calibrated with the EM+JES scheme. Jets from the decay of heavy-flavour hadrons are selected by the so-called MV1 algorithm, a neural-network-based b-tagging algorithm described in Ref. [92]. It is used at an operating point with 70% efficiency for b-jets, and a mistag rate of less than 1%, as determined from simulated $t\bar{t}$ events.

Events with leptonically decaying W bosons are selected as follows:

Candidate electrons with transverse momenta $p_T > 25$ GeV are required to pass the tight ATLAS electron quality cuts [73]. Muons with transverse momentum $p_T > 20$ GeV are required to pass ATLAS standard muon quality cuts [93]. Events with an electron (muon) are required to be triggered by an electron (muon) trigger with a threshold of 20 (18) GeV, thus ensuring the trigger is fully efficient. Events are required to have a missing transverse momentum $E_T^{\text{miss}} > 30$ GeV ($E_T^{\text{miss}} > 20$ GeV) in the electron (muon) channel. The signal region for this analysis requires exactly one charged lepton and four or more jets. Two b-tagged jets are required in each event. $E_T^{\text{miss}}$ is calculated from the vector sum of the energy in the calorimeter cells associated to topo-clusters [94]. Additionally, the transverse mass of the reconstructed leptonic W boson is required to pass $m_T^W > 30$ GeV in the electron channel, or $E_T^{\text{miss}} + m_T^W > 60$ GeV in the muon channel. Here $m_T^W$ is defined as:

$$m_T^W = \sqrt{2p_T^\ell E_T^{\text{miss}} (1 - \cos(\Delta\Phi(\ell, E_T^{\text{miss}})))},$$

with the lepton transverse momentum $p_T^\ell$ and the azimuthal angle $\Delta\Phi$ between the lepton and the missing transverse energy.

A cut is applied on each event to have fewer than seven reconstructed jets, to significantly reduce the number of possible jet pair combinations per event. The main background processes to $t\bar{t}$ are single-top production, multijet and W boson production in association with jets. The $t\bar{t}$ signal purity is greater than 90% after this selection.

<table><caption>Table 12: Systematic uncertainties on the $\alpha_l$ measurement. Uncertainties lower than 0.05% are not listed. The two different jet selection strategies for the W boson reconstruction discussed in the text are topological proximity (“topo. prox.”) and $p_T$-maximisation (“$p_T$-max.”).</caption><thead><tr><th>Effects</th><th>Δα<sub>l</sub> topo. prox. [%]</th><th>Δα<sub>l</sub> p<sub>T</sub>-max. [%]</th></tr></thead><tbody><tr><td>Multijet background</td><td>±0.12</td><td>±0.18</td></tr><tr><td>Jet resolution</td><td>±0.39</td><td>±0.80</td></tr><tr><td>MC generator</td><td>±0.41</td><td>±0.25</td></tr><tr><td>Fragmentation</td><td>±0.65</td><td>±0.68</td></tr><tr><td>Parton radiation</td><td>±2.48</td><td>±2.42</td></tr><tr><td>Total</td><td>±2.63</td><td>±2.65</td></tr></tbody></table>
<figure><img src="image_65.png" /><figcaption>Fig. 44: The three templates distributions for the reconstructed W mass in $t\bar{t}$ events obtained by shifting the jet energy by a factor $\alpha_l = 0.95$, 1 and 1.05 in the Monte Carlo simulation with respect to the one in data (a). The Monte Carlo simulation templates are also compared to the data distribution. The $\alpha_l$ measurement as a function of mean jet $p_T$ using the maximum $p_T$ reconstruction approach, is shown in (b). Error bars are statistical while hashed rectangles represent the total uncertainties.</figcaption></figure>

## 15.2 Reconstruction of the W boson

The reconstruction efficiency for hadronically decaying W bosons is measured by the fraction of reconstructed jet pairs matching the same W boson. This can be done by forming all possible light-quark jet pairs consisting of jets which are not b-tagged, and calculating their invariant mass $m_{ij}$. Then, only pairs with $|m_{ij} - m_W^{MC}| < 4\sigma_W$ are considered as originating from W boson decays. Here $m_W^{MC}$ is the W mass and $\sigma_W$ is the expected $m_W$ resolution, both taken from MC-simulation samples. This relatively large window of about 11 GeV avoids biases in the reconstructed W mass peak, and only about 3% of true W bosons are rejected by this mass cut

Two methods are used to select one jet pair per event. The first method is based on topological proximity in the detector, where the jet pair which minimises the distance between the two jets $\Delta R_{ij}$, calculated in $(\eta, \phi)$ space as defined in Eq. (3) in Sect. 5.6, is selected. This reconstruction has an efficiency of 51% in finding the signal jet pair at the level of the selection for reconstructible events. The second jet selection method is based on transverse momentum maximisation such that the two light-quark jets maximising the $p_T$ of the reconstructed W are taken as the two jets from the hadronic decay. This reconstruction has an efficiency of 55%.

Jet pairs with $\Delta R_{ij} < 0.7$ are rejected in order to avoid geometrically overlapping jets and to reduce the sensitivity to parton radiation in the W mass spectrum.

In order not to be sensitive to the jet mass the reconstructed W mass $m_W^{rec}$ is calculated as:

$$m_W^{\text{rec}} = \sqrt{2E_1E_2(1 - \cos \theta_{1,2})},$$

where $E_1, E_2$ are the respective energies of the paired jets, and $\theta_{1,2}$ is the opening angle between them.

## 15.3 Extraction of the relative light jet scale

The relative light-quark jet calibration $\alpha_l$ is defined by

$$\alpha_l = \frac{\alpha_l^{\text{data}}}{\alpha_l^{\text{MC}}},$$

where $\alpha_l^{\text{data}}$ ($\alpha_l^{\text{MC}}$) is the jet energy scale in the data (simulation). This analysis uses the expected dependency of the W mass distribution on the $\alpha_l$ parameter. Templates for the $m_W$ distributions are derived from MC simulations, where $\alpha_l^{\text{MC}}$ is varied. This rescaling of $\alpha_l^{\text{MC}}$ is applied before the event selection and the W reconstruction steps. A set of $m_W$ distributions are produced for different $\alpha_l$ values. In order to obtain the $m_W$ distribution of an arbitrary $\alpha_l$ value, a bin-by-bin interpolation is performed using the two generated and adjacent $\alpha_l$ values.

A binned likelihood maximisation with a Poisson law is used. It identifies the $\alpha_l$ values whose associated $m_W$ distribution fits the best to the observed $m_W$ distribution. The analysis templates are defined for $\alpha_l$ values ranging from $\alpha_l = 0.85$ to $\alpha_l = 1.15$.

In order to test the consistency of the extraction method, an arbitrary jet energy scale is applied to one pseudo-experiment of arbitrary luminosity. The comparison is then done between the applied scale and the measured one. The difference between both is compatible with zero for a wide range of $\alpha_l$ hypotheses.
The expected statistical precision on $\alpha_l$ is determined using pseudo-experiments each one containing a number of events corresponding to the luminosity recorded in 2011. A pull variable is computed, reflecting the differences between the measured and the expected mean values scaled with the observed uncertainties. The mean pull is compatible with zero and its standard deviation with unity. The mean value of the uncertainties obtained from the different pseudo-experiments is taken as the expected statistical precision. It is 0.28% for the maximum $p_T$ reconstruction method and 0.29% for the topological proximity reconstruction method.

## 15.4 Systematic uncertainties

The main sources of systematic uncertainties on the $\alpha_l$ measurement are summarised in Table 12 and presented for the topological proximity and the $p_T$ maximisation reconstruction methods.

A variety of potential systematic effects are evaluated. The uncertainty from the shape of the multijet background, the uncertainty on the jet energy resolution, the jet reconstruction efficiency and the b-tagging efficiency and mistag rate. The uncertainties on the Monte Carlo simulation model are estimated in terms of generator variations, fragmentation uncertainty and parton radiation variation rate. In particular the parton radiation rate can alter the $t\bar{t}$ final states, inducing distortions in the reconstructed $m_W$ distribution.

## 15.5 Results

Figure 44(a) shows the observed $m_W$ distribution from the maximum $p_T$ reconstruction compared to three different templates. The relative scale correction $\alpha_l$ is extracted for electron and muon channels together as well as for the two channels separately. Results are summarised in Table 13.

In order to test the stability of the measurement, cross-checks are performed by relaxing the $\Delta R_{ij}$ cut and by changing the $m_W$ reconstruction definition. None of these changes affects the measured $\alpha_l$ by more than 0.15%. Since the definition of $m_W$ depends on $\Delta R_{ij}$, a cross-check is done by an event reweighting in MC simulation in order to reproduce the observed $\Delta R_{ij}$ distribution in data. The effect on $\alpha_l$ is about 0.12% for the two reconstruction methods.

The relative scale $\alpha_l$ is studied as a function of the mean $p_T$, see Fig. 44, as well as a function of $\eta$ of the two jets coming from the W boson decay. The tested $p_T$ values range from 33 to 90 GeV. Templates of the $m_W$ are produced for each bin of $p_T$ or $\eta$. Taking into account systematic uncertainties, no significant dependence is observed with respect to the average $p_T$ or $\eta$ of the two jets. The mean $\alpha_l$ is measured as $\alpha_l = 1.0130 \pm 0.0028 \pm 0.027$.

The agreement between the jet energy scale in data and Monte Carlo simulation is found to be in agreement within the estimated uncertainties. The main systematic uncertainty is related to the modelling of additional parton radiation (see Table 12).

<table><caption>Table 13: The measurement of $\alpha_l$ using the closest proximity ($\Delta R_{ij}^{\min}$) and the maximum $p_T$ ($p_T^{\max}$) approach, respectively, for the electron channel, the muon channel and both together. Uncertainties are statistical only.</caption><thead><tr><th>α<sub>l</sub></th><th>e channel</th><th>μ channel</th><th>e + μ channels</th></tr></thead><tbody><tr><td>ΔR<sup>min</sup><sub>ij</sub></td><td>1.0130 ± 0.0048</td><td>1.0143 ± 0.0038</td><td>1.0137 ± 0.0031</td></tr><tr><td>p<sup>max</sup><sub>T</sub></td><td>1.0105 ± 0.0045</td><td>1.0141 ± 0.0038</td><td>1.0130 ± 0.0028</td></tr></tbody></table>

# 16 Systematic uncertainties on corrections for pile-up interactions

## 16.1 Event and object selection

The pile-up corrections for jets derived from MC simulation, as described in Sect. 6, can be validated with data samples of collisions events where a stable reference that is insensitive to pile-up can be used to assess the agreement of the Monte Carlo simulation with data. Of particular interest here are $\gamma$-jet events in prompt photon production, as the reconstructed photon kinematics are not affected by pile-up, and its transverse momentum $p_T^\gamma$ provides the stable reference for the pile-up dependent response of the balancing jet in the ratio $p_T^\gamma/p_T^{\text{ref}} = p_T^\gamma/p_T^\gamma$. The $\gamma$-jet sample is selected as detailed in Sect. 10.2.

Another per jet kinematic reference is provided by the track jets from the primary collision vertex introduced in Sect. 5.4. These are matched with calorimeter jets, and the transverse momentum ratio $p_T^{\text{jet}}/p_T^{\text{ref}} = p_T^{\text{jet}}/p_T^{\text{trackjet}}$ is evaluated. The jet event sample needed for this evaluation can be extracted from samples with central jets in the final state. Both this and the $\gamma$-jet data samples are mostly useful for validation of the pile-up correction methods, as the limited statistics and phase space coverage in 2011 data do not allow direct determination of the pile-up corrections from these final states in data.

To evaluate the pile-up corrections based on track jets, events with a calorimeter jet matching a track jet with $p_T^{\text{track}} > 20$ GeV are extracted from an event sample triggered by high-$p_T$ muons, thus avoiding potential jet-trigger biases. A track jet is only associated with a calorimeter jet not overlapping with any reconstructed muon with $p_T^\mu > 5$ GeV, to avoid potential biases from heavy-flavour jets containing semi-leptonic decays. The general matching criterion for track jets to calorimeter jets is based on the distance between the two jets $\Delta R$ in $(\eta, \phi)$ space, as defined in Eq. (3) in Sect. 5.6. Only uniquely matched track-jet-calorimeter-jet pairs with distances $\Delta R < 0.3$ are considered. Outside of the imposed requirement for calorimeter jet reconstruction in ATLAS in 2011 ($p_T^{\text{jet}} > 10$ GeV), no further cuts are applied on $p_T^{\text{jet}}$, to avoid biases in the $p_T^{\text{jet}}/p_T^{\text{track}}$ ratio, in particular at low $p_T^{\text{track}}$.

## 16.2 Derivation of the systematic uncertainty

The systematic uncertainties introduced by applying the MC-simulation-based pile-up correction to the reconstructed $p_T^{\text{jet,EM}}$ and $p_T^{\text{jet,LCW}}$ for jets in collision data include the variation of
<figure><img src="image_66.png" /><figcaption>Fig. 45: The difference from the average $\alpha^{\text{EM}}(\eta_{\text{det}})$ of the in-time pile-up signal contribution per reconstructed primary vertex ($\Delta(\partial p_{\text{T,EM}}^{\text{jet}}/\partial N_{\text{PV}})(p_{\text{T}}^{\text{truth}})$) as a function of the true jet transverse momentum $p_{\text{T}}^{\text{truth}}$, for MC-simulated jets reconstructed with anti-$k_t$ $R=0.4$ and $R=0.6$ at the EM scale, in two different regions (a) $|\eta_{\text{det}}| < 0.3$ and (b) $1.2 \le |\eta_{\text{det}}| < 2.1$ of the ATLAS calorimeter. In (c) and (d), the variations of the out-of-time pile-up signal contribution per interaction with $p_{\text{T}}^{\text{truth}}$ around its average $\beta^{\text{EM}}(\eta_{\text{det}})$ ($\Delta(\partial p_{\text{T,EM}}^{\text{jet}}/\partial \mu)(p_{\text{T}}^{\text{truth}})$) are shown for the same jet samples and the same respective $\eta_{\text{det}}$ regions. Logarithmic functions of $p_{\text{T}}^{\text{truth}}$ are fitted to the points obtained from MC simulations.</figcaption></figure>
<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /><figcaption>Fig. 46: The fractional systematic shift due to mis-modelling of the effect of in-time pile-up on the transverse momentum $p_{T,EM+JES}^{jet}$ of jets reconstructed with the anti-$k_t$ algorithm with $R = 0.4$, and calibrated with the EM+JES scheme, is shown as a function of $(N_{PV} - N_{PV}^{ref})$ in (a), (c), and (e) for various $p_{T,EM+JES}^{jet}$ bins. The same systematic shift is shown in (b), (d), and (f) for jets calibrated with the LCW+JES scheme, now in bins of $p_{T,LCW+JES}^{jet}$.</figcaption></figure>
<figure><img src="image_73.png" /><figcaption>(a) EM+JES, $R = 0.6$</figcaption></figure>

<figure><img src="image_74.png" /><figcaption>(b) LCW+JES, $R = 0.6$</figcaption></figure>

<figure><img src="image_75.png" /><figcaption>(c) EM+JES, $R = 0.6$</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>(d) LCW+JES, $R = 0.6$</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>(e) EM+JES, $R = 0.6$</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>(f) LCW+JES, $R = 0.6$</figcaption></figure>

Fig. 47: The fractional systematic shift due to mis-modelling of the effect of in-time pile-up on the transverse momentum $p_{T,EM+JES}^{jet}$ of jets reconstructed with the anti-$k_t$ algorithm with $R = 0.6$ and calibrated with the EM+JES scheme, is shown as a function of $(N_{PV} - N_{PV}^{ref})$ in (a), (c), and (e) for various $p_{T,EM+JES}^{jet}$ bins. The same systematic shift is shown in (b), (d), and (f) for jets calibrated with the LCW+JES scheme, now in bins of $p_{T,LCW+JES}^{jet}$.
<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /></figure>

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /><figcaption>Fig. 48: The fractional systematic shift due to mis-modelling of the effect of out-of-time pile-up on the transverse momentum $p_{T,EM+JES}^{jet}$ of jets reconstructed with the anti-$k_t$ algorithm with $R = 0.4$ and calibrated with the EM+JES scheme, is shown as a function of $(\mu - \mu^{\text{ref}})$ in (a), (c), and (e) for various $p_{T,EM+JES}^{jet}$ bins. The same systematic shift is shown in (b), (d), and (f) for jets calibrated with the LCW+JES scheme, now in bins of $p_{T,LCW+JES}^{jet}$.</figcaption></figure>
<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /><figcaption>Fig. 49: The fractional systematic shift due to mis-modelling of the effect of out-of-time pile-up on the transverse momentum $p_{T,EM+JES}^{jet}$ of jets reconstructed with the anti-$k_t$ algorithm with $R = 0.6$ and calibrated with the EM+JES scheme, is shown as a function of $(\mu - \mu^{\text{ref}})$ in (a), (c), and (e) for various $p_{T,EM+JES}^{jet}$ bins. The same systematic shift is shown in (b), (d), and (f) for jets calibrated with the LCW+JES scheme, now in bins of $p_{T,LCW+JES}^{jet}$.</figcaption></figure>
the slopes $\alpha = \partial p_T/\partial N_{PV}$ and $\beta = \partial p_T/\partial \mu$ with changing jet $p_T$. While the immediate expectation from the stochastic and diffuse nature of the (transverse) energy flow in pile-up events is that all slopes in $N_{PV}$ ($\alpha^{\text{EM}}$, $\alpha^{\text{LCW}}$) and $\mu$ ($\beta^{\text{EM}}$, $\beta^{\text{LCW}}$) are independent of this jet $p_T$, Fig. 45 clearly shows a $p_T^{\text{truth}}$ dependence of the signal contributions from in-time and out-of-time pile-up for jets reconstructed on EM scale. A similar $p_T^{\text{truth}}$ dependence can be observed for jets reconstructed on LCW scale.

The fact that the variations $\Delta(\partial p_T/\partial N_{PV})$ with $p_T^{\text{truth}}$ are very similar for narrow ($R = 0.4$) and wide ($R = 0.6$) anti-$k_t$ jets indicates that this $p_T$ dependence is associated with the signal core of the jet. The presence of dense signals from the jet increases the likelihood that small pile-up signals survive the noise suppression applied in the topological clustering algorithm, see Sect. 5.1. As the core signal density of jets increases with $p_T$, the acceptance for small pile-up signals thus increases as well. Consequently, the pile-up signal contribution to the jet increases. This jet $p_T$ dependence is expected to approach a plateau as the cluster occupancy in the core of the jet approaches saturation, which means that all calorimeter cells in the jet core survive the selection imposed by the noise thresholds in the topo-cluster formation, and therefore all pile-up scattered into these same cells contributes to the reconstructed jet $p_T$. The jet $p_T$ dependent pile-up contribution is not explicitly corrected for, and thus is implicitly included in the systematic uncertainty discussed below.

Since the pile-up correction is derived from MC simulations, it explicitly does not correct for systematic shifts due to mis-modelling of the effects of pile-up on simulated jets. The sizes of these shifts may be estimated from the differences between the offsets obtained from data and from MC simulations:

$$ \Delta\Theta^{\text{EM}} = \Theta^{\text{EM}}(N_{\text{PV}}, \mu)|_{\text{data}} - \Theta^{\text{EM}}(N_{\text{PV}}, \mu)|_{\text{MC}} \\ \Delta\Theta^{\text{LCW}} = \Theta^{\text{LCW}}(N_{\text{PV}}, \mu)|_{\text{data}} - \Theta^{\text{LCW}}(N_{\text{PV}}, \mu)|_{\text{MC}} $$

To assign uncertainties that can cover these shifts, and to incorporate the results from each in situ method, combined uncertainties are calculated as a weighted RMS of $\Delta\Theta(N_{PV}, \mu)$ from the offset measurements based on $\gamma$-jet and on track jets. The weight of each contribution is the inverse squared uncertainty of the corresponding $\Delta\Theta(N_{PV}, \mu)$. This yields absolute uncertainties in $\alpha$ and $\beta$, which are then translated to fractional systematic shifts in the fully calibrated and corrected jet $p_T$ that depend on the pile-up environment, as described by $N_{PV}$ and $\mu$.

Figure 46 shows the fractional systematic shift in the $p_T$ measurement for anti-$k_t$ jets with $R = 0.4$, as a function of the in-time pile-up activity measured by the displacement ($N_{PV} - N_{PV}^{\text{ref}}$). The shifts are shown for various regions of the ATLAS calorimeters, indicated by $\eta_{\text{det}}$, and in bins of the reconstructed transverse jet momentum $p_{T,\text{EM}+\text{JES}}^{\text{jet}}$ for jets calibrated with the EM+JES scheme (Figs. 46(a), 46(c), and 46(e)). Figures 46(b), 46(d) and 46(f) show the shifts for jets reconstructed with the LCW+JES scheme in the same regions of ATLAS, in bins of $p_{T,\text{LCW}+\text{JES}}^{\text{jet}}$. The same uncertainty contributions from wider jets reconstructed with the anti-$k_t$ algorithm with $R = 0.6$ are shown in Fig. 47.

Both the EM+JES and LCW+JES calibrations are normalized such that the pile-up signal contribution is 0 for $N_{PV} = N_{PV}^{\text{ref}}$

and $\mu = \mu^{\text{ref}}$, so the fractional systematic shifts associated with pile-up scale linearly with the displacement from this reference. In general, jets reconstructed with EM+JES show a larger systematic shift from in-time pile-up than LCW+JES jets, together with a larger dependence on the jet catchment area defined by $R$, and the jet direction $\eta_{\text{det}}$. In particular, the shift per reconstructed vertex for LCW+JES jets in the two lowest $p_{T,\text{LCW}+\text{JES}}^{\text{jet}}$ bins shows essentially no dependence on $R$ or $\eta_{\text{det}}$, as can be seen comparing Figs. 46(b) and 47(b) to Figs. 46(d) and 47(d).

The systematic shift associated with out-of-time pile-up, on the other hand, is independent of the chosen jet size, as shown in Fig. 48 for $R = 0.4$ and Fig. 49 for $R = 0.6$. Similar to the shift from in-time pile-up, the jets reconstructed with the LCW+JES scheme show smaller systematic shifts from out-of-time pile-up. The results shown in these figures also indicate that the shift from out-of-time pile-up is independent of the jet size. Note that both shifts contribute to the jet $p_T$ reconstruction uncertainty in an uncorrelated fashion, which is justified as while $N_{PV}$ and $\mu$ are correlated in a given sample, the corrections depending on them are derived independently.

## 16.3 Summary on pile-up interaction corrections

Dedicated correction methods addressing the signal contributions from in-time and out-of-time pile-up to the jet energy measurement with the ATLAS calorimeters were developed using MC simulations to measure the change of the jet signal as function of the characteristic variables measuring the pile-up activity, which are the number of reconstructed primary vertices $N_{PV}$ (in-time pile-up) and the average number of pile-up interactions per bunch crossing $\mu$ (out-of-time pile-up). The input to these corrections are the slopes $\alpha = \partial p_T/\partial N_{PV}$ and $\beta = \partial p_T/\partial \mu$, which are determined in the simulation for two jet signal scales, the EM scale ($p_{T,\text{EM}}^{\text{jet}}$) and the hadronic LCW scale ($p_{T,\text{LCW}}^{\text{jet}}$), both as functions of the truth-jet $p_T^{\text{truth}}$ and the direction of the jet in the detector $\eta_{\text{det}}$.

As an alternative to the approach based on MC simulation, the change of the reconstructed (calorimeter) jet $p_T$ with $N_{PV}$ and $\mu$ can be measured in data using the matching track jet's $p_T^{\text{trackjet}}$ as a kinematic reference independent of the pile-up activity. Furthermore, $\gamma$-jet events can be used in the same manner, with the photon $p_T$ providing the reference in this case. These experimental methods are restricted by the coverage of the ATLAS tracking detector (track jets), and the lack of significant statistics for events with jets at higher $\eta_{\text{det}}$ in $\gamma$-jet events in 2011.

Comparing the in situ measurements of $\alpha$ and $\beta$ with the corresponding simulation and the findings from the approach solely based on MC simulations allows the determination of systematic biases due to mis-modelling of the effects of pile-up on simulated jets. To cover these biases, uncertainties are assessed as functions of $N_{PV}$ and $\mu$. These uncertainties amount to less than 0.3%(0.5%) of the calibrated jet $p_T$ per reconstructed vertex for central anti-$k_t$ jets with $R = 0.4(0.6)$ with $20 < p_T < 30$ GeV and for $\mu = \mu^{\text{ref}}$, and about 0.7% per interaction for jets in the same phase space at $N_{PV} = N_{PV}^{\text{ref}}$, independent of the jet size. The uncertainty contribution in the forward
direction can be significantly larger, by up to a factor of two, especially at higher jet $p_T$, where the uncertainty in the central detector is smaller than 0.1%(0.2)% per vertex and 0.2% per interaction. These generally small uncertainties can be added in quadrature to give a total fractional uncertainty for each pile-up condition ($N_{PV,\mu}$).

A residual jet $p_T$ dependence of the pile-up correction is observed in MC simulation (see Fig. 45), but not yet fully confirmed in data due to limited size of the data set. It is therefore not explicitly addressed in the correction procedure, rather it is implicitly included into the systematic uncertainties. This dependence, which is not expected for a purely stochastic and diffuse signal contribution from both in-time and out-of-time pile-up, is introduced by the topo-clusters formation in the calorimeter, which enhances the survivability of small (pile-up) signals if higher density signals such as those in the core of a jet are close by. At very high jet $p_T$, this dependence reaches a plateau, since the jet core gets so dense that all calorimeter cells contribute to the jet signal, and therefore all signal generated by pile-up in these cells is directly included in the jet signal.

In summary, the pile-up signal contribution to jets in the ATLAS detector is well understood. The correction based on MC simulations controls this contribution to a high precision with uncertainties of less than 1% per reconstructed primary vertex and additional proton-proton collision per bunch crossing, yielding a small fractional contribution to the overall jet energy scale uncertainty over the whole phase space, except for the very forward region, where this uncertainty can be more significant.

## 17 Close-by jet effects on jet energy scale

The variation of the jet energy response due to nearby jets and the associated systematic uncertainty are reported in Ref. [3], using the data collected in 2010. The same analysis is performed to reassess this uncertainty for the 2011 data.

The analysis uses track jets from the primary vertex, as defined in Sect. 5.4, as a kinematic reference. The calorimeter jet's transverse momentum $p_T^{jet}$ relative to the track-jet transverse momentum $p_T^{trackjet}$ provides an in situ validation of the calorimeter jet response and the evaluation of the systematic uncertainty. The relative response measurement is performed in bins of $R_{min}$, the distance in $(\eta, \phi)$ space from the jet to the closest other jet with $p_T > 7$ GeV at the EM scale. The response to track jets is also evaluated for the non-isolated condition $R_{min} < 2.5 \times R$, where R is the distance parameter used in the anti-$k_t$ jet reconstruction, and the associated systematic uncertainty is assessed. In the relative response measurement, the track jet is matched to the calorimeter jet with the distance requirement $\Delta R < 0.3$, where $\Delta R$ is measured according to Eq. (3) (Sect. 5.6) in $(\eta, \phi)$ space. When two or more jets are matched within the $\Delta R$ range, the closest matched jet is taken.

The calorimeter jet response relative to the matched track jet, defined as the $p_T$ ratio of the calorimeter to the track jet as a function of $p_T^{jet}$,

$$r^{calo/trackjet} = p_T^{jet}/p_T^{trackjet},$$

is examined for different $R_{min}$ values measured for the two close-by calorimeter jets.<fnref n="18" /> The ratio of calorimeter jet response between non-isolated (i.e, small $R_{min}$) and isolated (large $R_{min}$) jets, given by

$$r_{non-iso/iso}^{calo/trackjet} = \frac{r_{non-iso}^{calo/trackjet}}{r_{iso}^{calo/trackjet}},$$

is compared between data and MC simulation. The relative difference between them,

$$A_{\text{close-by}} = \frac{r_{\text{non-iso/iso}}^{\text{calo/trackjet}}}{r_{\text{non-iso/iso}}^{\text{calo/trackjet}}} \Big|_{\text{Data}} \Big|_{\text{MC}}, \quad (15)$$

is assumed to represent the calorimeter JES uncertainty due to close-by jets. This uncertainty, convolved with the systematic uncertainty of the response to a track jet with a nearby jet, and evaluated in a similar way as the data-to-MC difference between the average $p_T$ ratio of the non-isolated to isolated track jets, provides the total JES systematic uncertainty due to the close-by jet effect.

## 17.1 Samples and event selection

Data collected with four single-jet, pre-scaled triggers with jet-$p_T$ thresholds of 10, 30, 55 and 135 GeV are used in the analysis. As in the MJB analysis discussed in Sect. 11, the data from a given trigger are used in a certain non-overlapping jet-$p_T$ range where the trigger is greater than 99% efficient. For MC simulation, the baseline PYTHIA samples described in Sect. 3 are used.

Events passing the trigger selections are required to satisfy the same primary vertex and event cleaning criteria for jets due to noise and detector problems as those used in the MJB analysis (see Sect. 11.2). Finally, events that contain at least two jets with calibrated $p_T > 20$ GeV and rapidity $|y| < 2.8$ are selected for the analysis.

The track jets are reconstructed from the selected tracks by using the anti-$k_t$ algorithm with $R = 0.4$ and $R = 0.6$, as described in Sect. 5.4. In the analysis presented below, track jets with $p_T > 10$ GeV and $|\eta| < 2.0$, composed of at least two tracks, are used. The close-by jet energy scale uncertainty is therefore assessed in the region of $|\eta| < 2.0$ where the calorimeter jets and track jets can be matched in $\eta$ and $\phi$.

## 17.2 Non-isolated jet energy scale uncertainty

The average track-jet transverse momentum is examined as a function of the calorimeter jet $p_T$ for different $R_{min}$ values starting from the jet radius in bins of $\Delta R_{min} = 0.1$. The ratio of the average track-jet $p_T$ between the non-isolated and isolated track jets $p_T^{non-iso}/p_T^{iso}$ in bin of the calorimeter $p_T$, is used to

<footnote n="18">¹⁸ Unless otherwise stated, both calorimeter jets are used in the jet response measurement if each of them can be matched to a track jet.</footnote>
<figure><img src="image_91.png" /><figcaption>Fig. 50: In (a), the deviation from unity of the data-to-MC ratio of the track-jet $p_T$ for non-isolated jets divided by the track-jet $p_T$ for isolated jets, is shown as a function of the jet $p_T$. The deviation from unity of the data-to-MC ratio of the relative response of non-isolated jets with respect to that of isolated jets as a function of the jet $p_T$ is shown in (b). As described in the text, the distributions show the ratios given in (a) Eq. (16) and (b) Eq. (15) for the four jet calibration schemes. Only statistical uncertainties are shown.</figcaption></figure>

quantify the uncertainty in the response to track jets. This comparison is shown in Fig. 50(a) as a deviation from unity of the data-to-MC ratio:

$$A_{\text{close-by}}^{\text{trackjet}} = \frac{p_{\text{T}}^{\text{non-iso}}/p_{\text{T}}^{\text{iso}}|_{\text{Data}}}{p_{\text{T}}^{\text{non-iso}}/p_{\text{T}}^{\text{iso}}|_{\text{MC}}} \quad (16)$$

to quantify the uncertainty in the response to track jets in the small $R_{\min}$ range of $R \le R_{\min} < R + 0.1$. The $A_{\text{close-by}}^{\text{trackjet}}$ has a strong $R_{\min}$ dependence, especially at small $\Delta R_{\min}$ range where the close-by jet overlaps the probe jet, and the dependence is more significant for jets with $R = 0.6$. The agreement between data and MC simulations improves with increasing $R_{\min}$.

The calorimeter jet response relative to the matched track jet ($r_{\text{calo/trackjet}}$) is investigated as a function of $p_T$, in terms of the non-isolated jet response relative to the isolated jet response $r_{\text{non-iso/iso}}^{\text{calo/trackjet}}$, for data and MC simulations. The data-to-MC ratio $A_{\text{close-by}}$ of $r_{\text{non-iso/iso}}^{\text{calo/trackjet}}$ is shown in Fig. 50(b) as the deviation from unity for the range of $R \le R_{\min} < R + 0.1$. As already seen in the track-jet response in Fig. 50(a), there is a strong $R_{\min}$ dependence on $A_{\text{close-by}}$ within the small $R_{\min}$ range mentioned above. The deviation of $A_{\text{close-by}}$ from unity is added in quadrature with the track-jet response uncertainties obtained above to get the overall JES uncertainties due to close-by jet effects. The convoluted uncertainty is about 3.5% (10%) at $R_{\min} < 0.5$ (0.7) for $R = 0.4$ (0.6) jets with $p_T = 30$ GeV, and becomes smaller than 1% at $R_{\min}$ above 0.8 for both sizes of jets. The uncertainty decreases with increasing jet $p_T$ and becomes about 2% (4%) at $R_{\min} < 0.5$ (0.7) for $R = 0.4$ (0.6) jets with $p_T = 100$ GeV.

## 18 Jet response difference for quark and gluon induced jets and associated uncertainty

All jet calibration schemes developed in ATLAS achieve an average response of the calorimeter to jets near unity for jets in the inclusive jet sample. However, the calorimeter response to jets also exhibits variations that can be correlated to the flavour of the partons (i.e., light or heavy quarks, or gluons) produced in the sample under study. This dependence is to a large extent due to differences in fragmentation and showering properties of jets loosely labelled as originating from a light quark or a gluon.

In this section, the dependence of the jet energy scale on whether a jet originates from a light quark or a gluon is studied. Also, a systematic uncertainty that accounts for the sample dependence of the jet energy scale is established using different MC simulations. In addition, jet properties that can be shown to discriminate between jets initiated by light quarks and gluons are used to build a light-quark/gluon tagger [3, 95]. The focus in this section is on understanding how the JES is affected by a selection based on the light-quark/gluon tagger, and the implications for the sample-dependent systematic uncertainty described if jets are tagged using this tagger. Details of the procedure to build a quark-gluon tagger can be found in Ref. [96].

### 18.1 Event selection

#### 18.1.1 Jet and track selection

Calorimeter jets with transverse momentum $p_T > 20$ GeV and $|\eta| < 4.5$ are reconstructed using the anti-$k_t$ jet algorithm with $R = 0.4$.
The variables described in Sect. 18.3 are constructed to describe the properties of jets. They are based on tracks with $p_T^{\text{track}} > 1$ GeV that are associated to jets if they are within a distance $\Delta R = R$ (equal to the distance parameter $R$ used to build the jet) of the jet axis. The tracks are further selected as described in Sect. 5.4, with slightly modified quality requirements in order to provide an even stronger association to the primary vertex (impact parameters $z_0 \sin(\theta) < 1$ mm and $d_0 < 1$ mm).

### 18.1.2 Jet flavour definition

Jets are labelled by partonic flavour, if they have $p_T > 40$ GeV and $|\eta| < 2.1$. They are matched to the highest-energy parton found inside the cone of the jet. This parton can be produced directly off the hard scatter, or by radiation.

This definition of partonic jet flavour is not theoretically sound, and that may have implications when attempting to apply this labelling to physics analyses. However, several studies with MADGRAPH [34] have demonstrated that this definition is not changed by the parton shower model choices, and is equivalent to a matrix-element-based labelling for over 95% of jets. Since the partonic flavour of a jet can only be easily defined in leading order, and since only a labelling indicating differences in jet properties is required for the performance evaluations presented in this paper, this definition is sufficient.

### 18.1.3 Dataset for flavour studies

Two main event samples are used. The first selects inclusive jet events (dijet sample). The second selects jets with a high-transverse momentum photon back-to-back with a jet (γ-jet sample). Both samples are defined using standard data-quality criteria and the requirement of a primary vertex with at least three associated tracks.

Central jet triggers are used for the dijet sample selection. These triggers provide a fully efficient jet selection for $p_T > 40$ GeV. Jet triggers with $p_T$ thresholds less than 500 GeV are pre-scaled, so that only a fraction of the events in this kinematic regime are recorded.

The γ-jet sample is selected as described in Sect. 10. In addition, a photon with $p_T > 45$ GeV in the event is required to be back-to-back (azimuthal distance $\Delta\phi > 2.8$ rad) to the leading jet. The sub-leading jet is required to have no more than 30% of the photon $p_T$.

## 18.2 Calorimeter response to quark and gluon induced jets

Jets labelled as originating from light quarks have significantly different response ($p_T^{\text{jet}}/p_T^{\text{truth}}$) from those labelled as originating from gluons in the MC simulation. This difference is a result of a difference in fragmentation that can be correlated to differences in observable properties of the two types of jets. Gluon jets tend to have more particles, and as a result, those particles tend to have lower $p_T$ than in the case of light-quark jets. Additionally, gluon jets tend to have a wider angular energy profile before interacting with the detector.

The harder particles in light-quark jets have a higher probability of penetrating further into the calorimeter, and thus more often reaching the hadronic calorimeter layers. The lower response of the calorimeter for low-$p_T$ particles combined with threshold and response effects related to the energy density inside the jet suggest that gluon jets should have a lower response than light-quark jets. The difference in calorimeter response in MC simulations between isolated light-quark and gluon jets is shown in Fig. 51, for anti-$k_t$ jets with $R = 0.4$ in the barrel calorimeter ($|\eta_{\text{det}}| < 0.8$).

Independent of the calibration scheme, the flavour-dependent response difference is largest at low $p_T$ (up to 8% for EM+JES), and decreases to a few percent at high $p_T$. A more sophisticated calibration scheme like LCW+JES reduces the differences, because it exploits signal features of individual particle showers in the calorimeter for calibration, and thus partly compensates for variations in jet fragmentation and directional energy flow in the jet. Even more so, the Global Sequential (GS) calibration introduced in Ref. [3], which can be applied on top of the (standard) EM+JES or LCW+JES calibration, or just to jets at the EM scale as done for the studies discussed here, shows the best performance at low $p_T$. This is due to its explicit use of a jet width variable which is strongly related with the transverse structure of the jet and is thus sensitive to differences between jets initiated by light-quarks and gluons. The response difference between light-quark- and gluon-initiated jets is reduced by roughly 1% for anti-$k_t$ jets with $R = 0.6$, because the larger jet area diminishes the effect of the energy loss of the broader jet.

The differences in response between jets initiated by light quarks and gluons can impact analyses in which the flavour composition of the sample is not well known. The corresponding JES uncertainties can be reduced if the flavour composition of the analysis sample is known and the accuracy of the MC description of the data can be established. This uncertainty can be extracted directly from Fig. 51 and amounts to about 2% at low $p_T$ and 0.5% at high $p_T$ for the EM+JES calibration, if the flavour composition of the sample is known within 25%. It can be reduced by a factor of two at low $p_T$ and even more at high $p_T$ through the use of one of the more sophisticated calibration schemes.

These response differences between jets initiated by light quarks and gluons result in a sample dependence of the energy scale and suggests that the JES calibration determined from in situ techniques might only be applicable within a larger systematic uncertainty to different jet samples. With the techniques commissioned up to date, the 2011 dataset only allows for a coarse validation of the differences in the jet energy scale between light-quark- and gluon-initiated jets. MC simulations are instead used to understand the impact of systematic effects in the response differences between light-quark and gluon jets.

Figure 51 shows the jet response difference between jets initiated by light quarks and gluons in the central $|\eta_{\text{det}}|$ region of ATLAS for PYTHIA (standard ATLAS MC11 tune), PYTHIA (PERUGIA2011 tune) and HERWIG++. Comparisons between the first two simulations show the impact of the underlying event tune on the response differences. Comparisons between PYTHIA and HERWIG++ provide an estimate of the impact of differences in the modelling of the parton shower,
<figure><img src="image_92.png" /><figcaption>Fig. 51: Difference in jet response $\mathcal{R} = p_T^{\text{jet}}/p_T^{\text{truth}}$ of isolated jets initiated by light quarks and gluons as a function of the true jet $p_T$, for anti-$k_t$ jets with $R = 0.4$ in the barrel calorimeter. Three different calibration schemes are shown for (a) the EM+JES calibration, (b) the LCW+JES calibration, and (c) the alternative Global Sequential (GS) [3] scheme. Three different MC simulation samples are also shown, PYTHIA (solid red circles), HERWIG++ (open blue circles) and PYTHIA PERUGIA2011 (open black squares).</figcaption></figure>

fragmentation and hadronisation for generators modelling the jet fragmentation well within the constraints provided by data. The differences in the response between these two models are large, while the effect of the underlying event tune is small, as can be seen by comparing the standard PYTHIA MC11 tune with the PERUGIA2011 tune.

Further analysis of the large differences between PYTHIA and HERWIG++ indicate that the cause is almost exclusively the difference in the response to gluon jets. This leads to a sizable response difference for the inclusive jet sample, which in the lower-$p_T$ region has mainly gluon-initiated jets in the final state. Significantly smaller differences are observed in the samples used to calibrate the absolute jet response in the lower-$p_T$ regime, like $\gamma$-jet and Z-jet, which have a dominant contribution from light-quark jets.

The systematic effect illustrated by the difference between the two MC simulations can be included as an additional systematic uncertainty. For this, the response variation $\Delta\mathcal{R}_S$ for a given event sample $S$ can be written as

$$ \Delta\mathcal{R}_S = \Delta f_g(\mathcal{R}_g - 1) + \Delta f_{uds}(\mathcal{R}_{uds} - 1) + f_g \Delta\mathcal{R}_g + f_{uds} \Delta\mathcal{R}_{uds} + f_b \Delta\mathcal{R}_b + f_c \Delta\mathcal{R}_c, \quad (17) $$

where $\mathcal{R}_g$, $\mathcal{R}_{uds}$, $\mathcal{R}_c$, and $\mathcal{R}_b$ refer to the response to jets initiated by gluons, light ($u, d, s$) quarks, c-quarks, and b-quarks, with $\Delta$ denoting the uncertainty on the respective variable. The fractions $f_x$ refer to the fractions of jets with a given partonic flavour $x \in \{g, uds, c, b\}$ in the sample $s$. Under the simplifying assumption that the jet energy scale uncertainty is established in situ for light-quark jets and that it is the same for jets from b-quarks and c-quarks, Eq. (17) can be simplified to

$$ \Delta\mathcal{R}_S = \Delta f_q(\mathcal{R}_q - \mathcal{R}_g) + \Delta\mathcal{R}_q + f_g \Delta\mathcal{R}_g^{\text{ex}}, \quad (18) $$

where $\Delta\mathcal{R}_q \equiv \Delta\mathcal{R}_{uds} \equiv \Delta\mathcal{R}_b \equiv \Delta\mathcal{R}_c$ and $f_q = f_{uds} + f_c + f_b = 1 - f_g$. The additional term $\Delta\mathcal{R}_g^{\text{ex}}$ reflects an additional variation

that represents the uncertainty on the response of gluon jets that arises from the systematic effects captured by the different MC simulations. Note that the first term of this equation is used to estimate the effect of the results shown in Fig. 51 on the systematic uncertainty of the jet energy scale in a sample of imprecisely known flavour composition.

The additional term $\mathcal{R}_g^{\text{ex}}$ was not added to the 2010 ATLAS jet energy scale uncertainty for simplicity, since it was much smaller than the dominant contributing effects. The improvements in the jet energy measurement achieved with the 2011 dataset require this more careful treatment. Using the response difference $\mathcal{R}_q - \mathcal{R}_g$ with the EM+JES calibration at low $p_T$ shown in Fig. 51, the uncertainty on $\mathcal{R}_g^{\text{ex}}$ amounts to about 3% in a sample with 75% gluon content, which is close to the inclusive jet sample. It is reduced to about 1% in a sample with 25% gluon content, as expected for $t\bar{t}$ with radiation. The uncertainty at high $p_T$ is smaller than 1%. This term in the uncertainty can also be reduced by a factor of 2 or more when using the more evolved calibration schemes LCW+JES or GS.

The in situ jet energy scale uncertainty is derived using $\gamma$-jet and Z-jet samples, which at low $p_T$ are dominated by light-quark jets. The expression for the total uncertainty presented here could be generalised to account for the fact that there is some gluon-initiated jet contamination, and that the uncertainty on the light-quark jet response $\Delta\mathcal{R}_q$ cannot be established using these samples alone. However, the approximation that the $\gamma$-jet and Z-jet sample are pure light-quark jet samples is most accurate at low $p_T$, where the gluon jet response uncertainty is largest. Thus, this approximation leads to inaccuracies that are significantly smaller than other systematic uncertainties in the average jet response.
## 18.3 Discrimination of light-quark and gluon induced jets

As indicated before, the differences between light-quark and gluon jets lead to (average) differences in observable final-state jet properties. Jets initiated by gluons are expected to be broader, with more low-$p_T$ particles than those initiated by light quarks. Relevant observables like the jet width $w_{jet}$, as reconstructed using the $p_T$ flow of tracks associated with the jet, and the number of those tracks $n_{trk}$, are already used to measure the average flavour fractions in different data samples [3]. They are identified as powerful discriminators for the purpose of understanding partonic flavour in previous studies [95]. More details on the quark-gluon tagger performance in the ATLAS detector can be found in Ref. [96].

These jet properties, reconstructed using selected high-quality tracks, are further exploited to build a likelihood discriminator or a light-quark/gluon tagger. Two-dimensional ($n_{trk}, w_{jet}$) distributions are determined for data and MC simulations using the inclusive jet and $\gamma$-jet event samples. The different fractions of light-quarks and gluons in these samples, which in MC simulation are extracted from PYTHIA with the ATLAS MC11 tune, are then reflected by variations in the ($n_{trk}, w_{jet}$) distributions, and the expected “pure” jet sample properties can be extracted. This procedure is applied both in data and MC simulations, and both data-driven and MC-based taggers are built. Operating points are defined at fixed light-quark jet efficiencies of 30%, 50%, 70% and 90%, using the same extracted ($n_{trk}, w_{jet}$) distributions.

The quark/gluon tagger essentially selects jets with both decreasing $n_{trk}$ and $w_{jet}$ as the operating point tightens, to achieve a higher gluon jet rejection at the expense of a lower light-quark jet efficiency. It can then be expected that jets selected with different operating points of the tagger have different jet energy scales. This is shown in Fig. 52, where the response as a function of the operating point used to select jets in an inclusive MC-simulation sample is shown for two $p_T$ bins for jets calibrated with the EM+JES calibration. Even choosing a high efficiency operating point increases the sample response significantly, particularly at low $p_T$, compared to the inclusive sample. The difference in response between light-quark and gluon jets is largest for the inclusive sample, and basically vanishes for the tightest operating point at high jet $p_T$. This is expected, since it is shown in Fig. 51 that applying a $n_{trk}$- and $w_{jet}$-based JES correction like GS removes the response differences between light-quark and gluon jets at high $p_T$. In addition, these jets are selected by the likelihood because they have quite similar (quark-jet-like) observable properties.

To gain confidence that the change in jet response does not affect analyses using the tagger, it is necessary to demonstrate that the agreement of the jet energy scale between MC simulations and data does not change when the likelihood cut corresponding to each operating point is applied. This is verified using the $\gamma$-jet balance technique described in Sect. 10, which finds changes of the data-to-MC agreement to be below 1%.

The same $p_T$-balancing technique allows for a study of the dependence of the JES on the tagger operating point in a specific sample, but not for an investigation of the light-quark and gluon jet responses directly. This is controlled through the sample- and flavour-dependent systematic uncertainties described

in the previous section and summarised in Eq. (18). The first term in this equation is based on the differences between light-quark and gluon JES, which become smaller when the tagger is used, as shown in Fig. 52. The second term is calculated comparing HERWIG++ and PYTHIA in the dijet sample. Both comparisons are performed for tagged jets, and they demonstrate that these uncertainties are actually smaller after the application of the tagger than before. The use of the uncertainties derived in the previous section is thus conservative for tagged jets, and the validation in the gluon jet sample is sufficient.

## 18.4 Summary of the jet flavour dependence analysis

The dependence of the jet energy scale on the flavour of the originating parton of the jet is evaluated in MC simulations. This difference, which enters the JES systematic uncertainty, is shown to be sensitive to certain details of the modelling of the decay and fragmentation of jets in the MC generators. An additional term is derived that needs to be added to the JES uncertainty to account for this dependence. It amounts to about 3% in a sample with a 75% gluon content (close to the inclusive jet sample) and is reduced to about 1% in a sample with 25% gluon content at low $p_T$ when using the EM+JES calibration scheme. The uncertainty at high $p_T$ is smaller than 1%. This contribution to the JES uncertainty can also be reduced by a factor of two or more when using the more sophisticated calibration schemes and is included as a part of the combined ATLAS jet energy scale uncertainty.

The flavour dependence of the JES arises to a great extent from differences in observable properties of jets, such as the number of tracks and the jet width measured with tracks. These properties can be used to reduce this dependence, as well as to discriminate between light-quark and gluon jets. The properties are used in ATLAS to build a quark/gluon jet tagger exploiting the differences in flavour composition between an inclusive jet and a $\gamma$-jet sample, in data as well as in MC simulations. The JES dependence on the choice of operating point used in the tagger yields a data-to-MC difference of less than 1%. Furthermore, the sample dependent uncertainties become smaller once jets are tagged, since the fragmentation is constrained to a specific phase space for which differences between light-quark and gluon jets between different MC generator models are smaller.

## 19 Jets with heavy-flavour content

In this section the measurement of the jet energy is studied for jets from heavy-flavour decays. The main observable used in the corresponding analysis based both on MC simulations and in situ techniques is the ratio $r_{trk}$ of the sum of transverse momentum vectors $\vec{p}_T^{\text{track}}$ from all tracks in the jet cone to the calorimeter jet transverse momentum $p_T^{\text{jet}}$,

$$r_{\text{trk}} = \frac{|\sum \vec{p}_T^{\text{track}}|}{p_T^{\text{jet}}}. \qquad (19)$$

These studies assess the jet energy measurement in the calorimeter in light-jet-enriched samples as well as for b-jet-enriched
<figure><img src="image_93.png" /><figcaption>Fig. 52: Jet $p_T$ response for the two leading jets in the dijet sample for different tagger light-quark operating points for jets with (a) $40 \text{ GeV} < p_T < 60 \text{ GeV}$ and (b) $260 \text{ GeV} < p_T < 310 \text{ GeV}$, and $|\eta_{\text{det}}| < 0.8$. Jets are labelled as light quark or gluon using the MC-simulation record and are further required to be isolated.</figcaption></figure>

samples in an inclusive jet sample and in an event sample where a top-quark pair is produced ($t\bar{t}$). The uncertainty on the b-jet energy measurement is thus evaluated over a wide range of $p_T$ and under different background conditions. Furthermore, the $p_T$ imbalance in a dijet system is used to validate the description of the kinematics of the neutrino coming from b-quarks decaying semileptonically in the MC simulation.

In the following jets originating from a b-quark (b-jets) and identified by means of b-tagging techniques are referred to as “b-tagged jets”. The notation “inclusive jets” is used to denote a mixture of jets initiated by light quarks, b-quarks, and gluons. All types of jets originating from b-quarks, including those containing semileptonic b-quark decays, are referred to as “inclusive b-jets”.

Since an unbiased sample of jets induced by charm quarks can not be selected in the data, no dedicated studies for charm jets have been performed. Charm jets are considered to be light jets and are treated as described in Section 18.

## 19.1 Jet selection and response definition

Jets with a calibrated transverse momentum $p_T^{\text{jet}} > 20 \text{ GeV}$ and a pseudorapidity $|\eta| < 2.5$ are used in this study.

Two aspects of the jet energy scale are studied separately: the response to particles absorbed in the calorimeter and the detector response to all produced particles including muons and neutrinos. The former is characterised by the calorimeter response $\mathcal{R}^{\text{calo}} = p_T^{\text{jet}}/p_T^{\text{truth}}$, where $p_T^{\text{truth}}$ is the $p_T$ of a matched truth jet built from stable final-state particles, as defined in Sect. 5.5, with the exclusion of muons and neutrinos. The latter is characterised by the all-particle response $\mathcal{R}^{\text{all}} = p_T^{\text{jet+}\mu}/p_T^{\text{truth,all}}$,

where $p_T^{\text{jet+}\mu}$ includes selected reconstructed muons inside the jet and $p_T^{\text{truth,all}}$ is the $p_T$ of a matched truth jet built from all stable final-state particles.

The jet energy scale of b-tagged jets in the dijet sample is studied using different b-tagging algorithms. For each algorithm, different operating points resulting in different efficiencies and purities are studied, as detailed in Sect. 19.3. In the MC simulation, the flavour of jets is determined as described in Ref. [92], by the presence of a heavy-flavour quark matched geometrically to the reconstructed jet, using the distance $\Delta R$ in $(\eta, \phi)$ space, see Eq. (3) in Sect. 5.6.

In the $t\bar{t}$ sample b-tagged jets are selected by means of the MV1 tagger [92]. The MV1 tagger uses the results from three b-tagging algorithms exploiting secondary-vertex and track impact-parameter information, which are input to a neural network to derive a likelihood discriminant to select b-jets. In this analysis, a jet is experimentally identified as a b-jet if the MV1 tagger weight ($w_{\text{MV1}}$) exceeds a threshold value of 0.6. This corresponds to 70% per-jet efficiency for selecting b-jets from $t\bar{t}$ decays, and a per-jet rejection factor for light-quark jets of about 130. To adjust the MC simulations to the b-tagging performance in data, a dedicated b-tagging efficiency correction [92] is applied to the simulation and the related systematic uncertainties are evaluated.

The influence of nearby jets on the measurements is studied by applying an isolation requirement which rejects jets that are separated from the nearest other jet by a distance $\Delta R < 2R$. The influence of this requirement is found to be negligible in the analyses presented, so the requirement is omitted in the results shown.
The jet vertex fraction JVF introduced in Sect. 8.2.3 is used to quantify the amount of energy in a jet coming from pile-up interactions.

## 19.2 Track selection

Tracks are associated to jets by requiring that the opening angle between the track and the jet direction be $\Delta R(\text{jet, track}) < 0.4$, measured in $(\eta, \phi)$ space. Tracks are required to pass the track selection criteria presented in Sect. 5.4 in the context of track jets. This assures an appropriate reconstruction quality and that the selected tracks come from the primary hard-scattering vertex.

## 19.3 Event selection

Events are initially selected by means of single-jet and single-lepton triggers. A primary vertex reconstructed from at least five tracks, which is consistent with the position and transverse size of the beam, is required. Analysis specific selections are described below.

### 19.3.1 Jet sample selection

Four complementary event selections are used for studies in the dijet sample:

1. An inclusive selection is used to study the energy calibration in the inclusive jet sample, and uses 11 single-jet triggers to cover the full $p_T$ range, to cope with the reduced data rate allowed for lower-$p_T$ triggers.

2. Two b-tagged jet selections are used to study the energy calibration of b-jets.

(a) An inclusive b-tagged sample is selected using five different single-jet triggers, since the range of $p_T$ for b-jet studies is limited by the low trigger rates at low $p_T$ and by the measurements of b-tagging efficiencies at high $p_T$.

(b) A semileptonic b-tagged sample is selected using a single muon-jet trigger, requiring a muon candidate inside a jet, which is less heavily pre-scaled, increasing the size of the sample collected with respect to a sample collected with a single-jet trigger.

3. A dijet selection is used to study the impact of semileptonic decays into muons and neutrinos.

Only one trigger is used to collect events in a specific $p_T$ bin. This procedure is found to be compatible within statistical uncertainties with a procedure that combines all jet triggers in each $p_T$ bin by weighting contributing events according to the integrated luminosity collected by the trigger that allowed the event to be recorded.

The measurement in the dijet sample is performed as a function of the average $p_T$ ($p_T^{\text{avg}}$) of the two leading jets, including the muon candidate if one is reconstructed inside the jet. The estimated muon energy loss in the active layers of the calorimeter is subtracted to avoid double counting.

The measurement in the inclusive samples is performed as a function of $p_T^{\text{jet}}$. The dijet event selection further requires:

1. At least two jets are reconstructed with $p_T^{\text{jet}} > 20$ GeV, $|\eta| < 1.2$ and $|\text{JVF}| > 0.75$.

2. The two leading (in $p_T$) jets are b-tagged with the MV1 algorithm ($w_{\text{MV1}} > 0.6$).

3. At least one of the jets with a muon candidate within $\Delta R < 0.4$ passes the selection described in Ref. [92].

4. No third-leading jet reconstructed in the event with $|\text{JVF}| > 0.6$ and $p_T^{\text{jet}} > \max(12 \text{ GeV}, 0.25 \cdot p_T^{\text{avg}})$.

5. The azimuthal distance between the two leading jets is $\Delta\phi_{jj} > 2.5$.

The selection on the inclusive samples requires at least one jet with $p_T^{\text{jet}} > 25$ GeV and $|\eta| < 2.5$, and the $|\text{JVF}| > 0.75$ cut. The muon selection is unchanged and different b-tagging algorithms and operating points are studied, since the neutrino energy is expected to be largely independent of the tagging algorithm, while JES is not.

The b-jet purity of these samples is measured with MC simulations to vary from 50% to 70% for the inclusive selection, 60% to 80% for the semileptonic selection, and to be above 80% for the dijet selection for the operating points studied. Observations at high $p_T \gtrsim 200$ GeV suggest that the purity might be underestimated by as much as 10% [97]. Uncertainties on the efficiency of the tagging algorithm to identify b-jets and c-jets can also impact these purity estimates by up to about 10% [92]. Despite these systematic effects, the purity of these samples remains sufficiently large for the validation purposes of this study.

### 19.3.2 Top-quark pair sample selection

Top-quark pair events where one of the W bosons produced by the top-quark decays to an electron or a muon are selected by the following requirements (see Ref. [45] for further details):

1. A single-lepton trigger is present.

2. Exactly one electron with transverse energy above 25 GeV, within pseudorapidity range of $|\eta|$ less than 2.47, and outside the region of transition between the barrel and the endcap calorimeters, $1.37 \le |\eta| < 1.52$ is reconstructed; or, exactly one muon with transverse momentum above 20 GeV is reconstructed within $|\eta| < 2.5$. The reconstructed charged lepton has to match the trigger object corresponding to the required triggers that passed.

3. For the $t\bar{t} \to e^+\mu^-$ channel the transverse W boson mass $m_{T(W)}$, reconstructed from the electron and $E_T^{\text{miss}}$, should be $m_{T(W)} > 25$ GeV, with $E_T^{\text{miss}} > 35$ GeV. Alternatively, for the $t\bar{t} \to \mu^+\mu^-$ channel, $E_T^{\text{miss}} > 25$ GeV and $E_T^{\text{miss}} + m_{T(W)} > 60$ GeV are required.

4. At least four jets with $p_T^{\text{jet}} > 25$ GeV, $|\text{JVF}| > 0.75$, and $|\eta| < 2.5$ are required. Among these, at least two jets should be b-tagged using the MV1 b-tagging algorithm ($w_{\text{MV1}} > 0.6$).

After this selection the background contamination in the $t\bar{t}$ sample is expected to be of order 10% and to mainly consist of events from W/Z+jets and single top-quark production. The contribution from multijet background after the requirement of two b-tagged jets is expected to be about 4%. The background
contamination in the selected data sample has no sizable impact in the studies performed, and it is considered as an additional systematic uncertainty.

## 19.4 MC-based systematic uncertainties on the calorimeter b-jet energy scale

The uncertainties on the b-jet transverse momentum measurement are studied using systematic variations in the MC simulation. The b-jet can be either reconstructed using a calibration with respect to all stable particles to study the all-particle energy scale, or excluding muons and neutrinos to study the calorimeter energy scale, as described in Sect. 5. The former definition is currently most relevant for b-tagging calibration analyses [92], and further discussed in Sect. 19.8.

The uncertainty in the calorimeter response to b-jets can be estimated using a combination of different MC simulations as reported in Ref. [3]. Figure 53(a) shows the calorimeter response to b-jets for various MC simulations.

The corresponding systematic uncertainties associated with the b-jet energy measurement are shown in Fig. 53(b). These uncertainties need to be considered in addition to those established for an inclusive jet sample, since b-jet specific effects are not taken into account in that analysis. These uncertainties can be applied to any sample of b-jets, whether a specific analysis uses tagging or not, and are of a size comparable to the uncertainties in the in situ measurements presented later in this paper.

Two key changes are made in this analysis with respect to what is reported in Ref. [3]. The dead material uncertainty, which is large in Fig. 53(a), but does not contribute significantly to the systematic uncertainty reported in Fig. 53(b), is calculated as an additional change in the response expected from dead material effects for a b-jet sample with respect to an inclusive sample (or a pure light-quark sample for comparable results). This is possible in 2011 because in situ jet energy scale corrections and uncertainties exist which are already accounting for a potential mis-modelling of the dead material in the MC simulation. The uncertainty component derived from the propagation of single-particle uncertainties to jets is also removed, while it contributes 0.5% in 2010 data. This result relies again on in situ studies, since differences in the calorimeter response between data and MC simulations are already taken into account in those studies. Residual effects that could give rise to an additional systematic uncertainty component for b-jets are constrained using a single-particle evaluation and are shown in Sect. 21.

## 19.5 Calorimeter jet energy measurement validation using tracks

The calorimeter jet energy scale can be probed by comparing the measured jet energy to that of a well-calibrated reference object with independent systematic uncertainties. Charged-particle tracks are well measured with uncertainties independent of the calorimeter, and can be associated with jets, are used here. The mean value of $r_{\text{trk}}$, defined in Eq. (19) is primarily sensitive to the particle composition of the jet and thus should be

well described by any well-tuned event generator. In computing $\langle r_{\text{trk}} \rangle$ it is important to truncate the $r_{\text{trk}}$ distribution (here with $r_{\text{trk}} < 3$) to avoid contributions from fake tracks with unphysically large $p_T$.

To verify the description of the calorimeter energy measurement in MC simulations, the double ratio of the charged-to-total momentum obtained in data to that obtained in Monte Carlo simulation is studied:

$$R_{r_{\text{trk}}} \equiv \frac{\langle r_{\text{trk}} \rangle_{\text{Data}}}{\langle r_{\text{trk}} \rangle_{\text{MC}}}. \qquad (20)$$

The ratio is evaluated for inclusive jets ($R_{r_{\text{trk,inclusive}}}$), b-tagged jets ($R_{r_{\text{trk,b-jet}}}$) and b-tagged jets with a reconstructed muon inside ($R_{r_{\text{trk,b-jet}}}^{\mu\nu}$, in the dijet sample only). The calorimeter response ratio $R'$ of b-tagged jets relative to inclusive jets is then defined using Eq. (20) from each respective sample,

$$R' \equiv \frac{R_{r_{\text{trk,b-jet}}}}{R_{r_{\text{trk,inclusive}}}}. \qquad (21)$$

This ratio is used to test the relative systematic uncertainty between b-tagged and inclusive jets. In the $t\bar{t}$ sample, where the fraction of b-jets is large ($\approx 50\%$), the light jets (non b-tagged) component is used in the denominator instead of the inclusive one. It is mainly comprised of jets from the W boson decay but also to a lesser extent of gluon jets from initial- and final-state radiation. As a consequence, when comparing the results obtained in the $t\bar{t}$ and the dijet analyses, the difference in terms of jet flavour components entering the calculation of $R_{r_{\text{trk,inclusive}}}$ needs to be taken into consideration.

## 19.6 Systematic uncertainties

Systematic uncertainties in the $r_{\text{trk}}$ measurement arise from the modelling of the jet (and b-jet) fragmentation, b-tagging calibration, jet resolution and track reconstruction efficiency. In addition, for high-$p_T$ jets ($p_T > 500$ GeV) an efficiency loss in the tracking in the jet core is observed in MC simulations, and a systematic uncertainty is added to account for potential mis-modelling of this effect. These uncertainties are assumed to be uncorrelated. The resulting fractional systematic uncertainties on $r_{\text{trk}}$ and $R'$ are shown in Figs. 54(b), 54(d), and 54(f) for the inclusive jet sample, and in Figs. 55(b), 55(d), and 55(f) for the $t\bar{t}$ sample. They are determined as follows.

### 1 MC generator and tunes

These systematic uncertainties capture the effects of differences in $p_T^{\text{track}}$ caused by different fragmentation models. Differences in the calorimeter response, caused by the different particle spectra, can also impact the $r_{\text{trk}}$ measurement in certain MC simulations and should not be part of the uncertainty, since such shifts are measurable in the data. The $r_{\text{trk}}$ distribution is, thus, calculated from the various samples described in Sect. 3 using $p_T^{\text{truth}}$ in the denominator, even though only small differences are observed in most samples when including calorimeter effects, i.e. using the jet $p_T$ reconstructed with the calorimeters ($p_T^{\text{calo}}$).

In the top pair analysis, differences between MC@NLO and POWHEG+HERWIG are considered as process or generator systematic uncertainties. Fragmentation and decay
<figure><img src="image_94.png" /><figcaption>Fig. 53: Average response to b-jets in some of the different samples used to calculate the b-jet energy scale systematic uncertainties is depicted in (a). The resulting uncertainties in the ratio of the b-jet response to the response of jets in an inclusive sample are shown in (b). These results are obtained for b-jets built with the anti-$k_t$ algorithm with resolution parameter $R = 0.4$.</figcaption></figure>

systematic uncertainties are evaluated taking the difference between PYTHIA and HERWIG. In the dijet analysis, differences between PYTHIA and HERWIG++ set the systematic uncertainties from uncertainties in the decay models. The updated fragmentation tune in HERWIG++ prevents this comparison from being a conservative measure of the b-jet fragmentation systematic uncertainties. These are evaluated using comparisons to the Bowler-Lund [98] and Professor tunes [99].

## 2 b-tagging calibration

The scale factors that correct the b-tagging efficiencies in MC simulations to match the measured values are varied within their total uncertainty.

## 3 Material description

The knowledge of the tracking efficiency modelling in MC simulations is evaluated in detail in Ref. [100]. The systematic uncertainty on the tracking efficiency for isolated tracks increases from 2% ($|\eta^{\text{track}}| < 1.3$) to 7% ($2.3 \le |\eta^{\text{track}}| < 2.5$) for tracks with $p_T > 500$ MeV. The resulting effect on $r_{\text{trk}}$ is about 3% for $0 \le |\eta| < 2.1$ and about 4% for $2.1 \le |\eta| < 2.5$.

## 4 Tracking in jet core

High track densities in the jet core influence the tracking efficiency due to shared hits between tracks, fake tracks and lost tracks. The number of shared hits is well described in the MC simulation. The $p_T$ carried by fake tracks is negligible. A relative systematic uncertainty of 50% on the loss of efficiency obtained in the simulation is assigned to account for potential mis-modelling of this effect.

## 5 Jet energy resolution

The jet energy resolution in MC simulations is degraded by about 10%.

## 6 Background contamination

For the $t\bar{t}$ sample the analysis is repeated including the expected background contamination (except the multijet contribution) and the full difference is taken as an estimate of the systematic uncertainty.

The dominant contributions to the systematic uncertainty in the $t\bar{t}$ analysis are due to variations in the detector material and fragmentation/decay models. In the dijet sample, the material, fragmentation and decay uncertainties also dominate the systematic uncertainties, except at $p_T \gtrsim 500$ GeV where the uncertainty caused by the loss of efficiency in the jet core dominates. In Fig. 55, the contributions to the total systematic uncertainty due to the jet resolution, b-tagging calibration, background contamination and due to the modelling of the initial- and final-state radiation are labelled as “other” systematic uncertainties.

For $R'$, the tracking components (the material description, impacting the tracking efficiency) of the systematic uncertainty entering both the numerator and denominator are correlated and thus approximately cancel. A similar consideration holds for the jet energy resolution. The most significant systematic uncertainties on $R'$ are due to the choice of the MC generator and the fragmentation and decay models.
<figure><img src="image_95.png" /><figcaption>Fig. 54: Ratio of the average $r_{\text{trk}}$ given in Eq. (19) in data and MC simulations for (a) inclusive jets and (c) tagged b-jets. In (e), the b-tagged to inclusive sample ratio variable $R'$ from Eq. (21) is shown. The contributions of the systematic uncertainties to the total uncertainty in the different measurements are shown in (b), (d), and (f), respectively. Jets within $|\eta| < 1.2$ are used.</figcaption></figure>
<figure><img src="image_96.png" /><figcaption>Fig. 55: Ratio of the average $R_{\text{trk}}$ given in Eq. (19) in $t\bar{t}$ events in data and MC simulations for (a) light-jets and tagged (c) b-jets. In (e), the ratio of $R_{\text{trk}}$ from Eq. (20) between the b-jet and the light-jet sample is shown. The total systematic uncertainty is shown as a band, and the dotted lines correspond to unity and the 2.5% deviation from unity. The contributions of the systematic uncertainties to the total uncertainty in the different measurements are shown in (b), (d), and (f), respectively. The contributions to the total systematic uncertainty due to the jet resolution, b-tagging calibration, background contamination and the modelling of the initial- and final-state radiation are grouped under “Other systematics”. Jets with $|\eta| < 1.2$ are used.</figcaption></figure>
<figure><img src="image_97.png" /></figure>

Fig. 56: Average jet response as a function of true transverse momentum of jets built using all stable particles, for a sample of inclusive jets (solid circles), a sample of b-jets tagged with the MV1 tagging algorithm (open circles) and a sample of semileptonically decaying b-jets with a reconstructed muon inside (open squares), is shown in (a). The resulting semileptonic correction, as a function of calorimeter jet $p_T$, used to transform the $p_T$ of a jet in the semileptonic sample to the $p_T$ of a jet in an inclusive sample of b-jets, is displayed in (b). Associated systematic uncertainties are shown around the central value, and the combined uncertainty is shown as a coloured band.

## 19.7 Results

Figures 54(a) and 54(c) show the ratio of the average of the $r_{\text{trk}}$ distribution in data and MC simulations for jets in the inclusive jet sample with $|\eta| < 1.2$. Figures 54(b) and 54(d) show the different components of the associated systematic uncertainty, as discussed in Sect. 19.6.

The study in the sample without b-tagging covers up to approximately 2 TeV, and provides a cross check over almost the full range of calibrated $p_T$ studied in situ through the analyses used to establish the systematic uncertainty on the jet energy scale in ATLAS. No $p_T$ dependence is observed and agreement is found between data and MC simulations within systematic uncertainties. Similar results are found in higher $|\eta|$ regions.

Agreement of the MC simulations with the data for the $r_{\text{trk}}$ measurements is found within systematic uncertainties across all $p_T$ for inclusive jets and for $p_T^{\text{jet}} < 400$ GeV for b-tagged jets. The relative response $R'$ between b-tagged and inclusive jets is shown in Fig. 54(e) and the uncertainty band corresponds to the relative b-jet energy scale uncertainty with respect to the inclusive jet sample. Figure 54(f) shows the different components of the associated systematic uncertainty. A difference between data and MC simulations is found but almost covered by the systematic uncertainties. This difference is partially caused by the overall 1% shift found in the inclusive sample. Similar results are found in the sample of b-jets decaying to muons selected in the dijet sample, with a larger difference between data and MC simulations of up to 4% in the lowest $p_T$ bin probed.

However, the uncertainties in the modelling are also somewhat larger, limiting the constraints on the jet energy scale of these jets to approximately 3%.

The corresponding results from the same study performed in the $t\bar{t}$ sample are shown in Fig. 55.

The results in this sample are consistent with those obtained in the dijet sample, except for the better agreement between data and MC simulations in the light-jet sample, which also leads to better agreement in the b-jet to light-jet sample results. The systematic uncertainties are also comparable, despite the different methods used in their evaluation. The uncertainty in the in situ technique used to assess the b-jet energy scale is estimated to be approximately 2.5% and 3% in the ranges $|\eta| < 1.2$ and $1.2 \le |\eta| < 2.5$, respectively, for jets with $p_T^{\text{jet}} < 400$ GeV from these studies.

## 19.8 Semileptonic correction and associated uncertainties

The study of the all-particle response $\mathcal{R}^{\text{all}}$ of b-jets, i.e. the energy scale calculated with respect to jets built using all stable particles, is also necessary for many analyses, given that about 40% of b-jets decay semileptonically, thus having a non-negligible amount of their energy carried by neutrinos. In particular, the study of the b-tagging efficiency in a sample of b-jets decaying semileptonically to muons [92] requires a correction that maps the all-particle jet energy scale of that sample
to that of an inclusive sample of b-jets. This correction and its systematic uncertainties are estimated in this section. The correction also has applications beyond the b-tagging calibration since it can also be used to improve the reconstruction of b-jets identified as semileptonic. The study of the all-particle energy scale in this section is performed independently of the study of the calorimeter energy scale, even though the two are not straightforward to decouple in in situ studies.

Figure 56(a) shows the all-particle response for an inclusive jet sample, a sample of b-jets tagged with the MV1 algorithm and a sample of b-jets containing a muon from a semileptonic b decay. The semileptonic b-jets sample is selected using hadron-level information, and no b-tagging is imposed. However, the muon is required to pass kinematic and quality cuts detailed in Ref. [92]. The effect of neutrinos is clearly visible in both the tagged b-jets sample and more significantly in the semileptonic b-jets sample. The increase at low $p_T$ in the semileptonic sample arises from biases created by the muon kinematic cuts.

The response of semileptonically decaying b-jets is corrected to that of an inclusive b-tagged jet sample. The correction is constructed using techniques similar to those used in the EM+JES calibration introduced in Sect. 5. This correction is shown in Fig. 56(b), as a function of calibrated jet $p_T$ for fixed muon $p_T$ and jets with $|\eta| < 0.8$. The correction is not explicitly dependent on $p_T^\mu$ even though it enters in the calculation of the reconstructed jet $p_T$ used to compute the correction.

Systematic uncertainties in this correction need to account for our knowledge of b-jet fragmentation and decay, as well as the effect of the muon spectrum and muon reconstruction. These uncertainties are presented in Ref. [92]. Since only one correction is calculated and used for all tagging algorithms and operating points commissioned up to date, an additional systematic uncertainty that covers the spread of the corrections for all these different operating points is added. All uncertainties are combined in quadrature. Only the most significant uncertainties are included in Fig. 56(b), namely the uncertainty that arises from the different correction for different operating points, and the uncertainty that arises from the limitations in the knowledge of the muon momentum spectrum in the centre-of-mass energy of the decaying hadron. These uncertainties are estimated by reweighting that spectrum to match a measurement obtained in $e^+e^-$ scattering [101]. Due to the significant differences between that spectrum and the one found in PYTHIA, these variations are considered sufficient. All other uncertainties are combined and shown in the figure under the same curve.

The uncertainty is about 1.5% for most $p_T$ values in the central region, except at low $p_T$ where it increases to about 4%. The behaviour is similar at larger $\eta$, except in the most forward bin ($2.1 < |\eta| < 2.5$), where variations across tagging operating points cause the uncertainty to increase to about 2%.

## 19.9 Semileptonic neutrino energy validation using dijet balance

The modelling of the energy carried by the neutrino in the inclusive b-jet sample and in the semileptonic b-jet sample can be validated using the $p_T$ balance of a dijet system. The same

<figure><img src="image_98.png" /><figcaption>Fig. 57: Relative response of the semileptonic sample with respect to the inclusive b-jet sample as calculated from the dijet $p_T$ asymmetry. The uncertainty band around the data denotes systematic uncertainties in the asymmetry measurement.</figcaption></figure>

technique is used in Ref. [3] to validate the variation of the calorimeter response as a function of different jet properties. The response in data is calculated using the asymmetry in the jet $p_T$ of the two jets in the dijet system. The two jets are required to be b-tagged, and the probe jet is required to have a selected reconstructed muon within $\Delta R < 0.4$. The relative response, calculated from the asymmetry, is sensitive to the energy carried by the neutrino, but also to the response differences between the b-tagged and semileptonic b-jet samples. These differences, however, are well modelled in the MC simulation, as shown in Sect. 19.7.

Figure 57 shows the relative response of semileptonic b-jets with respect to inclusive b-jets obtained in data and MC simulations using dijet balance.

The presence of neutrinos in the b-jet decay causes the estimated relative response to be below 1. The uncertainty band around the data represents systematic uncertainties in the imbalance. These are calculated through variations in the soft-radiation cut in the selection (i.e. the $p_T$ used for the veto on the third leading jet) as Section 8.4. An additional contribution to the uncertainty is added to the first $p_T$ bin to account for differences between data and MC simulations in the turn-on of the efficiency curve for the muon-jet trigger used in this analysis. Agreement is found between data and MC simulations, validating the description of this process that is exploited to develop the semileptonic correction presented in the previous Sect. 19.8.

## 19.10 Conclusions on heavy-flavour jets

The uncertainty on the jet energy measurement is studied for light jets as well as inclusive and semileptonic b-jets. In the inclusive jet sample the jet energy scale is probed using tracks associated with jets over a wide range of jet $p_T$. Comparisons between data and MC simulations show agreement within sys-
tematic uncertainties of approximately 3% with weak dependence on the transverse momentum of the jets. The b-jet energy scale is also probed using tracks associated with b-tagged jets in the data. The results in the $t\bar{t} \to l^+l^-$ jets and inclusive jet samples suggest that the jet energy scale of b-jets is well described by the MC simulation, within systematic uncertainties of about 2% to 3%.

In the MC simulation a correction for semileptonic b-jets decaying to muons is derived, which adjusts the transverse momentum measurement to that in an inclusive sample of b-jets. The systematic uncertainties on this correction are also derived using MC simulations. They are found to be about 2%. The uncertainty in the jet energy measurement due to effects specific to b-jets is also determined using Monte Carlo simulations. This uncertainty ranges from 1% to 3%.

The energy scale of semileptonic b-jets decaying to muons is probed in the dijet sample in parallel with a study of the energy carried by the accompanying neutrino. The latter confirms the results found in MC simulations within systematic uncertainties of about 3%.

## 20 Jet response in problematic calorimeter regions

<figure><img src="image_99.png" /><figcaption>Fig. 58: Average relative response of the probe jets with respect to the tag jets as a function of various impact points in the azimuthal direction ($\phi$) of the probe jet. The average $p_T$ of the two leading jets is $300 \le p_T^{\text{avg}} < 400$ GeV. The vertical solid lines indicate the location of the Tile calorimeter modules. The non-operating Tile calorimeter module is at $\phi = 1.03$. The markers indicate the results for no correction (triangles), the cell-based corrections (squares) and the corrections based on the jet shape (circles). The lower part of the figure shows the respective average values of the two corrections as a function of the azimuthal angle of the probe jet.</figcaption></figure>

At the end of the 2011 data taking period 11 out of 256 modules of the ATLAS central hadronic Tile calorimeter were not operational. Moreover, during the data taking, some Tile calorimeter modules occasionally became non-operational for short periods of time, e.g. due to trips of the high voltage. In this section the impact of non-operating Tile modules on the jet energy measured is studied using a tag-and-probe technique based in the $p_T$ balance of the two leading jets in the event following Sect. 8.1.1. The response of the tag jet, required to be in a fully operational part of the calorimeter, is used to test the response of a probe jet that impinges close to and in the region of the non-operating Tile module.

The performance of two reconstruction algorithms that correct for non-operating parts of the calorimeters based on the energy deposits in nearby cells or the average transverse jet shape is assessed.

# 20.1 Correction algorithms for non-operating calorimeter modules

## 20.1.1 Correction based on calorimeter cell energies

This correction is implemented in the standard ATLAS calorimeter energy reconstruction. It estimates the energy density of a non-operating Tile calorimeter cell on the basis of energy measured by the two neighbouring cells that belong to the same Tile calorimeter layer sub-detector as the non-operating cell. The energy density of the non-operating cell is estimated as the average (arithmetic mean) of the energy density of the neighbouring cells. This correction is called BCHcor,cell correction in the following.

## 20.1.2 Corrections based on jet shapes

This correction is applied after jet reconstruction. The expected average jet shape is used to estimate the energy deposited in the non-operating Tile calorimeter cells. The correction is derived from MC simulations where all calorimeter modules are operational. It is calculated as a function of the transverse momentum and the pseudorapidity of the jet, the calorimeter type, the calorimeter layer and the angular distance between the jet axis and the cell centre in the $(\eta, \phi)$ space ($\Delta R$ in Eq. (3) in Sect. 5.6). It is applied for both LAr and Tile calorimeter cells and is called BCHcor,jet in the following.

In predefined bins of $p_T^{\text{jet}}$ and $\eta_{\text{det}}$ and for all the calorimeter cells that belong to the jets the average relative energy (defined as $E_{\text{cell}}/E_{\text{jet}}$) in each calorimeter type, layer and $dR$ bin is calculated. For all non-operational calorimeter cell in a jet the following correction is calculated:

$$ \text{BCH}_{\text{cor,jet}} = \sum_{\text{bad cells}} \frac{E_{\text{cell}}}{E_{\text{jet}}} $$

and the energy of the jet is corrected with:

$$ E_{\text{jet}}^{\text{corrected}} = \frac{E_{\text{jet}}^{\text{uncorrected}}}{1 - \text{BCH}_{\text{cor,jet}}} $$
## 20.2 Performance of the bad calorimeter region corrections

The performance of the correction methods can be assessed using a tag-and-probe technique in events with two jets with high transverse momentum. The dependence of the relative jet response between the tag and the probe jets is studied as a function of the azimuthal angle of the probe jet.

The tag jet is selected such that it hits a fully operating part of the ATLAS calorimeter and is inside a central $\eta$ region ($|\eta| < 1.6$). Jets in the gap between Tile Long Barrel and Tile Extended Barrel (i.e. jets with axes pointing to the region $0.8 \le \eta < 1.2$) are excluded. The probe jet is chosen such that its axis points to the vicinity of the non-operating Tile module. Only probe jets with $0.1 \le |\eta| < 0.8$ are used.

Figure 58 shows the jet response of the probe jet in the region of a missing Tile module and in the neighbouring regions for events where the average jet $p_T$ of the two leading jets is between 300 and 400 GeV. A decrease of the probe jet response by about 15% is observed in the region with the non-operating Tile calorimeter module when no correction is applied. This reduces to only about 10% for the cell-based correction. However, an overcorrection by about 10% is observed in the vicinity of the region with the missing Tile module. The correction based on the jet shape performs much better. There is no overcorrection in the vicinity of the problematic module and the probe jet energy is compensated much better if the jet axis falls into the module. There is only a small overcorrection by a few percent in the vicinity of the non-operating module.

### 20.2.1 Conclusion on bad calorimeter regions

The corrections for missing Tile calorimeter modules show a good performance. The average jet response variations close to the missing calorimeter are evaluated with a tag-and-probe technique in data. The jet response variation is about 5-10%. The correction using jet shape information shows a better performance than the correction simply averaging the energy deposition in the neighbouring calorimeter cells.

The Monte Carlo simulation includes the missing Tile calorimeter modules and describes the jet response variations in data. The remaining differences are included in the JES uncertainty derived from the in situ techniques.

# 21 Summary of the total jet energy scale systematic uncertainty

Figures 59 and 60 show the fractional jet energy scale uncertainty from the in situ measurements as a function of $p_T^{\text{jet}}$ for four representative values of $\eta$, and as a function of $\eta$ for two representative values of $p_T^{\text{jet}}$. The total uncertainty is given by the absolute (JES) and the relative in situ calibration uncertainties added in quadrature. For jets in the central region it amounts to 3% at $p_T^{\text{jet}} \approx 17$ GeV, falling to 2% at $p_T^{\text{jet}} \approx 25$ GeV, and is below 1% for $55 \le p_T^{\text{jet}} < 500$ GeV. The uncertainty increases for forward jets ($|\eta| > 1.2$) due to the uncertainty on the modelling of the parton radiation altering the dijet $p_T$ balance

in the $\eta$-intercalibration technique. For very forward low-$p_T$ jets ($p_T \approx 25$ GeV, $|\eta| \approx 4$), the uncertainty can be as large as 6%. The in situ JES uncertainty is similar for the EM+JES and LCW+JES calibration schemes.

For jets with $p_T^{\text{jet}} > 1$ TeV the JES uncertainty is derived from single-hadron response measurements [4], given the large statistical error of the multijet balance technique beyond $p_T^{\text{jet}} > 1$ TeV. The uncertainties from the in situ techniques are kept fixed at $p_T^{\text{jet}} = 1$ TeV and subtracted in quadrature from the uncertainty of the single-hadron response measurements, which is the dominant contribution at high $p_T^{\text{jet}}$ in 2010 and 2011.

Table 14 presents a summary of the total in situ JES uncertainties in representative $\eta$ and $p_T^{\text{jet}}$ regions for anti-$k_t$ jets with $R = 0.4$ and $R = 0.6$ calibrated with the EM+JES and LCW+JES schemes.

The total in situ calibration uncertainty (labelled “baseline in situ JES”) together with the additional uncertainties that depend on the event sample used in the physics analysis is shown in Figs. 61 to 64 for two illustrative samples. The procedure to estimate those uncertainties<fnref n="19" /> is detailed in Sect. 18.

Figures 61 and 62 show the flavour response uncertainty and the flavour composition uncertainties for light jets in an event sample with top-quark pairs decaying semileptonically. Semileptonic decays are selected in the MC simulation samples based on truth information, and electrons are not considered as jets when estimating the jet response. The MC generator used to evaluate the sample response and the gluon fraction is MC@NLO, while the gluon fraction uncertainty is derived using the difference in gluon fractions between the ACERMC and POWHEG generators. The average gluon fraction uncertainty ranges from 2% to 10% depending on the jet transverse momentum and pseudorapidity. For differential measurements, the gluon fraction and its uncertainty can also be determined as a function of the property measured (e.g. number of jets). Figure 65 shows the total uncertainty for b-jets in the case of jets with $R = 0.4$ calibrated using the EM+JES and LCW+JES schemes.

Figures 63 and 64 show the flavour uncertainties for an event sample of inclusive jets. The sample response and gluon fraction are evaluated using the PYTHIA nominal sample, while the gluon fraction uncertainty is derived considering the average difference in the fraction of gluons between the PYTHIA nominal sample and samples producing using the POWHEG (interfaced with Pythia for parton showering and hadronisation) and the HERWIG++ generators. The gluon fraction uncertainty in the inclusive jet case is up to 7% but decreases rapidly with jet $p_T$ to less than 2%.

A conservative topology uncertainty due to close-by jets is shown assuming the presence of a close-by jet with $R_{\text{min}} = 0.7$. The pile-up uncertainties are given for the average conditions of $N_{\text{PV}} = 10$ and $\mu = 8.5$ in the 2011 dataset, with an RMS of 3 for both $N_{\text{PV}}$ and $\mu$.

The total uncertainty is calculated by adding all uncertainty sources in quadrature. The uncertainty for jets calibrated with

<footnote n="19">¹⁹ If no information on the fraction of gluons or its uncertainty is available for a given analysis sample, a gluon fraction of 50% with 50% uncertainty is used, representing an unknown flavour composition for the sample.</footnote>
<table><caption>Table 14: Summary of the in situ EM+JES and LCW+JES jet energy scale systematic uncertainties for different $p_T^{jet}$ and $|\eta|$ values for anti-$k_t$ jets with $R = 0.4$ and $R = 0.6$. These values do not include pile-up, flavour or topology uncertainties.</caption><thead><tr><th rowspan="2">|η| region</th><th colspan="5">Fractional EM+JES JES uncertainty for R = 0.4</th></tr><tr><th>p<sub>T</sub><sup>jet</sup> = 20 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 40 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 200 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 800 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 1.5 TeV</th></tr></thead><tbody><tr><td>|η| = 0.1</td><td>2.6%</td><td>1.2%</td><td>0.8%</td><td>1.3%</td><td>3.2%</td></tr><tr><td>|η| = 0.5</td><td>2.6%</td><td>1.2%</td><td>0.8%</td><td>1.3%</td><td>3.2%</td></tr><tr><td>|η| = 1.0</td><td>2.8%</td><td>1.4%</td><td>1.0%</td><td>1.3%</td><td>3.2%</td></tr><tr><td>|η| = 1.5</td><td>3.2%</td><td>2.0%</td><td>1.5%</td><td>1.4%</td><td>3.3%</td></tr><tr><td>|η| = 2.0</td><td>3.8%</td><td>2.9%</td><td>2.1%</td><td>1.6%</td><td></td></tr><tr><td>|η| = 2.5</td><td>4.3%</td><td>3.8%</td><td>2.8%</td><td></td><td></td></tr><tr><td>|η| = 3.0</td><td>4.7%</td><td>4.5%</td><td>3.4%</td><td></td><td></td></tr><tr><td>|η| = 3.5</td><td>5.1%</td><td>4.9%</td><td>4.6%</td><td></td><td></td></tr><tr><td>|η| = 4.0</td><td>5.7%</td><td>5.1%</td><td>4.9%</td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">|η| region</th><th colspan="5">Fractional LCW+JES JES uncertainty for R = 0.4</th></tr><tr><th>p<sub>T</sub><sup>jet</sup> = 20 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 40 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 200 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 800 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 1.5 TeV</th></tr></thead><tbody><tr><td>|η| = 0.1</td><td>2.4%</td><td>1.2%</td><td>0.8%</td><td>1.3%</td><td>3.2%</td></tr><tr><td>|η| = 0.5</td><td>2.5%</td><td>1.2%</td><td>0.8%</td><td>1.3%</td><td>3.2%</td></tr><tr><td>|η| = 1.0</td><td>2.6%</td><td>1.4%</td><td>1.1%</td><td>1.3%</td><td>3.2%</td></tr><tr><td>|η| = 1.5</td><td>3.1%</td><td>2.1%</td><td>1.7%</td><td>1.4%</td><td>3.3%</td></tr><tr><td>|η| = 2.0</td><td>3.9%</td><td>2.9%</td><td>2.6%</td><td>1.8%</td><td></td></tr><tr><td>|η| = 2.5</td><td>4.6%</td><td>3.9%</td><td>3.4%</td><td></td><td></td></tr><tr><td>|η| = 3.0</td><td>5.2%</td><td>4.6%</td><td>3.9%</td><td></td><td></td></tr><tr><td>|η| = 3.5</td><td>5.8%</td><td>5.2%</td><td>4.5%</td><td></td><td></td></tr><tr><td>|η| = 4.0</td><td>6.2%</td><td>5.5%</td><td>5.1%</td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">|η| region</th><th colspan="5">Fractional EM+JES JES uncertainty for R = 0.6</th></tr><tr><th>p<sub>T</sub><sup>jet</sup> = 20 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 40 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 200 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 800 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 1.5 TeV</th></tr></thead><tbody><tr><td>|η| = 0.1</td><td>2.7%</td><td>1.4%</td><td>0.8%</td><td>1.8%</td><td>3.3%</td></tr><tr><td>|η| = 0.5</td><td>2.7%</td><td>1.5%</td><td>0.8%</td><td>1.8%</td><td>3.3%</td></tr><tr><td>|η| = 1.0</td><td>2.8%</td><td>1.6%</td><td>0.9%</td><td>1.8%</td><td>3.3%</td></tr><tr><td>|η| = 1.5</td><td>3.0%</td><td>1.9%</td><td>1.3%</td><td>1.9%</td><td>3.3%</td></tr><tr><td>|η| = 2.0</td><td>3.6%</td><td>2.6%</td><td>1.9%</td><td>2.0%</td><td></td></tr><tr><td>|η| = 2.5</td><td>4.3%</td><td>3.4%</td><td>2.4%</td><td></td><td></td></tr><tr><td>|η| = 3.0</td><td>5.2%</td><td>4.1%</td><td>3.0%</td><td></td><td></td></tr><tr><td>|η| = 3.5</td><td>5.7%</td><td>4.7%</td><td>3.8%</td><td></td><td></td></tr><tr><td>|η| = 4.0</td><td>5.9%</td><td>4.8%</td><td>4.6%</td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">|η| region</th><th colspan="5">Fractional LCW+JES JES uncertainty for R = 0.6</th></tr><tr><th>p<sub>T</sub><sup>jet</sup> = 20 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 40 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 200 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 800 GeV</th><th>p<sub>T</sub><sup>jet</sup> = 1.5 TeV</th></tr></thead><tbody><tr><td>|η| = 0.1</td><td>2.3%</td><td>1.3%</td><td>0.8%</td><td>1.6%</td><td>3.2%</td></tr><tr><td>|η| = 0.5</td><td>2.3%</td><td>1.3%</td><td>0.8%</td><td>1.6%</td><td>3.2%</td></tr><tr><td>|η| = 1.0</td><td>2.4%</td><td>1.4%</td><td>1.0%</td><td>1.6%</td><td>3.2%</td></tr><tr><td>|η| = 1.5</td><td>2.7%</td><td>1.8%</td><td>1.6%</td><td>1.7%</td><td>3.2%</td></tr><tr><td>|η| = 2.0</td><td>3.3%</td><td>2.4%</td><td>2.2%</td><td>1.9%</td><td></td></tr><tr><td>|η| = 2.5</td><td>4.4%</td><td>3.3%</td><td>2.8%</td><td></td><td></td></tr><tr><td>|η| = 3.0</td><td>6.0%</td><td>4.6%</td><td>3.3%</td><td></td><td></td></tr><tr><td>|η| = 3.5</td><td>7.0%</td><td>5.6%</td><td>3.8%</td><td></td><td></td></tr><tr><td>|η| = 4.0</td><td>7.2%</td><td>6.0%</td><td>4.7%</td><td></td><td></td></tr></tbody></table>
<figure><img src="image_100.png" /><figcaption>Fig. 59: Fractional in situ jet energy scale systematic uncertainty as a function of ((a), (b)) $p_T^{\text{jet}}$ and ((c), (d)) jet pseudorapidity for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the EM+JES calibration scheme. The contributions from each in situ method are shown separately. Uncertainties from pile-up, flavour, and topology are not included.</figcaption></figure>
<figure><img src="image_101.png" /><figcaption>Fig. 60: Fractional in situ jet energy scale systematic uncertainty as a function of ((a), (b)) $p_T^{\text{jet}}$ and ((c), (d)) jet pseudorapidity for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the LCW+JES calibration scheme. The contributions from each in situ method are shown separately. Uncertainties from pile-up, flavour, and topology are not included.</figcaption></figure>
<figure><img src="image_102.png" /><figcaption>Fig. 61: Sample-dependent fractional jet energy scale systematic uncertainty as a function of ((a), (b)) $p_T^{\text{jet}}$ and ((c), (d)) jet pseudorapidity for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the EM+JES calibration scheme. The uncertainty shown applies to semileptonic top-decays with average 2011 pile-up conditions, and does not include the uncertainty on the jet energy scale of b-jets.</figcaption></figure>
<figure><img src="image_103.png" /><figcaption>Fig. 62: Sample-dependent fractional jet energy scale systematic uncertainty as a function of ((a), (b)) $p_T^{\text{jet}}$ and ((c), (d)) jet pseudorapidity for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the LCW+JES calibration scheme. The uncertainty shown applies to semileptonic top-decays with average 2011 pile-up conditions, and does not include the uncertainty on the jet energy scale of b-jets.</figcaption></figure>
<figure><img src="image_104.png" /><figcaption>Fig. 63: Sample-dependent fractional jet energy scale systematic uncertainty as a function of ((a), (b)) $p_T^{\text{jet}}$ and ((c), (d)) jet pseudorapidity for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the EM+JES calibration scheme. The uncertainty shown applies to inclusive QCD jets with average 2011 pile-up conditions, and does not include the uncertainty on the jet energy scale of b-jets.</figcaption></figure>
<figure><img src="image_105.png" /><figcaption>Fig. 64: Sample-dependent fractional jet energy scale systematic uncertainty as a function of ((a), (b)) $p_T^{\text{jet}}$ and ((c), (d)) jet pseudorapidity for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the LCW+JES calibration scheme. The uncertainty shown applies to inclusive QCD jets with average 2011 pile-up conditions, and does not include the uncertainty on the jet energy scale of b-jets.</figcaption></figure>
<figure><img src="image_106.png" /><figcaption>Fig. 65: Fractional jet energy scale systematic uncertainty as a function of $p_T^{\text{jet}}$ for anti-$k_t$ jets with distance parameter of $R = 0.4$ calibrated using the (a) EM+JES and (b) LCW+JES calibration schemes. The uncertainty shown applies to b-jets with average 2011 pile-up conditions.</figcaption></figure>

the LCW+JES scheme is significantly smaller than the one for the EM+JES scheme, mainly because this scheme reduces the sensitivity to the jet flavour.

## 22 Conclusions

The ATLAS jet energy scale (JES) and its systematic uncertainty are determined for jets produced in proton-proton collisions with a centre-of-mass energy of $\sqrt{s} = 7$ TeV using the full 2011 dataset that corresponds to an integrated luminosity of $4.7 \text{ fb}^{-1}$. Jets are reconstructed from clusters of calorimeter cells with the anti-$k_t$ algorithm with distance parameters $R = 0.4$ or $R = 0.6$. The uncertainty of the jet energy measurement is evaluated for jets with calibrated transverse momenta $p_T^{\text{jet}} > 20$ GeV and pseudorapidities $|\eta| < 4.5$ using a combination of in situ techniques exploiting the transverse momentum balance between a jet and a reference object.

For central jets ($|\eta| < 1.2$) with $20 \le p_T^{\text{jet}} < 800$ GeV, photons or Z bosons are used as reference objects. A system of low-$p_T$ jets is used to extend the JES validation up to the TeV regime. The smallest JES uncertainty of less than 1% is found for jets with $55 \le p_T^{\text{jet}} < 500$ GeV. For jets with $p_T = 20$ GeV the uncertainty is about 3%. For $p_T^{\text{jet}} > 1$ TeV the JES uncertainty is estimated from single-hadron response measurements in situ and in beam tests and is about 3%. The JES uncertainty for forward jets is derived from dijet $p_T$ balance measurements. The resulting uncertainty is largest for low-$p_T$ jets at $|\eta| = 4.5$ and amounts to 6%.

From the uncertainties of the in situ techniques used to assess the JES uncertainty, the correlation of the uncertainties in $p_T^{\text{jet}}$ and $\eta$ are derived and made available for physics analysis as a set of systematic uncertainty sources.

The effect of multiple proton-proton interactions is corrected for as a function of the measured and the expected numbers of pile-up events, and an uncertainty is evaluated using in situ techniques. Additional JES uncertainties due to specific event topologies, such as close-by jets or selections of event samples with an enhanced content of jets originating from light quarks or gluons, are also discussed. These uncertainties depend on the event sample used in a given physics analysis and are evaluated for representative examples. For an event sample of semileptonically decaying top-pairs, assuming average 2011 pile-up conditions, the total JES uncertainty accounting for all effects is below 3% for $60 \le p_T^{\text{jet}} < 1000$ GeV when using the EM+JES calibration scheme, and it is further reduced to below 2.5% if using the more refined LCW+JES calibration scheme. In the case of a sample of inclusive QCD jets under the same conditions, the total JES uncertainties for the EM+JES and LCW+JES calibration schemes are below 3.5% and 2%, respectively.

# Acknowledgement

## 23 Acknowledgements

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; Yer-PHI, 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, DNSRC and Lundbeck Foundation, Denmark; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MIN-ERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF,
South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.

The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and 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) and in the Tier-2 facilities worldwide.

## References

1. M. Cacciari, G. P. Salam, and G. Soyez, *The anti-$k_t$ jet clustering algorithm*, JHEP 0804 (2008) 063, arXiv:0802.1189 [hep-ph].

2. ATLAS Collaboration, *Measurement of inclusive jet and dijet cross sections in proton-proton collisions at 7 TeV centre-of-mass energy with the ATLAS detector*, Eur. Phys. J. C 71 (2011) 1512, arXiv:1009.5908 [hep-ex].

3. ATLAS Collaboration, *Jet energy measurement with the ATLAS detector in proton-proton collisions at $\sqrt{s} = 7$ TeV*, Eur. Phys. J. C 73 (2013) 2304, arXiv:1112.6426 [hep-ex].

4. ATLAS Collaboration, *Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC*, Eur. Phys. J. C 73 (2013) 2305, arXiv:1203.1302 [hep-ex].

5. ATLAS Collaboration, *The ATLAS experiment at the CERN Large Hadron Collider*, JINST 3 (2008) S08003.

6. CDF Collaboration, A. Bhatti et al., *Determination of the jet energy scale at the collider detector at Fermilab*, Nucl. Instrum. Meth. A 566 (2006) 375-412, arXiv:0510047 [hep-ex].

7. D0 Collaboration Collaboration, B. Abbott et al., *Determination of the absolute jet energy scale in the D0 calorimeters*, Nucl. Instrum. Meth. A 424 (1999) 352-394, arXiv:hep-ex/9805009 [hep-ex].

8. D0 Collaboration, V. M. Abazov et al., *Jet energy scale determination in the D0 experiment*, arXiv:1312.6873 [hep-ex].

9. CMS Collaboration, *Determination of Jet Energy Calibration and Transverse Momentum Resolution in CMS*, JINST 6 (2011) P11002, arXiv:1107.4277 [physics.ins-det].

10. D0 Collaboration, V. M. Abazov et al., *Measurement of the inclusive jet cross section in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV*, Phys. Rev. D 85 (2012) 052006, arXiv:1110.3771 [hep-ex].

11. CDF Collaboration, T. Aaltonen et al., *Measurement of the Inclusive Jet Cross Section at the Fermilab Tevatron p anti-p Collider Using a Cone-Based Jet Algorithm*, Phys. Rev. D 78 (2008) 052006, arXiv:0807.2204 [hep-ex].

12. UA2 Collaboration, P. Bagnaia et al., *Measurement of production and properties of jets at the CERN anti-p p collider*, Z. Phys. C 20 (1983) 117-134.

13. H1 Collaboration, C. Adloff et al., *Measurement of neutral and charged current cross-sections in positron proton collisions at large momentum transfer*, Eur. Phys. J. C 13 (2000) 609-639, arXiv:hep-ex/9908059 [hep-ex].

14. H1 Collaboration, F. Aaron et al., *A Precision Measurement of the Inclusive ep Scattering Cross Section at HERA*, Eur. Phys. J. C 64 (2009) 561-587, arXiv:0904.3513 [hep-ex].

15. ZEUS Collaboration, S. Chekanov et al., *High mass dijet cross-sections in photoproduction at HERA*, Phys. Lett. B 531 (2002) 9-27, arXiv:hep-ex/0112030 [hep-ex].

16. ZEUS Collaboration, S. Chekanov et al., *Dijet photoproduction at HERA and the structure of the photon*, Eur. Phys. J. C 23 (2002) 615-631, arXiv:hep-ex/0112029 [hep-ex].

17. M. Wing, *Setting the jet energy scale for the ZEUS calorimeter*, arXiv:hep-ex/0206036 [hep-ex].

18. ATLAS Collaboration, *Performance of the ATLAS Trigger System in 2010*, Eur. Phys. J. C 72 (2012) 1849, arXiv:1110.1530 [hep-ex].

19. ATLAS Collaboration, *Readiness of the ATLAS Liquid Argon Calorimeter for LHC Collisions*, Eur. Phys. J. C 70 (2010) 723-753, arXiv:0912.2642 [physics.ins-det].

20. ATLAS Collaboration, *Readiness of the ATLAS Tile calorimeter for LHC collisions*, Eur. Phys. J. C 70 (2010) 1193-1236, arXiv:1007.5423 [physics.ins-det].

21. A. Buckley et al., *General-purpose event generators for LHC physics*, Phys. Rept. 504 (2011) 145-233, arXiv:1101.2599 [hep-ph].

22. T. Sjostrand, S. Mrenna, and P. Z. Skands, *PYTHIA 6.4 physics and manual*, JHEP 0605 (2006) 026, arXiv:0603.175 [hep-ph].

23. R. Corke and T. Sjostrand, *Improved Parton Showers at Large Transverse Momentum*, Eur. Phys. J. C 69 (2010) 1-18, arXiv:1003.2384 [hep-ph].

24. T. Sjostrand and P. Z. Skands, *Transverse-momentum-ordered showers and interleaved multiple interactions*, Eur. Phys. J. C 39 (2005) 129-154, arXiv:0408302 [hep-ph].

25. B. Andersson et al., *Parton fragmentation and string dynamics*, Phys. Rep. 97 (1983) 31-145.

26. ATLAS Collaboration, *ATLAS tunes for Pythia6 and Pythia8 for MC11*, ATLAS-PHYS-PUB-2011-009, July, 2011. https://cdsweb.cern.ch/record/1363300.

27. A. Sherstnev and R. S. Thorne, *Parton Distributions for LO Generators*, Eur. Phys. J. C55 (2008) 553-575, arXiv:0711.2473 [hep-ph].

28. M. Bahr et al., *Herwig++ physics and manual*, Eur. Phys. J. C 58 (2008) 639-707, arXiv:0803.0883 [hep-ph].

29. G. Marchesini et al., *Monte Carlo simulation of general hard processes with coherent QCD radiation*, Nucl. Phys. B 310 (1988) 461.

30. G. Marchesini et al., *A Monte Carlo event generator for simulating hadron emission reactions with interfering gluons*, Comput. Phys. Commun. 67 (1991) 465-508.

31. G. Corcella et al., *HERWIG 6.5 release note*, arXiv:0210213 [hep-ph].

32. B. R. Webber, *A QCD model for jet fragmentation including soft gluon interference*, Nucl. Phys. B 238 (1984) 492.

33. M. Bahr, S. Gieseke, and M. H. Seymour, *Simulation of multiple partonic interactions in Herwig++, JHEP 0807 (2008) 076, arXiv:0803.3633 [hep-ph].

34. J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, *MadGraph 5 : Going Beyond*, JHEP 1106 (2011) 128, arXiv:1106.0522 [hep-ph].

35. J. Pumplin, D. Stump, J. Huston, H. Lai, P. M. Nadolsky, et al., *New generation of parton distributions with uncertainties from global QCD analysis*, JHEP 0207 (2002) 012, arXiv:hep-ph/0201195 [hep-ph].

36. M. L. Mangano, M. Moretti, and R. Pittau, *Multijet matrix elements and shower evolution in hadronic collisions: $W\bar{b} + n$ jets as a case study*, Nucl. Phys. B 632 (2002) 343-362, arXiv:hep-ph/0108069 [hep-ph].
37. M. L. Mangano, M. Moretti, F. Piccinini, R. Pittau, and A. D. Polosa, ALPGEN, a generator for hard multiparton processes in hadronic collisions, JHEP 0307 (2003) 001, arXiv:hep-ph/0206293 [hep-ph].

38. J. Butterworth, J. R. Forshaw, and M. Seymour, Multiparton interactions in photoproduction at HERA, Z. Phys. C 72 (1996) 637-646, arXiv:hep-ph/9601371 [hep-ph].

39. ATLAS Collaboration, New ATLAS event generator tunes to 2010 data, ATL-PHYS-PUB-2011-008, May, 2011. http://cdsweb.cern.ch/record/1345343.

40. S. Frixione and B.R. Webber, Matching NLO QCD computations and parton shower simulations, JHEP 0206 (2002) 029, hep-ph/0204244.

41. H.-L. Lai et al., New parton distributions for collider physics, Phys. Rev. D82 (2010) 074024, arXiv:1007.2241 [hep-ph].

42. G. Corcella et al., HERWIG 6: An Event generator for hadron emission reactions with interfering gluons (including supersymmetric processes), JHEP 0101 (2001) 010, hep-ph/0011363.

43. S. Frixione, P. Nason, and C. Oleari, Matching NLO QCD computations with Parton Shower simulations: the POWHEG method, JHEP 0711 (2007) 070, arXiv:0709.2092 [hep-ph].

44. B.P. Kersevan and E. Richter-Was, The Monte Carlo event generator AcerMC version 2.0 with interfaces to PYTHIA 6.2 and HERWIG 6.5, (2004), hep-ph/0405247.

45. ATLAS Collaboration, Measurement of the top quark mass with the template method in the $t\bar{t} \to lepton + jets$ channel using ATLAS data, Eur. Phys. J. C 72 (2012) 2046, arXiv:1203.5755 [hep-ex].

46. ATLAS Collaboration, Measurement of ttbar production with a veto on additional central jet activity in pp collisions at $\sqrt{s} = 7$ TeV using the ATLAS detector, Eur. Phys. J. C 72 (2012) 2043, arXiv:1203.5015 [hep-ex].

47. T. Sjostrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852-867, arXiv:0710.3820 [hep-ph].

48. R. Corke and T. Sjostrand, Interleaved Parton Showers and Tuning Prospects, JHEP 1103 (2011) 032, arXiv:1011.1759 [hep-ph].

49. GEANT4 Collaboration, S. Agostinelli et al., GEANT4: A simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250-303.

50. ATLAS Collaboration, The ATLAS simulation infrastructure, Eur. Phys. J. C 70 (2010) 823-874, arXiv:1005.4568 [physics.ins-det].

51. H. W. Bertini, Intranuclear-cascade calculation of the secondary nucleon spectra from nucleon-nucleus interactions in the energy range 340 to 2900 MeV and comparisons with experiment, Phys. Rev. A 188 (1969) 1711-1730.

52. M. P. Guthrie, R. G. Alsmiller, and H. W. Bertini, Calculation of the capture of negative pions in light elements and comparison with experiments pertaining to cancer radiotherapy, Nucl. Instrum. Meth. 66 (1968) 29-36.

53. M. P. Guthrie and H. W. Bertini, News item results from medium-energy intranuclear-cascade calculation, Nucl. Phys. A 169 (1971) 670-672.

54. N. V. Stepanov, Statistical modeling of fission of excited atomic nuclei. 2. calculation and comparison with experiment, In Russian, ITEP, Moscow, 1988.

55. G. Folger and J. Wellisch, String parton models in GEANT4, eConf C0303241 (2003) MOMT007, arXiv:nucl-th/0306007 [nucl-th].

56. N. S. Amelin et al., Transverse flow and collectivity in ultrarelativistic heavy ion collisions, Phys. Rev. Lett. 67 (1991) 1523-1526.

57. L. V. Bravina, L. P. Csernai, P. Levai, N. S. Amelin, and D. Strottman, Fluid dynamics and quark gluon string model: What we can expect for Au + Au collisions at 11.6-A/GeV/c, Nucl. Phys. A 566 (1994) 461-464.

58. N. S. Amelin, L. P. Csernai, E. F. Staubo, and D. Strottman, Collectivity in ultrarelativistic heavy ion collisions, Nucl. Phys. A 544 (1992) 463-466.

59. L. V. Bravina, Scaling violation of transverse flow in heavy ion collisions at AGS energies, Phys. Lett. B 344 (1995) 49-54.

60. ATLAS Collaboration, The simulation principle and performance of the ATLAS fast calorimeter simulation FastCaloSim, ATL-PHYS-PUB-2010-013, October, 2010. https://cds.cern.ch/record/1300517.

61. ATLAS Collaboration, The ATLAS Simulation Infrastructure, Eur. Phys. J. C 70 (2010) 823-874, arXiv:1005.4568 [physics.ins-det].

62. C. Cojocaru et al., Hadronic calibration of the ATLAS liquid argon end-cap calorimeter in the pseudorapidity region $1.6 < |\eta| < 1.8$ in beam tests, Nucl. Instrum. Meth. A 531 (2004) 481-514.

63. W. Lampl et al., Calorimeter clustering algorithms: description and performance, ATL-LARG-PUB-2008-002, April, 2008. http://cdsweb.cern.ch/record/1099735.

64. M. Cacciari and G. P. Salam, Dispelling the $N^3$ myth for the $k_t$ jet-finder, Phys. Lett. B 641 (2006) 57-61.

65. M. Cacciari, G. P. Salam, and G. Soyez http://fastjet.fr/.

66. E. Abat et al., Combined performance studies for electrons at the 2004 ATLAS combined test-beam, JINST 5 (2010) P11006.

67. M. Aharrouche et al., Measurement of the response of the ATLAS liquid argon barrel calorimeter to electrons at the 2004 combined test-beam, Nucl. Instrum. Meth. A 614 (2010) 400-432.

68. J. Colas et al., Response uniformity of the ATLAS liquid argon electromagnetic calorimeter, Nucl. Instrum. Meth. A 582 (2007) 429-455, arXiv:0709.1094 [physics.ins-det].

69. M. Aharrouche et al., Energy linearity and resolution of the ATLAS electromagnetic barrel calorimeter in an electron test-beam, Nucl. Instrum. Meth. A 568 (2006) 601-623.

70. P. Adragna et al., Testbeam studies of production modules of the ATLAS Tile calorimeter, Nucl. Instrum. Meth. A 606 (2009) 362-394.

71. J. Pinfold et al., Performance of the ATLAS liquid argon endcap calorimeter in the pseudorapidity region $2.5 < |\eta| < 4.0$ in beam tests, Nucl. Instrum. Meth. A 593 (2008) 324-342.

72. M. Aharrouche et al., Study of the response of ATLAS electromagnetic liquid argon calorimeters to muons, Nucl. Instrum. Meth. A 606 (2009) 419-431.

73. ATLAS Collaboration, Electron performance measurements with the ATLAS detector using the 2010 LHC proton-proton collision data, Eur. Phys. J. C 72 (2012) 1909, arXiv:1110.3174 [hep-ex].

74. ATLAS Collaboration, Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run, JINST 1307 (2013) P07004, arXiv:1303.0223 [hep-ex].

75. ATLAS Collaboration, Performance of Missing Transverse Momentum Reconstruction in Proton-Proton Collisions at 7 TeV with ATLAS, Eur. Phys. J. C 72 (2012) 1844, arXiv:1108.5602 [hep-ex].
76. Particle Data Group Collaboration, J. Beringer et al., Review of Particle Physics (RPP), Phys. Rev. D 86 (2012) 010001.

77. ATLAS Collaboration, Measurement of the Inelastic Proton-Proton Cross-Section at $\sqrt{s} = 7$ TeV with the ATLAS Detector, Nature Commun. 2 (2011) 463, arXiv:1104.0326 [hep-ex].

78. ATLAS Collaboration, Luminosity determination in p-p collisions at $\sqrt{s} = 7$ TeV using the ATLAS detector at the LHC, Eur. Phys. J. C 71 (2011) 1630, arXiv:1101.2185 [hep-ex].

79. M. Cacciari, G. P. Salam, and G. Soyez, The Catchment Area of Jets, JHEP 0804 (2008) 005, arXiv:0802.1188 [hep-ph].

80. ATLAS Collaboration, Expected performance of the ATLAS experiment - detector, trigger and physics, arXiv:0901.0512 [hep-ex].

81. V. Lendermann et al., Combining Triggers in HEP Data Analysis, Nucl. Instrum. Meth. A 604 (2009) 707-718, arXiv:0901.4118 [hep-ex].

82. ATLAS Collaboration, Jet energy resolution in proton-proton collisions at $\sqrt{s} = 7$ TeV recorded in 2010 with the ATLAS detector, Eur. Phys. J. C 73 (2013) 2306, arXiv:1210.6210 [hep-ex].

83. ATLAS Collaboration, Performance of the Electron and Photon Trigger in p-p Collisions at $\sqrt{s} = 7$ TeV, ATLAS-CONF-2011-114, August, 2011. http://cdsweb.cern.ch/record/1375551.

84. ATLAS Collaboration, Jet energy resolution from in-situ techniques with the ATLAS detector using proton-proton collisions at a centre of mass energy $\sqrt{s} = 7$ TeV, ATLAS-CONF-2010-054, July, 2010. http://cdsweb.cern.ch/record/1281311.

85. ATLAS Collaboration, Measurement of the production cross section for Z/gamma* in association with jets in pp collisions at $\sqrt{s} = 7$ TeV with the ATLAS Detector, Phys. Rev. D 85 (2012) 032009, arXiv:1111.2690 [hep-ex].

86. R. Barlow, Asymmetric Errors, in Proceedings of the PHYSTAT2003 conference. SLAC, September, 2003. arXiv:physics/0401042.

87. ATLAS Collaboration, Measurement of the inclusive isolated prompt photon cross section in pp collisions at $\sqrt{s} = 7$ TeV with the ATLAS detector, Phys. Rev. D 83 (2011) 052005, arXiv:1012.4389 [hep-ex].

88. T. Gabriel, D. E. Groom, P. Job, N. Mokhov, and G. Stevenson, Energy dependence of hadronic activity, Nucl. Instrum. Meth. A 338 (1994) 336-347.

89. P. Z. Skands, Tuning Monte Carlo generators: The Perugia tunes, Phys. Rev. D 82 (2010) 074018, arXiv:1005.3457 [hep-ph].

90. E. Abat et al., Study of energy response and resolution of the ATLAS barrel calorimeter to hadrons of energies from 20 GeV to 350 GeV, Nucl. Instrum. Meth. A 621 (2010) 134-150.

91. ATLAS Collaboration, Determination of the tau energy scale and the associated systematic uncertainty in proton-proton collisions at $\sqrt{s} = 7$ TeV with the ATLAS detector at the LHC in 2011, ATLAS-CONF-2012-054, June, 2012. http://cdsweb.cern.ch/record/1453781.

92. ATLAS collaboration, Measurement of the b-tag Efficiency in a Sample of Jets Containing Muons with 5 fb1 of Data from the ATLAS Detector, ATLAS-CONF-2012-043, March, 2012. http://cdsweb.cern.ch/record/1435197.

93. ATLAS Collaboration, Muon reconstruction efficiency and momentum resolution of the ATLAS experiment in proton-proton collisions at $\sqrt{s}=7$ TeV in 2010, arXiv:1404.4562 [hep-ex].

94. ATLAS Collaboration, Performance of Missing Transverse Momentum Reconstruction in pp collisions at $\sqrt{s} = 7$ TeV with ATLAS, Eur. Phys. J. C 72 (2011) 1844, arXiv:1108.5602 [hep-ex].

95. J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC, Phys. Rev. Lett. 107 (2011) 172001, arXiv:1106.3076 [hep-ph].

96. ATLAS Collaboration, Light-quark and gluon jet discrimination in pp collisions at $\sqrt{s} = 7$ TeV with the ATLAS detector, arXiv:1405.6583 [hep-ex].

97. ATLAS Collaboration, Measurement of the flavour composition of dijet events in pp collisions at $\sqrt{s} = 7$ TeV with the ATLAS detector, Eur. Phys. J. C 73 (2013) 2301, arXiv:1210.0441 [hep-ex].

98. M. Bowler, $e^+e^-$ Production of Heavy Quarks in the String Model, Z. Phys. C 11 (1981) 169.

99. A. Buckley, H. Hoeth, H. Lacker, H. Schulz, and J. E. von Seggern, Systematic event generator tuning for the LHC, Eur. Phys. J. C 65 (2010) 331-357, arXiv:0907.2973 [hep-ph].

100. ATLAS Collaboration, Charged-particle multiplicities in p-p interactions measured with the ATLAS detector at the LHC, New J. Phys. 13 (2011) 053033, arXiv:1012.5104 [hep-ex].

101. DELPHI Collaboration, J. Abdallah et al., Determination of heavy quark non-perturbative parameters from spectral moments in semileptonic B decays, Eur. Phys. J. C 45 (2006) 35-59, arXiv:hep-ex/0510024 [hep-ex].

102. ATLAS Collaboration, Jet energy scale and its systematic uncertainty for jets produced in proton-proton collisions at $\sqrt{s}=7$ TeV and measured with the ATLAS detector, ATLAS-CONF-2010-056, July, 2010. http://cdsweb.cern.ch/record/1281329.
# Appendix A: Comparison of the ATLAS JES uncertainty with previous calibrations

The progress of the JES uncertainty is demonstrated in Fig. 66. The label “2011 in situ” refers to the uncertainty documented in this paper, the uncertainty estimate on the 2010 data-set is detailed in Ref. [3] while the uncertainty determined before LHC collisions is described in Ref. [102]. The label “2010 in situ” refers to the uncertainty derived from in situ techniques in the 2010 data-set that is discussed as cross-check to the uncertainty derived from the single-hadron response in Ref. [3].

<figure><img src="image_107.png" /><figcaption>Fig. 66: Fractional jet energy scale systematic uncertainty for inclusive jets as a function of $p_T^{jet}$ for jets with ((a), (c)) $R = 0.4$ and ((b), (d)) $R = 0.6$ calibrated with the ((a), (b)) EM+JES and ((c), (d)) LCW+JES schemes and with $\eta = 0.5$.</figcaption></figure>
The ATLAS Collaboration

G. Aad⁴⁸, T. Abajyan²¹, B. Abbott¹¹², J. Abdallah¹², S. Abdel Khalek¹¹⁶, O. Abdinov¹¹, R. Aben¹⁰⁶, B. Abi¹¹³, M. Abolins⁸⁹, O.S. AbouZeid¹⁵⁹, H. Abramowicz¹⁵⁴, H. Abreu¹³⁷, Y. Abulaiti¹⁴⁷a,¹⁴⁷b, B.S. Acharya¹⁶⁵a,¹⁶⁵b,ª, L. Adamczyk³⁸a, D.L. Adams²⁵, T.N. Addy⁵⁶, J. Adelman¹⁷⁷, S. Adomeit⁹⁹, T. Adye¹³⁰, S. Aefsky²³, T. Agatonovic-Jovin¹³b, J.A. Aguilar-Saavedra¹²⁵f,¹²⁵a, M. Agustoni¹⁷, S.P. Ahlen²², A. Ahmad¹⁴⁹, F. Ahmadov⁶⁴,ᵇ, G. Aielli¹³⁴a,¹³⁴b, T.P.A. Åkesson⁸⁰, G. Akimoto¹⁵⁶, A.V. Akimov⁹⁵, M.A. Alam⁷⁶, J. Albert¹⁷⁰, S. Albrand⁵⁵, M.J. Alconada Verzini⁷⁰, M. Aleksa³⁰, I.N. Aleksandrov⁶⁴, F. Alessandria⁹⁰a, C. Alexa²⁶a, G. Alexander¹⁵⁴, G. Alexandre⁴⁹, T. Alexopoulos¹⁰, M. Alhroob¹⁶⁵a,¹⁶⁵c, M. Aliev¹⁶, G. Alimonti⁹⁰a, L. Alio⁸⁴, J. Alison³¹, B.M.M. Allbrooke¹⁸, L.J. Allison⁷¹, P.P. Allport⁷³, S.E. Allwood-Spiers⁵³, J. Almond⁸³, A. Aloisio¹⁰³a,¹⁰³b, R. Alon¹⁷³, A. Alonso³⁶, F. Alonso⁷⁰, A. Altheimer³⁵, B. Alvarez Gonzalez⁸⁹, M.G. Alviggi¹⁰³a,¹⁰³b, K. Amako⁶⁵, Y. Amaral Coutinho²⁴a, C. Amelung²³, V.V. Ammosov¹²⁹,*, S.P. Amor Dos Santos¹²⁵a,¹²⁵c, A. Amorim¹²⁵a,¹²⁵b, S. Amoroso⁴⁸, N. Amram¹⁵⁴, G. Amundsen²³, C. Anastopoulos³⁰, L.S. Ancu¹⁷, N. Andari³⁰, T. Andeen³⁵, C.F. Anders⁵⁸b, G. Anders⁵⁸a, K.J. Anderson³¹, A. Andreazza⁹⁰a,⁹⁰b, V. Andrei⁵⁸a, X.S. Anduaga⁷⁰, S. Angelidakis⁹, P. Anger⁴⁴, A. Angerami³⁵, F. Anghinolfi³⁰, A.V. Anisenkov¹⁰⁸, N. Anjos¹²⁵a, A. Annovi⁴⁷, A. Antonaki⁹, M. Antonelli⁴⁷, A. Antonov⁹⁷, J. Antos¹⁴⁵b, F. Anulli¹³³a, M. Aoki¹⁰², L. Aperio Bella¹⁸, R. Apolle¹¹⁹.c, G. Arabidze⁸⁹, I. Aracena¹⁴⁴, Y. Arai⁶⁵, A.T.H. Arce⁴⁵, S. Arfaoui¹⁴⁹, J-F. Arguin⁹⁴, S. Argyropoulos⁴², E. Arik¹⁹a,*, M. Arik¹⁹a, A.J. Armbruster⁸⁸, O. Arnaez⁸², V. Arnal⁸¹, O. Arslan²¹, A. Artamonov⁹⁶, G. Artoni²³, S. Asai¹⁵⁶, N. Asbah⁹⁴, S. Ask²⁸, B. Åsman¹⁴⁷a,¹⁴⁷b, L. Asquith⁶, K. Assamagan²⁵, R. Astalos¹⁴⁵a, A. Astbury¹⁷⁰, M. Atkinson¹⁶⁶, N.B. Atlay¹⁴², B. Auerbach⁶, E. Auge¹¹⁶, K. Augsten¹²⁷, M. Aurousseau¹⁴⁶b, G. Avolio³⁰, G. Azuelos⁹⁴,d, Y. Azuma¹⁵⁶, M.A. Baak³⁰, C. Bacci¹³⁵a,¹³⁵b, A.M. Bach¹⁵, H. Bachacou¹³⁷, K. Bachas¹⁵⁵, M. Backes³⁰, M. Backhaus²¹, J. Backus Mayes¹⁴⁴, E. Badescu²⁶a, P. Bagiacchi¹³³a,¹³³b, P. Bagnaia¹³³a,¹³³b, Y. Bai³³a, D.C. Bailey¹⁵⁹, T. Bain³⁵, J.T. Baines¹³⁰, O.K. Baker¹⁷⁷, S. Baker⁷⁷, P. Balek¹²⁸, F. Balli¹³⁷, E. Banas³⁹, Sw. Banerjee¹⁷⁴, D. Banfi³⁰, A. Bangert¹⁵¹, V. Bansal¹⁷⁰, H.S. Bansil¹⁸, L. Barak¹⁷³, S.P. Baranov⁹⁵, T. Barber⁴⁸, E.L. Barberio⁸⁷, D. Barberis⁵⁰a,⁵⁰b, M. Barbero⁸⁴, T. Barillari¹⁰⁰, M. Barisonzi¹⁷⁶, T. Barklow¹⁴⁴, N. Barlow²⁸, B.M. Barnett¹³⁰, R.M. Barnett¹⁵, A. Baroncelli¹³⁵a, G. Barone⁴⁹, A.J. Barr¹¹⁹, F. Barreiro⁸¹, J. Barreiro Guimarães da Costa⁵⁷, R. Bartoldus¹⁴⁴, A.E. Barton⁷¹, P. Bartos¹⁴⁵a, V. Bartsch¹⁵⁰, A. Bassalat¹¹⁶, A. Basye¹⁶⁶, R.L. Bates⁵³, L. Batkova¹⁴⁵a, J.R. Batley²⁸, M. Battistin³⁰, F. Bauer¹³⁷, H.S. Bawa¹⁴⁴,e, T. Beau⁷⁹, P.H. Beauchemin¹⁶², R. Beccherle⁵⁰a, P. Bechtle²¹, H.P. Beck¹⁷, K. Becker¹⁷⁶, S. Becker⁹⁹, M. Beckingham¹³⁹, A.J. Beddall¹⁹c, A. Beddall¹⁹c, S. Bedikian¹⁷⁷, V.A. Bednyakov⁶⁴, C.P. Bee⁸⁴, L.J. Beemster¹⁰⁶, T.A. Beermann¹⁷⁶, M. Begel²⁵, K. Behr¹¹⁹, C. Belanger-Champagne⁸⁶, P.J. Bell⁴⁹, W.H. Bell⁴⁹, G. Bella¹⁵⁴, L. Bellagamba²⁰a, A. Bellerive²⁹, M. Bellomo³⁰, A. Belloni⁵⁷, O.L. Beloborodova¹⁰⁸f, K. Belotskiy⁹⁷, O. Beltramello³⁰, O. Benary¹⁵⁴, D. Benchekroun¹³⁶a, K. Bentdz¹⁴⁷a,¹⁴⁷b, N. Benekos¹⁶⁶, Y. Benhammou¹⁵⁴, E. Benhar Noccioli⁴⁹, J.A. Benitez Garcia¹⁶⁰b, D.P. Benjamin⁴⁵, J.R. Bensinger²³, K. Benslama¹³¹, S. Bentvelsen¹⁰⁶, D. Berge³⁰, E. Bergeaas Kuutmann¹⁶, N. Berger⁵, F. Berghaus¹⁷⁰, E. Berglund¹⁰⁶, J. Beringer¹⁵, C. Bernard²², P. Bernat⁷⁷, R. Bernhard⁴⁸, C. Bernius⁷⁸, F.U. Bernlochner¹⁷⁰, T. Berry⁷⁶, P. Berta¹²⁸, C. Bertella⁸⁴, F. Bertolucci¹²³a,¹²³b, M.I. Besana⁹⁰a, G.J. Besjes¹⁰⁵, O. Bessidskaia¹⁴⁷a,¹⁴⁷b, N. Besson¹³⁷, S. Bethke¹⁰⁰, W. Bhimji⁴⁶, R.M. Bianchi¹²⁴, L. Bianchini²³, M. Bianco³⁰, O. Biebel⁹⁹, S.P. Bieniek⁷⁷, K. Bierwagen⁵⁴, J. Biesiada¹⁵, M. Biglietti¹³⁵a, J. Bilbao De Mendizabal⁴⁹, H. Bilokon⁴⁷, M. Bindi²⁰a,²⁰b, S. Binet¹¹⁶, A. Bingul¹⁹c, C. Bini¹³³a,¹³³b, B. Bittner¹⁰⁰, C.W. Black¹⁵¹, J.E. Black¹⁴⁴, K.M. Black²², D. Blackburn¹³⁹, R.E. Blair⁶, J.-B. Blanchard¹³⁷, T. Blazek¹⁴⁵a, I. Bloch⁴², C. Blocker²³, J. Blocki³⁹, W. Blum⁸²,*, U. Blumenschein⁵⁴, G.J. Bobbink¹⁰⁶, V.S. Bobrovnikov¹⁰⁸, S.S. Bocchetta⁸⁰, A. Bocci⁴⁵, C.R. Boddy¹¹⁹, M. Boehler⁴⁸, J. Boek¹⁷⁶, T.T. Boek¹⁷⁶, N. Boelaert³⁶, J.A. Bogaerts³⁰, A.G. Bogdanchikov¹⁰⁸, A. Bogouch⁹¹,*, C. Bohm¹⁴⁷a, J. Bohm¹²⁶, V. Boisvert⁷⁶, T. Bold³⁸a, V. Boldea²⁶a, A.S. Boldyrev⁹⁸, N.M. Bolnet¹³⁷, M. Bomben⁷⁹, M. Bona⁷⁵, M. Boonekamp¹³⁷, S. Bordini⁷⁹, C. Borer¹⁷, A. Borisov¹²⁹, G. Borissov⁷¹, M. Borri⁸³, S. Borroni⁴², J. Bortfeldt⁹⁹, V. Bortolotto¹³⁵a,¹³⁵b, K. Bos¹⁰⁶, D. Boscherini²⁰a, M. Bosman¹², H. Boterenbrood¹⁰⁶, J. Bouchami⁹⁴, J. Boudreau¹²⁴, E.V. Bouhova-Thacker⁷¹, D. Boumediene³⁴, C. Bourdarios¹¹⁶, N. Bousson⁸⁴, S. Boutouil¹³⁶d, A. Boveia³¹, J. Boyd³⁰, I.R. Boyko⁶⁴, I. Bozovic-Jelisavcic¹³b, J. Bracinik¹⁸, P. Branchini¹³⁵a, A. Brandt⁸, G. Brandt¹⁵, O. Brandt⁵⁸a, U. Bratzler¹⁵⁷, B. Brau⁸⁵, J.E. Brau¹¹⁵, H.M. Braun¹⁷⁶,*, S.F. Brazzale¹⁶⁵a,¹⁶⁵c, B. Brelier¹⁵⁹, K. Brendlinger¹²¹, R. Brenner¹⁶⁷, S. Bressler¹⁷³, T.M. Bristow⁴⁶, D. Britton⁵³, F.M. Brochu²⁸, I. Brock²¹, R. Brock⁸⁹, F. Broggi⁹⁰a, C. Bromberg⁸⁹, J. Bronner¹⁰⁰, G. Brooijmans³⁵, T. Brooks⁷⁶, W.K. Brooks³²b, J. Brosamer¹⁵, E. Brost¹¹⁵, G. Brown⁸³, J. Brown⁵⁵, P.A. Bruckman de Renstrom³⁹, D. Bruncko¹⁴⁵b, R. Bruneliere⁴⁸, S. Brunet⁶⁰, A. Bruni²⁰a, G. Bruni²⁰a, M. Bruschi²⁰a, L. Bryngemark⁸⁰, T. Buanes¹⁴, Q. Buat⁵⁵, F. Bucci⁴⁹, P. Buchholz¹⁴², R.M. Buckingham¹¹⁹, A.G. Buckley⁵³, S.I. Buda²⁶a, I.A. Budagov⁶⁴, B. Budick¹⁰⁹, F. Buehrer⁴⁸, L. Bugge¹¹⁸, M.K. Bugge¹¹⁸, O. Bulekov⁹⁷, A.C. Bundock⁷³, M. Bunse⁴³, H. Burckhart³⁰, S. Burdin⁷³, T. Burgess¹⁴, B. Burghgrave¹⁰⁷, S. Burke¹³⁰, I. Burmeister⁴³, E. Busato³⁴, V. Büscher⁸², P. Bussey⁵³, C.P. Buszello¹⁶⁷, B. Butler⁵⁷, J.M. Butler²², A.I. Butt³, C.M. Buttar⁵³, J.M. Butterworth⁷⁷, W. Buttinger²⁸, A. Buzatu⁵³, M. Byszewski¹⁰, S. Cabrera Urbán¹⁶⁸, D. Caforio²⁰a,²⁰b, O. Cakir⁴a, P. Calafiura¹⁵, G. Calderini⁷⁹, P. Calfayan⁹⁹, R. Calkins¹⁰⁷, L.P. Caloba²⁴a, R. Caloi¹³³a,¹³³b, D. Calvet³⁴, S. Calvet³⁴, R. Camacho Toro⁴⁹, P. Camarri¹³⁴a,¹³⁴b, D. Cameron¹¹⁸, L.M. Caminada¹⁵, R. Caminal Armadans¹², S. Campana³⁰, M. Campanelli⁷⁷, V. Canale¹⁰³a,¹⁰³b, F. Canelli³¹, A. Canepa¹⁶⁰a, J. Cantero⁸¹, R. Cantrill⁷⁶, T. Cao⁴⁰, M.D.M. Capeans Garrido³⁰, I. Caprini²⁶a, M. Caprini²⁶a, M. Capua³⁷a,³⁷b, R. Caputo⁸², R. Cardarelli¹³⁴a, T. Carli³⁰, G. Carlino¹⁰³a, L. Carminati⁹⁰a,⁹⁰b, S. Caron¹⁰⁵, E. Carquin³²a, G.D. Carrillo-Montoya¹⁴⁶c, A.A. Carter⁷⁵, J.R. Carter²⁸, J. Carvalho¹²⁵a,¹²⁵c, D. Casadei⁷⁷, M.P. Casado¹², C. Caso⁵⁰a,⁵⁰b,*, E. Castaneda-Miranda¹⁴⁶b, A. Castelli¹⁰⁶, V. Castillo Gimenez¹⁶⁸, N.F. Castro¹²⁵a, P. Catastini⁵⁷, A. Catinaccio³⁰, J.R. Catmore⁷¹, A. Cattai³⁰, G. Cattani¹³⁴a,¹³⁴b, S. Caughron⁸⁹, V. Cavaliere¹⁶⁶, D. Cavalli⁹⁰a, M. Cavalli-Sforza¹², V. Cavasinni¹²³a,¹²³b, F. Ceradini¹³⁵a,¹³⁵b, B. Cerio⁴⁵,
K. Cerny¹²⁸, A.S. Cerqueira²⁴b, A. Cerri¹⁵⁰, L. Cerrito⁷⁵, F. Cerutti¹⁵, A. Cervelli¹⁷, S.A. Cetin¹⁹b, A. Chafaq¹³⁶a,
D. Chakraborty¹⁰⁷, I. Chalupkova¹²⁸, K. Chan³, P. Chang¹⁶⁶, B. Chapleau⁸⁶, J.D. Chapman²⁸, D. Charfeddine¹¹⁶,
D.G. Charlton¹⁸, V. Chavda⁸³, C.A. Chavez Barajas³⁰, S. Cheatham⁸⁶, S. Chekanov⁶, S.V. Chekulaev¹⁶⁰a, G.A. Chelkov⁶⁴,
M.A. Chelstowska⁸⁸, C. Chen⁶³, H. Chen²⁵, K. Chen¹⁴⁹, L. Chen³³d,⁸, S. Chen³³c, X. Chen¹⁷⁴, Y. Chen³⁵, Y. Cheng³¹,
A. Cheplakov⁶⁴, R. Cherkaoui El Moursli¹³⁶e, V. Chernyatin²⁵,*, E. Cheu⁷, L. Chevalier¹³⁷, V. Chiarella⁴⁷, G. Chiefari¹⁰³a,¹⁰³b,
J.T. Childers³⁰, A. Chilingarov⁷¹, G. Chiodini⁷²a, A.S. Chisholm¹⁸, R.T. Chislett⁷⁷, A. Chitan²⁶a, M.V. Chizhov⁶⁴,
S. Chouridou⁹, B.K.B. Chow⁹⁹, I.A. Christidi⁷⁷, D. Chromek-Burckhart³⁰, M.L. Chu¹⁵², J. Chudoba¹²⁶, G. Ciapetti¹³³a,¹³³b,
A.K. Ciftci⁴a, R. Ciftci⁴a, D. Cinca⁶², V. Cindro⁷⁴, A. Ciocio¹⁵, M. Cirilli⁸⁸, P. Cirkovic¹³b, Z.H. Citron¹⁷³, M. Citterio⁹⁰a,
M. Ciubancan²⁶a, A. Clark⁴⁹, P.J. Clark⁴⁶, R.N. Clarke¹⁵, W. Cleland¹²⁴, J.C. Clemens⁸⁴, B. Clement⁵⁵, C. Clement¹⁴⁷a,¹⁴⁷b,
Y. Coadou⁸⁴, M. Cobal¹⁶⁵a,¹⁶⁵c, A. Coccaro¹³⁹, J. Cochran⁶³, S. Coelli⁹⁰a, L. Coffey²³, J.G. Cogan¹⁴⁴, J. Coggeshall¹⁶⁶,
J. Colas⁵, B. Cole³⁵, S. Cole¹⁰⁷, A.P. Colijn¹⁰⁶, C. Collins-Tooth⁵³, J. Collot⁵⁵, T. Colombo⁵⁸c, G. Colon⁸⁵, G. Compostella¹⁰⁰,
P. Conde Muiño¹²⁵a,¹²⁵b, E. Coniavitis¹⁶⁷, M.C. Conidi¹², I.A. Connelly⁷⁶, S.M. Consonni⁹⁰a,⁹⁰b, V. Consorti⁴⁸,
S. Constantinescu²⁶a, C. Conta¹²⁰a,¹²⁰b, G. Conti⁵⁷, F. Conventi¹⁰³a,⁷, M. Cooke¹⁵, B.D. Cooper⁷⁷, A.M. Cooper-Sarkar¹¹⁹,
N.J. Cooper-Smith⁷⁶, K. Copic¹⁵, T. Cornelissen¹⁷⁶, M. Corradi²⁰a, F. Corriveau⁸⁶, i, A. Corso-Radu¹⁶⁴, A. Cortes-Gonzalez¹²,
G. Cortiana¹⁰⁰, G. Costa⁹⁰a, M.J. Costa¹⁶⁸, D. Costanzo¹⁴⁰, D. Côté⁸, G. Cottin³²a, L. Journeya¹⁷⁰, G. Cowan⁷⁶, B.E. Cox⁸³,
K. Cranmer¹⁰⁹, G. Cree²⁹, S. Crépé-Renaudin⁵⁵, F. Crescioli⁷⁹, M. Crispin Ortuzar¹¹⁹, M. Cristinziani²¹, G. Crosetti³⁷a,³⁷b,
C.-M. Cuciuc²⁶a, C. Cuenca Almenar¹⁷⁷, T. Cuhadar Donszelmann¹⁴⁰, J. Cummings¹⁷⁷, M. Curatolo⁴⁷, C. Cuthbert¹⁵¹,
H. Czirr¹⁴², P. Czodrowski⁴⁴, Z. Czyczula¹⁷⁷, S. D'Auria⁵³, M. D'Onofrio⁷³, A. D'Orazio¹³³a,¹³³b
M.J. Da Cunha Sargedas De Sousa¹²⁵a,¹²⁵b, C. Da Via⁸³, W. Dabrowski³⁸a, A. Dafinca¹¹⁹, T. Dai⁸⁸, F. Dallaire⁹⁴,
C. Dallapiccola⁸⁵, M. Dam³⁶, A.C. Daniells¹⁸, M. Dano Hoffmann³⁶, V. Dao¹⁰⁵, G. Darbo⁵⁰a, G.L. Darlea²⁶c, S. Darmora⁸,
J.A. Dassoulas⁴², W. Davey²¹, C. David¹⁷⁰, T. Davidek¹²⁸, E. Davies¹¹⁹, c, M. Davies⁹⁴, O. Davignon⁷⁹, A.R. Davison⁷⁷,
Y. Davygora⁵⁸a, E. Dawe¹⁴³, I. Dawson¹⁴⁰, R.K. Daya-Ishmukhametova²³, K. De⁸, R. de Asmundis¹⁰³a, S. De Castro²⁰a,²⁰b,
S. De Cecco⁷⁹, J. de Graat⁹⁹, N. De Groot¹⁰⁵, P. de Jong¹⁰⁶, C. De La Taille¹¹⁶, H. De la Torre⁸¹, F. De Lorenzi⁶³,
L. De Nooij¹⁰⁶, D. De Pedis¹³³a, A. De Salvo¹³³a, U. De Sanctis¹⁶⁵a,¹⁶⁵c, A. De Santo¹⁵⁰, J.B. De Vivie De Regie¹¹⁶,
G. De Zorzi¹³³a,¹³³b, W.J. Dearnaley⁷¹, R. Debbe²⁵, C. Debenedetti⁴⁶, B. Dechenaux⁵⁵, D.V. Dedovich⁶⁴, J. Degenhardt¹²¹,
J. Del Peso⁸¹, T. Del Prete¹²³a,¹²³b, T. Delemontex⁵⁵, F. Deliot¹³⁷, M. Deliyergiyev⁷⁴, A. Dell'Acqua³⁰, L. Dell'Asta²²,
M. Della Pietra¹⁰³a,⁷, D. della Volpe⁴⁹, M. Delmastro⁵, P.A. Delsart⁵⁵, C. Deluca¹⁰⁶, S. Demers¹⁷⁷, M. Demichev⁶⁴,
A. Demilly⁷⁹, B. Demirkoz¹², j, S.P. Denisov¹²⁹, D. Derendarz³⁹, J.E. Derkaoui¹³⁶d, F. Derue⁷⁹, P. Dervan⁷³, K. Desch²¹,
P.O. Deviveiros¹⁰⁶, A. Dewhurst¹³⁰, B. DeWilde¹⁴⁹, S. Dhaliwal¹⁰⁶, R. Dhullipudi⁷⁸, k, A. Di Ciaccio¹³⁴a,¹³⁴b, L. Di Ciaccio⁵,
A. Di Domenico¹³³a,¹³³b, C. Di Donato¹⁰³a,¹⁰³b, A. Di Girolamo³⁰, B. Di Girolamo³⁰, A. Di Mattia¹⁵³, B. Di Micco¹³⁵a,¹³⁵b,
R. Di Nardo⁴⁷, A. Di Simone⁴⁸, R. Di Sipio²⁰a,²⁰b, D. Di Valentino²⁹, M.A. Diaz³²a, E.B. Diehl⁸⁸, J. Dietrich⁴²,
T.A. Dietzsch⁵⁸a, S. Diglio⁸⁷, K. Dindar Yagci⁴⁰, J. Dingfelder²¹, C. Dionisi¹³³a,¹³³b, P. Dita²⁶a, S. Dita²⁶a, F. Dittus³⁰,
F. Djama⁸⁴, T. Djobava⁵¹b, M.A.B. do Vale²⁴c, A. Do Valle Wemans¹²⁵a,¹²⁵g, T.K.O. Doan⁵, D. Dobos³⁰, E. Dobson⁷⁷,
J. Dodd³⁵, C. Doglioni⁴⁹, T. Doherty⁵³, T. Dohmae¹⁵⁶, J. Dolejsi¹²⁸, Z. Dolezal¹²⁸, B.A. Dolgoshein⁹⁷,*, M. Donadelli²⁴d,
S. Donati¹²³a,¹²³b, P. Dondero¹²⁰a,¹²⁰b, J. Donini³⁴, J. Dopke³⁰, A. Doria¹⁰³a, A. Dos Anjos¹⁷⁴, A. Dotti¹²³a,¹²³b, M.T. Dova⁷⁰,
A.T. Doyle⁵³, M. Dris¹⁰, J. Dubbert⁸⁸, S. Dube¹⁵, E. Dubreuil³⁴, E. Duchovni¹⁷³, G. Duckeck⁹⁹, O.A. Ducu²⁶a, D. Duda¹⁷⁶,
A. Dudarev³⁰, F. Dudziak⁶³, L. Duflot¹¹⁶, L. Duguid⁷⁶, M. Dührssen³⁰, M. Dunford⁵⁸a, H. Duran Yildiz⁴a, M. Düren⁵²,
M. Dwuznik³⁸a, J. Ebke⁹⁹, W. Edson², C.A. Edwards⁷⁶, N.C. Edwards⁴⁶, W. Ehrenfeld²¹, T. Eifert¹⁴⁴, G. Eigen¹⁴,
K. Einsweiler¹⁵, E. Eisenhandler⁷⁵, T. Ekelof¹⁶⁷, M. El Kacimi¹³⁶c, M. Ellert¹⁶⁷, S. Elles⁵, F. Ellinghaus⁸², K. Ellis⁷⁵,
N. Ellis³⁰, J. Elmsheuser⁹⁹, M. Elsing³⁰, D. Emeliyanov¹³⁰, Y. Enari¹⁵⁶, O.C. Endner⁸², M. Endo¹¹⁷, R. Engelmann¹⁴⁹,
J. Erdmann¹⁷⁷, A. Ereditato¹⁷, D. Eriksson¹⁴⁷a, G. Ernis¹⁷⁶, J. Ernst², M. Ernst²⁵, J. Ernwein¹³⁷, D. Errede¹⁶⁶, S. Errede¹⁶⁶,
E. Ertel⁸², M. Escalier¹¹⁶, H. Esch⁴³, C. Escobar¹²⁴, X. Espinal Curull¹², B. Esposito⁴⁷, F. Etienne⁸⁴, A.I. Etienvre¹³⁷,
E. Etzion¹⁵⁴, D. Evangelakou⁵⁴, H. Evans⁶⁰, L. Fabbri²⁰a,²⁰b, G. Facini³⁰, R.M. Fakhru dinov¹²⁹, S. Falciano¹³³a, Y. Fang³³a,
M. Fanti⁹⁰a,⁹⁰b, A. Farbin⁸, A. Farilla¹³⁵a, T. Farooque¹⁵⁹, S. Farrell¹⁶⁴, S.M. Farrington¹⁷¹, P. Farthouat³⁰, F. Fassi¹⁶⁸,
P. Fassnacht³⁰, D. Fassouliotis⁹, B. Fatholahzadeh¹⁵⁹, A. Favareto⁵⁰a,⁵⁰b, L. Fayard¹¹⁶, P. Federic¹⁴⁵a, O.L. Fedin¹²²,
W. Fedorko¹⁶⁹, M. Fehling-Kaschek⁴⁸, L. Feligioni⁸⁴, C. Feng³³d, E.J. Feng⁶, H. Feng⁸⁸, A.B. Fenyuk¹²⁹, W. Fernando⁶,
S. Ferrag⁵³, J. Ferrando⁵³, V. Ferrara⁴², A. Ferrari¹⁶⁷, P. Ferrari¹⁰⁶, R. Ferrari¹²⁰a, D.E. Ferreira de Lima⁵³, A. Ferrer¹⁶⁸,
D. Ferrere⁴⁹, C. Ferretti⁸⁸, A. Ferretto Parodi⁵⁰a,⁵⁰b, M. Fiascaris³¹, F. Fiedler⁸², A. Filipčič⁷⁴, M. Filipuzzi⁴², F. Filthaut¹⁰⁵,
M. Fincke-Keeler¹⁷⁰, K.D. Finelli⁴⁵, M.C.N. Fiolhais¹²⁵a,¹²⁵c, L. Fiorini¹⁶⁸, A. Firan⁴⁰, J. Fischer¹⁷⁶, M.J. Fisher¹¹⁰,
E.A. Fitzgerald²³, M. Flechl⁴⁸, I. Fleck¹⁴², P. Fleischmann¹⁷⁵, S. Fleischmann¹⁷⁶, G.T. Fletcher¹⁴⁰, G. Fletcher⁷⁵, T. Flick¹⁷⁶,
A. Floderus⁸⁰, L.R. Flores Castillo¹⁷⁴, A.C. Florez Bustos¹⁶⁰b, M.J. Flowerdew¹⁰⁰, T. Fonseca Martin¹⁷, A. Formica¹³⁷,
A. Forti⁸³, D. Fortin¹⁶⁰a, D. Fournier¹¹⁶, H. Fox⁷¹, P. Francavilla¹², M. Franchini²⁰a,²⁰b, S. Franchino³⁰, D. Francis³⁰,
M. Franklin⁵⁷, S. Franz⁶¹, M. Fraternali¹²⁰a,¹²⁰b, S. Fratina¹²¹, S.T. French²⁸, C. Friedrich⁴², F. Friedrich⁴⁴, D. Froidevaux³⁰,
J.A. Frost²⁸, C. Fukunaga¹⁵⁷, E. Fullana Torregrosa¹²⁸, B.G. Fulsom¹⁴⁴, J. Fuster¹⁶⁸, C. Gabaldon⁵⁵, O. Gabizon¹⁷³,
A. Gabrielli²⁰a,²⁰b, A. Gabrielli¹³³a,¹³³b, S. Gadatsch¹⁰⁶, T. Gadfort²⁵, S. Gadomski⁴⁹, G. Gagliardi⁵⁰a,⁵⁰b, P. Gagnon⁶⁰,
C. Galea⁹⁹, B. Galhardo¹²⁵a,¹²⁵c, E.J. Gallas¹¹⁹, V. Gallo¹⁷, B.J. Gallop¹³⁰, P. Gallus¹²⁷, G. Galster³⁶, K.K. Gan¹¹⁰,
R.P. Gandrajula⁶², J. Gao³³b, Y.S. Gao¹⁴⁴, e, F.M. Garay Walls⁴⁶, F. Garberson¹⁷⁷, C. García¹⁶⁸, J.E. García Navarro¹⁶⁸,
M. Garcia-Sciveres¹⁵, R.W. Gardner³¹, N. Garelli¹⁴⁴, V. Garonne³⁰, C. Gatti⁴⁷, G. Gaudio¹²⁰a, B. Gaur¹⁴², L. Gauthier⁹⁴,
P. Gauzzi¹³³a,¹³³b, I.L. Gavrilenko⁹⁵, C. Gay¹⁶⁹, G. Gaycken²¹, E.N. Gazis¹⁰, P. Ge³³d, m, Z. Gecse¹⁶⁹, C.N.P. Gee¹³⁰,
D.A.A. Geerts¹⁰⁶, Ch. Geich-Gimbel²¹, K. Gellerstedt¹⁴⁷a,¹⁴⁷b, C. Gemme⁵⁰a, A. Gemmell⁵³, M.H. Genest⁵⁵,
S. Gentile¹³³a,¹³³b, M. George⁵⁴, S. George⁷⁶, D. Gerbaudo¹⁶⁴, A. Gershon¹⁵⁴, H. Ghazlane¹³⁶b, N. Ghobbane³⁴,
B. Giacobbe²⁰a, S. Giagu¹³³a,¹³³b, V. Giangiobbe¹², P. Giannetti¹²³a,¹²³b, F. Gianotti³⁰, B. Gibbard²⁵, S.M. Gibson⁷⁶,
M. Gilchriese¹⁵, T.P.S. Gillam²⁸, D. Gillberg³⁰, A.R. Gillman¹³⁰, D.M. Gingrich³,⁴, N. Giokaris⁹, M.P. Giordani¹⁶⁵a,¹⁶⁵c,
R. Giordano¹⁰³a,¹⁰³b, F.M. Giorgi¹⁶, P. Giovannini¹⁰⁰, P.F. Giraud¹³⁷, D. Giugni⁹⁰a, C. Giuliani⁴⁸, M. Giunta⁹⁴, B.K. Gjelsten¹¹⁸,
I. Gkialas¹⁵⁵,⁷, L.K. Gladilin⁹⁸, C. Glasman⁸¹, J. Glatzer²¹, A. Glazov⁴², G.L. Glonti⁶⁴, M. Goblirsch-Kolb¹⁰⁰, J.R. Goddard⁷⁵,
J. Godfrey¹⁴³, J. Godlewski³⁰, C. Goeringer⁸², S. Goldfarb⁸⁸, T. Golling¹⁷⁷, D. Golubkov¹²⁹, A. Gomes¹²⁵a,¹²⁵b,¹²⁵d,
L.S. Gomez Fajardo⁴², R. Gonçalo⁷⁶, J. Goncalves Pinto Firmino Da Costa⁴², L. Gonella²¹, S. González de la Hoz¹⁶⁸,
G. Gonzalez Parra¹², M.L. Gonzalez Silva²⁷, S. Gonzalez-Sevilla⁴⁹, J.J. Goodson¹⁴⁹, L. Goossens³⁰, P.A. Gorbounov⁹⁶,
H.A. Gordon²⁵, I. Gorelov¹⁰⁴, G. Gorfine¹⁷⁶, B. Gorini³⁰, E. Gorini⁷²a,⁷²b, A. Gorišek⁷⁴, E. Gornicki³⁹, A.T. Goshaw⁶,
C. Gössling⁴³, M.I. Gostkin⁶⁴, M. Gouighri¹³⁶a, D. Goujdami¹³⁶c, M.P. Goulette⁴⁹, A.G. Goussiou¹³⁹, C. Goy⁵, S. Gozpinar²³,
H.M.X. Grabas¹³⁷, L. Graber⁵⁴, I. Grabowska-Bold³⁸a, P. Grafström²⁰a,²⁰b, K.-J. Grahn⁴², J. Gramling⁴⁹, E. Gramstad¹¹⁸,
F. Grancagnolo⁷²a, S. Grancagnolo¹⁶, V. Grassi¹⁴⁹, V. Gratchev¹²², H.M. Gray³⁰, J.A. Gray¹⁴⁹, E. Graziani¹³⁵a,
O.G. Grebenyuk¹²², Z.D. Greenwood⁷⁸,⁷, K. Gregersen³⁶, I.M. Gregor⁴², P. Grenier¹⁴⁴, J. Griffiths⁸, N. Grigalashvili⁶⁴,
A.A. Grillo¹³⁸, K. Grimm⁷¹, S. Grinstein¹²,⁰, Ph. Gris³⁴, Y.V. Grishkevich⁹⁸, J.-F. Grivaz¹¹⁶, J.P. Grohs⁴⁴, A. Grohsjean⁴²,
E. Gross¹⁷³, J. Grosse-Knetter⁵⁴, G.C. Grossi¹³⁴a,¹³⁴b, J. Groth-Jensen¹⁷³, Z.J. Grout¹⁵⁰, K. Grybel¹⁴², F. Guescini⁴⁹,
D. Guest¹⁷⁷, O. Gueta¹⁵⁴, C. Guicheney³⁴, E. Guido⁵⁰a,⁵⁰b, T. Guillemin¹¹⁶, S. Guindon², U. Gul⁵³, C. Gumpert⁴⁴,
J. Gunther¹²⁷, J. Guo³⁵, S. Gupta¹¹⁹, P. Gutierrez¹¹², N.G. Gutierrez Ortiz⁵³, C. Gutschow⁷⁷, N. Guttman¹⁵⁴, C. Guyot¹³⁷,
C. Gwenlan¹¹⁹, C.B. Gwilliam⁷³, A. Haas¹⁰⁹, C. Haber¹⁵, H.K. Hadavand⁸, P. Haefner²¹, S. Hageboeck²¹, Z. Hajduk³⁹,
H. Hakobyan¹⁷⁸, M. Haleem⁴¹, D. Hall¹¹⁹, G. Halladjian⁸⁹, K. Hamacher¹⁷⁶, P. Hamal¹¹⁴, K. Hamano⁸⁷, M. Hamer⁵⁴,
A. Hamilton¹⁴⁶a,⁰, S. Hamilton¹⁶², L. Han³³b, K. Hanagaki¹¹⁷, K. Hanawa¹⁵⁶, M. Hance¹⁵, P. Hanke⁵⁸a, J.R. Hansen³⁶,
J.B. Hansen³⁶, J.D. Hansen³⁶, P.H. Hansen³⁶, P. Hansson¹⁴⁴, K. Hara¹⁶¹, A.S. Hard¹⁷⁴, T. Harenberg¹⁷⁶, S. Harkusha⁹¹,
D. Harper⁸⁸, R.D. Harrington⁴⁶, O.M. Harris¹³⁹, P.F. Harrison¹⁷¹, F. Hartjes¹⁰⁶, A. Harvey⁵⁶, S. Hasegawa¹⁰², Y. Hasegawa¹⁴¹,
S. Hassani¹³⁷, S. Haug¹⁷, M. Hauschild³⁰, R. Hauser⁸⁹, M. Havranek²¹, C.M. Hawkes¹⁸, R.J. Hawkings³⁰, A.D. Hawkins⁸⁰,
T. Hayashi¹⁶¹, D. Hayden⁸⁹, C.P. Hays¹¹⁹, H.S. Hayward⁷³, S.J. Haywood¹³⁰, S.J. Head¹⁸, T. Heck⁸², V. Hedberg⁸⁰, L. Heelan⁸,
S. Heim¹²¹, B. Heinemann¹⁵, S. Heisterkamp³⁶, J. Hejbal¹²⁶, L. Helary²², C. Heller⁹⁹, M. Heller³⁰, S. Hellman¹⁴⁷a,¹⁴⁷b,
D. Hellmich²¹, C. Helsens³⁰, J. Henderson¹¹⁹, R.C.W. Henderson⁷¹, A. Henrichs¹⁷⁷, A.M. Henriques Correia³⁰,
S. Henrot-Versille¹¹⁶, C. Hensel⁵⁴, G.H. Herbert¹⁶, C.M. Hernandez⁸, Y. Hernández Jiménez¹⁶⁸, R. Herrberg-Schubert¹⁶,
G. Herten⁴⁸, R. Hertenberger⁹⁹, L. Hervas³⁰, G.G. Hesketh⁷⁷, N.P. Hessey¹⁰⁶, R. Hickling⁷⁵, E. Higón-Rodriguez¹⁶⁸,
J.C. Hill²⁸, K.H. Hiller⁴², S. Hillert²¹, S.J. Hillier¹⁸, I. Hinchliffe¹⁵, E. Hines¹²¹, M. Hirose¹¹⁷, D. Hirschbuehl¹⁷⁶, J. Hobbs¹⁴⁹,
N. Hod¹⁰⁶, M.C. Hodgkinson¹⁴⁰, P. Hodgson¹⁴⁰, A. Hoecker³⁰, M.R. Hoeferkamp¹⁰⁴, J. Hoffman⁴⁰, D. Hoffmann⁸⁴,
J.I. Hofmann⁵⁸a, M. Hohlfeld⁸², T.R. Holmes¹⁵, T.M. Hong¹²¹, L. Hooft van Huysduynen¹⁰⁹, J.-Y. Hostachy⁵⁵, S. Hou¹⁵²,
A. Hoummada¹³⁶a, J. Howard¹¹⁹, J. Howarth⁸³, M. Hrabovsky¹¹⁴, I. Hristova¹⁶, J. HRivnac¹¹⁶, T. Hryn'ova⁵, P.J. Hsu⁸²,
S.-C. Hsu¹³⁹, D. Hu³⁵, X. Hu²⁵, Y. Huang¹⁴⁶c, Z. Hubacek³⁰, F. Hubaut⁸⁴, F. Huegging²¹, A. Huettmann⁴², T.B. Huffman¹¹⁹,
E.W. Hughes³⁵, G. Hughes⁷¹, M. Huhtinen³⁰, T.A. Hülsing⁸², M. Hurwitz¹⁵, N. Huseynov⁶⁴,⁸, J. Huston⁸⁹, J. Huth⁵⁷,
G. Iacobucci⁴⁹, G. Iakovidis¹⁰, I. Ibragimov¹⁴², L. Iconovidou-Fayard¹¹⁶, J. Idaraga¹¹⁶, E. Ideal¹⁷⁷, P. Iengo¹⁰³a,
O. Igonkina¹⁰⁶, T. Iizawa¹⁷², Y. Ikegami⁶⁵, K. Ikematsu¹⁴², M. Ikeno⁶⁵, D. Iliadis¹⁵⁵, N. Ilic¹⁵⁹, Y. Inamaru⁶⁶, T. Ince¹⁰⁰,
P. Ioannou⁹, M. Iodice¹³⁵a, K. Iordanidou⁹, V. Ippolito¹³³a,¹³³b, A. Irles Quiles¹⁶⁸, C. Isaksson¹⁶⁷, M. Ishino⁶⁷, M. Ishitsuka¹⁵⁸,
R. Ishmukhametov¹¹⁰, C. Issever¹¹⁹, S. Istin¹⁹a, A.V. Ivashin¹²⁹, W. Iwanski³⁹, H. Iwasaki⁶⁵, J.M. Izen⁴¹, V. Izzo¹⁰³a,
B. Jackson¹²¹, J.N. Jackson⁷³, M. Jackson⁷³, P. Jackson¹, M.R. Jaekel³⁰, V. Jain², K. Jakobs⁴⁸, S. Jakobsen³⁶, T. Jakoubek¹²⁶,
J. Jakubek¹²⁷, D.O. Jamin¹⁵², D.K. Jana¹¹², E. Jansen⁷⁷, H. Jansen³⁰, J. Janssen²¹, M. Janus¹⁷¹, R.C. Jared¹⁷⁴, G. Jarlskog⁸⁰,
L. Jeanty⁵⁷, G.-Y. Jeng¹⁵¹, I. Jen-La Plante³¹, D. Jennens⁸⁷, P. Jenni⁴⁸,⁰, J. Jentzsch⁴³, C. Jeske¹⁷¹, S. Jézéquel⁵, M.K. Jha²⁰a,
H. Ji¹⁷⁴, W. Ji⁸², J. Jia¹⁴⁹, Y. Jiang³³b, M. Jimenez Belenguer⁴², S. Jin³³a, A. Jinaru²⁶a, O. Jinnouchi¹⁵⁸, M.D. Joergensen³⁶,
D. Joffe⁴⁰, K.E. Johansson¹⁴⁷a, P. Johansson¹⁴⁰, K.A. Johns⁷, K. Jon-And¹⁴⁷a,¹⁴⁷b, G. Jones¹⁷¹, R.W.L. Jones⁷¹, T.J. Jones⁷³,
P.M. Jorge¹²⁵a,¹²⁵b, K.D. Joshi⁸³, J. Jovicevic¹⁴⁸, X. Ju¹⁷⁴, C.A. Jung⁴³, R.M. Jungst³⁰, P. Jussel⁶¹, A. Juste Rozas¹²,⁰,
M. Kaci¹⁶⁸, A. Kaczmarska³⁹, P. Kadlecik³⁶, M. Kado¹¹⁶, H. Kagan¹¹⁰, M. Kagan¹⁴⁴, E. Kajomovitz⁴⁵, S. Kalinin¹⁷⁶,
S. Kama⁴⁰, N. Kanaya¹⁵⁶, M. Kaneda³⁰, S. Kaneti²⁸, T. Kanno¹⁵⁸, V.A. Kantserov⁹⁷, J. Kanzaki⁶⁵, B. Kaplan¹⁰⁹, A. Kapliy³¹,
D. Kar⁵³, K. Karakostas¹⁰, N. Karastathis¹⁰, M. Karnevskiy⁸², S.N. Karpov⁶⁴, K. Karthik¹⁰⁹, V. Kartvelishvili⁷¹,
A.N. Karyukhin¹²⁹, L. Kashif¹⁷⁴, G. Kasieczka⁵⁸b, R.D. Kass¹¹⁰, A. Kastanas¹⁴, Y. Kataoka¹⁵⁶, A. Katre⁴⁹, J. Katzy⁴²,
V. Kaushik⁷, K. Kawagoe⁶⁹, T. Kawamoto¹⁵⁶, G. Kawamura⁵⁴, S. Kazama¹⁵⁶, V.F. Kazanin¹⁰⁸, M.Y. Kazarinov⁶⁴, R. Keeler¹⁷⁰,
P.T. Keener¹²¹, R. Kehoe⁴⁰, M. Keil⁵⁴, J.S. Keller¹³⁹, H. Keoshkerian⁵, O. Kepka¹²⁶, B.P. Kerševan⁷⁴, S. Kersten¹⁷⁶,
K. Kessoku¹⁵⁶, J. Keung¹⁵⁹, F. Khalil-zada¹¹, H. Khandanyan¹⁴⁷a,¹⁴⁷b, A. Khanov¹¹³, D. Kharchenko⁶⁴, A. Khodinov⁹⁷,
A. Khomich⁵⁸a, T.J. Khoo²⁸, G. Khoriauli²¹, A. Khoroshilov¹⁷⁶, V. Khovanskiy⁹⁶, E. Khramov⁶⁴, J. Khubua⁵¹b,
H. Kim¹⁴⁷a,¹⁴⁷b, S.H. Kim¹⁶¹, N. Kimura¹⁷², O. Kind¹⁶, B.T. King⁷³, M. King⁶⁶, R.S.B. King¹¹⁹, S.B. King¹⁶⁹, J. Kirk¹³⁰,
A.E. Kiryunin¹⁰⁰, T. Kishimoto⁶⁶, D. Kisielewska³⁸a, T. Kitamura⁶⁶, T. Kittelmann¹²⁴, K. Kiuchi¹⁶¹, E. Kladiva¹⁴⁵b,
M. Klein⁷³, U. Klein⁷³, K. Kleinknecht⁸², P. Klimek¹⁴⁷a,¹⁴⁷b, A. Klimentov²⁵, R. Klingenberg⁴³, J.A. Klinger⁸³,
E.B. Klinkby³⁶, T. Klioutchnikova³⁰, P.F. Klok¹⁰⁵, E.-E. Kluge⁵⁸a, P. Kluit¹⁰⁶, S. Kluth¹⁰⁰, E. Kneringer⁶¹, E.B.F.G. Knoops⁸⁴,
A. Knue⁵⁴, T. Kobayashi¹⁵⁶, M. Kobel⁴⁴, M. Kocian¹⁴⁴, P. Kodys¹²⁸, S. Koenig⁸², P. Koevesarki²¹, T. Koffas²⁹, E. Koffeman¹⁰⁶,
L.A. Kogan¹¹⁹, S. Kohlmann¹⁷⁶, Z. Kohout¹²⁷, T. Kohriki⁶⁵, T. Koi¹⁴⁴, H. Kolanoski¹⁶, I. Koletsou⁵, J. Koll⁸⁹, A.A. Komar⁹⁵,*,
Y. Komori¹⁵⁶, T. Kondo⁶⁵, K. Köneke⁴⁸, A.C. König¹⁰⁵, T. Kono⁶⁵,r, R. Konoplich¹⁰⁹,s, N. Konstantinidis⁷⁷, R. Kopeliansky¹⁵³, S. Koperny³⁸a, L. Köpke⁸², A.K. Kopp⁴⁸, K. Korcyl³⁹, K. Kordas¹⁵⁵, A. Korn⁴⁶, A.A. Korol¹⁰⁸, I. Korolkov¹², E.V. Korolkova¹⁴⁰, V.A. Korotkov¹²⁹, O. Kortner¹⁰⁰, S. Kortner¹⁰⁰, V.V. Kostyukhin²¹, S. Kotov¹⁰⁰, V.M. Kotov⁶⁴, A. Kotwal⁴⁵, C. Kourkoumelis⁹, V. Kouskoura¹⁵⁵, A. Koutsman¹⁶⁰a, R. Kowalewski¹⁷⁰, T.Z. Kowalski³⁸a, W. Kozanecki¹³⁷, A.S. Kozhin¹²⁹, V. Kral¹²⁷, V.A. Kramarenko⁹⁸, G. Kramberger⁷⁴, M.W. Krasny⁷⁹, A. Krasznahorkay¹⁰⁹, J.K. Kraus²¹, A. Kravchenko²⁵, S. Kreiss¹⁰⁹, J. Kretzschmar⁷³, K. Kreutzfeldt⁵², N. Krieger⁵⁴, P. Krieger¹⁵⁹, K. Kroeninger⁵⁴, H. Kroha¹⁰⁰, J. Kroll¹²¹, J. Kroseberg²¹, J. Krstic¹³a, U. Kruchonak⁶⁴, H. Krüger²¹, T. Kruker¹⁷, N. Krumnack⁶³, Z.V. Krumshteyn⁶⁴, A. Kruse¹⁷⁴, M.C. Kruse⁴⁵, M. Kruskal²², T. Kubota⁸⁷, S. Kuday⁴⁴, S. Kuehn⁴⁸, A. Kuge158c, T. Kuhl42, V. Kukhtin⁶⁴, Y. Kulchitsky91, S. Kuleshov32b, M. Kuna133a,133b, J. Kunkle121, A. Kupco126, H. Kurashige66, M. Kurata161, Y.A. Kurochkin91, R. Kurumida66, V. Kus126, E.S. Kuwertz148, M. Kuze158, J. Kvita143, R. Kwee16, A. La Rosa49, L. La Rotonda37a,37b, L. Labarga81, S. Lablak136a, C. Lacasta168, F. Lacava133a,133b, J. Lacey29, H. Lacker16, D. Lacour79, V.R. Lacuesta168, E. Ladygin64, R. Lafaye5, B. Laforge79, T. Lagouri177, S. Lai48, H. Laier58a, E. Laisne55, L. Lambourne77, C.L. Lampen7, W. Lampl7, E. Lançon137, U. Landgraf48, M.P.J. Landon75, V.S. Lang58a, C. Lange42, A.J. Lankford164, F. Lanni25, K. Lantzsch30, A. Lanza120a, S. Laplace79, C. Lapoire21, J.F. Laporte137, T. Lari90a, A. Larner119, M. Lassnig30, P. Laurelli47, V. Lavorini37a,37b, W. Lavrijsen15, P. Laycock73, B.T. Le55, O. Le Dortz79, E. Le Guirriec84, E. Le Menedeu12, T. LeCompte6, F. Ledroit-Guillon55, C.A. Lee152, H. Lee106, J.S.H. Lee117, S.C. Lee152, L. Lee177, G. Lefebvre79, M. Lefebvre170, F. Legger99, C. Leggett15, A. Lehan73, M. Lehmacher21, G. Lehmann Miotto30, A.G. Leister177, M.A.L. Leite24d, R. Leitner128, D. Lellouch173, B. Lemmer54, V. Lendermann58a, K.J.C. Leney146c, T. Lenz106, G. Lenzen176, B. Lenzi30, R. Leone7, K. Leonhardt44, S. Leontsinis10, C. Leroy94, J-R. Lessard170, C.G. Lester28, C.M. Lester121, J. Levêque5, D. Levin88, L.J. Levinson173, A. Lewis119, G.H. Lewis109, A.M. Leyko21, M. Leyton16, B. Li33b,t, B. Li84, H. Li149, H.L. Li31, S. Li45, X. Li88, Z. Liang119,u, H. Liao34, B. Liberti134a, P. Lichard30, K. Lie166, J. Liebal21, W. Liebig14, C. Limbach21, A. Limosani87, M. Limper62, S.C. Lin152,v, F. Linde106, B.E. Lindquist149, J.T. Linnemann89, E. Lipeles121, A. Lipniacka14, M. Lisovyi42, T.M. Liss166, D. Lissauer25, A. Lister169, A.M. Litke138, B. Liu152, D. Liu152, J.B. Liu33b, K. Liu33b,w, L. Liu88, M. Liu45, M. Liu33b, Y. Liu33b, M. Livan120a,120b, S.S.A. Livermore119, A. Lleres55, J. Llorente Merino81, S.L. Lloyd75, F. Lo Sterzo152, E. Lobodzinska42, P. Loch7, W.S. Lockman138, T. Loddenkoetter21, F.K. Loebinger83, A.E. Loevschall-Jensen36, A. Loginov177, C.W. Loh169, T. Lohse16, K. Lohwasser48, M. Lokajicek126, V.P. Lombardo5, J.D. Long88, R.E. Long71, L. Lopes125a, D. Lopez Mateos57, B. Lopez Paredes140, J. Lorenz99, N. Lorenzo Martinez116, M. Losada163, P. Loscutoff15, M.J. Losty160a,*, X. Lou41, A. Lounis116, J. Love6, P.A. Love71, A.J. Lowe144,e, F. Lu33a, H.J. Lubatti139, C. Luci133a,133b, A. Lucotte55, D. Ludwig42, I. Ludwig48, F. Luehring60, W. Lukas61, L. Luminari133a, E. Lund118, J. Lundberg147a,147b, O. Lundberg147a,147b, B. Lund-Jensen148, M. Lungwitz82, D. Lynn25, R. Lysak126, E. Lytken80, H. Ma25, L.L. Ma33d, G. Maccarrone47, A. Macchiolo100, B. Maček74, J. Machado Miguens125a,125b, D. Macina30, R. Mackeprang36, R. Madar48, R.J. Madaras15, H.J. Maddocks71, W.F. Mader44, A. Madsen167, M. Maeno8, T. Maeno25, L. Magnoni164, E. Magradze54, K. Mahboubi48, J. Mahlstedt106, S. Mahmoud73, G. Mahout18, C. Maiani137, C. Maidantchik24a, A. Maio125a,125b,125d, S. Majewski115, Y. Makida65, N. Makovec116, P. Mal137,x, B. Malaescu79, Pa. Malecki39, V.P. Maleev122, F. Malek55, U. Mallik62, D. Malon6, C. Malone144, S. Maltezos10, V.M. Malyshev108, S. Malyukov30, J. Mamuzic13b, L. Mandelli90a, I. Mandić74, R. Mandrysch62, J. Maneira125a,125b, A. Manfredini100, L. Manhaes de Andrade Filho24b, J.A. Manjarres Ramos137, A. Mann99, P.M. Manning138, A. Manousakis-Katsikakis9, B. Mansoulie137, R. Mantifel86, L. Mapelli30, L. March168, J.F. Marchand29, F. Marchese134a,134b, G. Marchiori79, M. Marcisovsky126, C.P. Marino170, C.N. Marques125a, F. Marroquim24a, Z. Marshall15, L.F. Marti17, S. Marti-Garcia168, B. Martin30, B. Martin89, J.P. Martin94, T.A. Martin171, V.J. Martin46, B. Martin dit Latour49, H. Martinez137, M. Martinez12,o, S. Martin-Haugh130, A.C. Martyniuk170, M. Marx139, F. Marzano133a, A. Marzin112, L. Masetti82, T. Mashimo156, R. Mashinistov95, J. Masik83, A.L. Maslennikov108, I. Massa20a,20b, N. Massol5, P. Mastrandrea149, A. Mastroberardino37a,37b, T. Masubuchi156, H. Matsunaga156, T. Matsushita66, P. Mättig176, S. Mättig42, J. Mattmann82, C. Mattravers119,c, J. Maurer84, S.J. Maxfield73, D.A. Maximov108,f, R. Mazini152, L. Mazzaferro134a,134b, M. Mazzanti90a, G. Mc Goldrick159, S.P. Mc Kee88, A. McCarn88, R.L. McCarthy149, T.G. McCarthy29, N.A. McCubbin130, K.W. McFarlane56,*, J.A. Mcfayden140, G. Mchedlidze51b, T. Mclaughlan18, S.J. McMahon130, R.A. McPherson170,i, A. Meade85, J. Mechnich106, M. Mechtel176, M. Medinnis42, S. Meehan31, R. Meera-Lebbai112, S. Mehlhase36, A. Mehta73, K. Meier58a, C. Meineck99, B. Meirose80, C. Melachrinos31, B.R. Mellado Garcia146c, F. Meloni90a,90b, L. Mendoza Navas163, A. Mengarelli20a,20b, S. Menke100, E. Meoni162, K.M. Mercurio57, S. Mergelmeyer21, N. Meric137, P. Mermod49, L. Merola103a,103b, C. Meroni90a, F.S. Merritt31, H. Merritt110, A. Messina30,y, J. Metcalfe25, A.S. Mete164, C. Meyer82, C. Meyer31, J-P. Meyer137, J. Meyer30, J. Meyer54, S. Michal30, R.P. Middleton130, S. Migas73, L. Mijović137, G. Mikenberg173, M. Mikestikova126, M. Mikuž74, D.W. Miller31, C. Mills57, A. Milov173, D.A. Milstead147a,147b, D. Milstein173, A.A. Minaenko129, M. Miñano Moya168, I.A. Minashvili64, A.I. Mincer109, B. Mindur38a, M. Mineev64, Y. Ming174, L.M. Mir12, G. Mirabelli133a, T. Mitani172, J. Mitrevski138, V.A. Mitsou168, S. Mitsui65, P.S. Miyagawa140, J.U. Mjörnmark80, T. Moa147a,147b, V. Moeller28, S. Mohapatra149, W. Mohr48, S. Molander147a,147b, R. Moles-Valls168, A. Molfetas30, K. Mönig42, C. Monini55, J. Monk36, E. Monnier84, J. Montejo Berlingen12, F. Monticelli70, S. Monzani20a,20b, R.W. Moore3, C. Mora Herrera49, A. Moraes53, N. Morange62, J. Morel54, D. Moreno82, M. Moreno Llácer168, P. Morettini50a, M. Morgenstern44, M. Morii57, S. Moritz82, A.K. Morley148, G. Mornacchi30, J.D. Morris75, L. Morvaj102, H.G. Moser100, M. Mosidze51b, J. Moss110, R. Mount144, E. Mountricha25, S.V. Mouraviev95,*, E.J.W. Moyse85, R.D. Mudd18, F. Mueller58a, J. Mueller124, K. Mueller21, T. Mueller28, T. Mueller82, D. Muenstermann49, Y. Munwes154, J.A. Murillo Quijada18,
W.J. Murray¹³⁰, I. Mussche¹⁰⁶, E. Musto¹⁵³, A.G. Myagkov¹²⁹,⁻z, M. Myska¹²⁶, O. Nackenhorst⁵⁴, J. Nadal⁵⁴, K. Nagai⁶¹, R. Nagai¹⁵⁸, Y. Nagai⁸⁴, K. Nagano⁶⁵, A. Nagarkar¹¹⁰, Y. Nagasaka⁵⁹, M. Nagel¹⁰⁰, A.M. Nairz³⁰, Y. Nakahama³⁰, K. Nakamura⁶⁵, T. Nakamura¹⁵⁶, I. Nakano¹¹¹, H. Namasivayam⁴¹, G. Nanava²¹, A. Napier¹⁶², R. Narayan⁵⁸b, M. Nash⁷⁷,ᶜ, T. Nattermann²¹, T. Naumann⁴², G. Navarro¹⁶³, H.A. Neal⁸⁸, P.Yu. Nechaeva⁹⁵, T.J. Neep⁸³, A. Negri¹²⁰a,¹²⁰b, G. Negri³⁰, M. Negrini²⁰a, S. Nektarijevic⁴⁹, A. Nelson¹⁶⁴, T.K. Nelson¹⁴⁴, S. Nemecek¹²⁶, P. Nemethy¹⁰⁹, A.A. Nepomuceno²⁴a, M. Nessi³⁰,aa, M.S. Neubauer¹⁶⁶, M. Neumann¹⁷⁶, A. Neusiedl⁸², R.M. Neves¹⁰⁹, P. Nevski²⁵, F.M. Newcomer¹²¹, P.R. Newman¹⁸, D.H. Nguyen⁶, V. Nguyen Thi Hong¹³⁷, R.B. Nickerson¹¹⁹, R. Nicolaidou¹³⁷, B. Nicquevert³⁰, J. Nielsen¹³⁸, N. Nikiforou³⁵, A. Nikiforov¹⁶, V. Nikolaenko¹²⁹,⁻z, I. Nikolic-Audit⁷⁹, K. Nikolics⁴⁹, K. Nikolopoulos¹⁸, P. Nilsson⁸, Y. Ninomiya¹⁵⁶, A. Nisati¹³³a, R. Nisius¹⁰⁰, T. Nobe¹⁵⁸, L. Nodulman⁶, M. Nomachi¹¹⁷, I. Nomidis¹⁵⁵, S. Norberg¹¹², M. Nordberg³⁰, J. Novakova¹²⁸, M. Nozaki⁶⁵, L. Nozka¹¹⁴, K. Ntekas¹⁰, A.-E. Nuncio-Quiroz²¹, G. Nunes Hanninger⁸⁷, T. Nunnemann⁹⁹, E. Nurse⁷⁷, B.J. O'Brien⁴⁶, F. O'grady⁷, D.C. O'Neil¹⁴³, V. O'Shea⁵³, L.B. Oakes⁹⁹, F.G. Oakham²⁹,ᵈ, H. Oberlack¹⁰⁰, J. Ocariz⁷⁹, A. Ochi⁶⁶, M.I. Ochoa⁷⁷, S. Oda⁶⁹, S. Odaka⁶⁵, H. Ogren⁶⁰, A. Oh⁸³, S.H. Oh⁴⁵, C.C. Ohm³⁰, T. Ohshima¹⁰², W. Okamura¹¹⁷, H. Okawa²⁵, Y. Okumura³¹, T. Okuyama¹⁵⁶, A. Olariu²⁶a, A.G. Olchevski⁶⁴, S.A. Olivares Pino⁴⁶, M. Oliveira¹²⁵a,¹²⁵c, l, D. Oliveira Damazio²⁵, E. Oliver Garcia¹⁶⁸, D. Olivito¹²¹, A. Olszewski³⁹, J. Olszowska³⁹, A. Onofre¹²⁵a,¹²⁵e, P.U.E. Onyisi³¹,ab, C.J. Oram¹⁶⁰a, M.J. Oreglia³¹, Y. Oren¹⁵⁴, D. Orestano¹³⁵a,¹³⁵b, N. Orlando⁷²a,⁷²b, C. Oropeza Barrera⁵³, R.S. Orr¹⁵⁹, B. Osculati⁵⁰a,⁵⁰b, R. Ospanov¹²¹, G. Otero y Garzon²⁷, H. Otono⁶⁹, M. Ouchrif¹³⁶d, E.A. Ouellette¹⁷⁰, F. Ould-Saada¹¹⁸, A. Ouraou¹³⁷, K.P. Oussoren¹⁰⁶, Q. Ouyang³³a, A. Ovcharova¹⁵, M. Owen⁸³, S. Owen¹⁴⁰, V.E. Ozcan¹⁹a, N. Ozturk⁸, K. Pachal¹¹⁹, A. Pacheco Pages¹², C. Padilla Aranda¹², S. Pagan Griso¹⁵, E. Paganis¹⁴⁰, C. Pahl¹⁰⁰, F. Paige²⁵, P. Pais⁸⁵, K. Pajchel¹¹⁸, G. Palacino¹⁶⁰b, S. Palestini³⁰, D. Pallin³⁴, A. Palma¹²⁵a,¹²⁵b, J.D. Palmer¹⁸, Y.B. Pan¹⁷⁴, E. Panagiotopoulou¹⁰, J.G. Panduro Vazquez⁷⁶, P. Pani¹⁰⁶, N. Panikashvili⁸⁸, S. Panitkin²⁵, D. Pantea²⁶a, Th.D. Papadopoulou¹⁰, K. Papageorgiou¹⁵⁵,ⁿ, A. Paramonov⁶, D. Paredes Hernandez³⁴, M.A. Parker²⁸, F. Parodi⁵⁰a,⁵⁰b, J.A. Parsons³⁵, U. Parzefall⁴⁸, S. Pashapour⁵⁴, E. Pasqualucci¹³³a, S. Passaggio⁵⁰a, A. Passeri¹³⁵a, F. Pastore¹³⁵a,¹³⁵b,*, Fr. Pastore⁷⁶, G. Pásztor⁴⁹,ac, S. Pataraia¹⁷⁶, N.D. Patel¹⁵¹, J.R. Pater⁸³, S. Patricelli¹⁰³a,¹⁰³b, T. Pauly³⁰, J. Pearce¹⁷⁰, M. Pedersen¹¹⁸, S. Pedraza Lopez¹⁶⁸, R. Pedro¹²⁵a,¹²⁵b, S.V. Peleganchuk¹⁰⁸, D. Pelikan¹⁶⁷, H. Peng³³b, B. Penning³¹, J. Penwell⁶⁰, D.V. Perepelitsa³⁵, T. Perez Cavalcanti⁴², E. Perez Codina¹⁶⁰a, M.T. Pérez García-Estañ¹⁶⁸, V. Perez Reale³⁵, L. Perini⁹⁰a,⁹⁰b, H. Pernegger³⁰, R. Perrino⁷²a, R. Peschke⁴², V.D. Peshekhonov⁶⁴, K. Peters³⁰, R.F.Y. Peters⁵⁴,ad, B.A. Petersen³⁰, J. Petersen³⁰, T.C. Petersen³⁶, E. Petit⁵, A. Petridis¹⁴⁷a,¹⁴⁷b, C. Petridou¹⁵⁵, E. Petrolo¹³³a, F. Petrucci¹³⁵a,¹³⁵b, M. Petteni¹⁴³, R. Pezoa³²b, P.W. Phillips¹³⁰, G. Piacquadio¹⁴⁴, E. Pianori¹⁷¹, A. Picazio⁴⁹, E. Piccaro⁷⁵, M. Piccinini²⁰a,²⁰b, S.M. Piec⁴², R. Piegaia²⁷, D.T. Pignotti¹¹⁰, J.E. Pilcher³¹, A.D. Pilkington⁷⁷, J. Pina¹²⁵a,¹²⁵b,¹²⁵d, M. Pinamonti¹⁶⁵a,¹⁶⁵c, ae, A. Pinder¹¹⁹, J.L. Pinfold³, A. Pingel³⁶, B. Pinto¹²⁵a, C. Pizio⁹⁰a,⁹⁰b, M.-A. Pleier²⁵, V. Pleskot¹²⁸, E. Plotnikova⁶⁴, P. Plucinski¹⁴⁷a,¹⁴⁷b, S. Poddar⁵⁸a, F. Podlyski³⁴, R. Poettgen⁸², L. Poggioli¹¹⁶, D. Pohl²¹, M. Pohl⁴⁹, G. Polesello¹²⁰a, A. Policicchio³⁷a,³⁷b, R. Polifka¹⁵⁹, A. Polini²⁰a, C.S. Pollard⁴⁵, V. Polychronakos²⁵, D. Pomeroy²³, K. Pommès³⁰, L. Pontecorvo¹³³a, B.G. Pope⁸⁹, G.A. Popeneciu²⁶b, D.S. Popovic¹³a, A. Poppleton³⁰, X. Portell Bueso¹², G.E. Pospelov¹⁰⁰, S. Pospisil¹²⁷, K. Potamianos¹⁵, I.N. Potrap⁶⁴, C.J. Potter¹⁵⁰, C.T. Potter¹¹⁵, G. Poulard³⁰, J. Poveda⁶⁰, V. Pozdnyakov⁶⁴, R. Prabhu⁷⁷, P. Pralavorio⁸⁴, A. Pranko¹⁵, S. Prasad³⁰, R. Pravahan⁸, S. Prell⁶³, D. Price⁸³, J. Price⁷³, L.E. Price⁶, D. Prieur¹²⁴, M. Primavera⁷²a, M. Proissl⁴⁶, K. Prokofiev¹⁰⁹, F. Prokoshin³²b, E. Protopapadaki¹³⁷, S. Protopopescu²⁵, J. Proudfoot⁶, X. Prudent⁴⁴, M. Przybycien³⁸a, H. Przysiezniak⁵, S. Psoroulas²¹, E. Ptacek¹¹⁵, E. Pueschel⁸⁵, D. Puldon¹⁴⁹, M. Purohit²⁵,af, P. Puzo¹¹⁶, Y. Pylypchenko⁶², J. Qian⁸⁸, A. Quadt⁵⁴, D.R. Quarrie¹⁵, W.B. Quayle¹⁴⁶c, D. Quilty⁵³, V. Radeka²⁵, V. Radescu⁴², S.K. Radhakrishnan¹⁴⁹, P. Radloff¹¹⁵, F. Ragusa⁹⁰a,⁹⁰b, G. Rahal¹⁷⁹, S. Rajagopalan²⁵, M. Rammensee⁴⁸, M. Rammes¹⁴², A.S. Randle-Conde⁴⁰, C. Rangel-Smith⁷⁹, K. Rao¹⁶⁴, F. Rauscher⁹⁹, T.C. Rave⁴⁸, T. Ravenscroft⁵³, M. Raymond³⁰, A.L. Read¹¹⁸, D.M. Rebuzzi¹²⁰a,¹²⁰b, A. Redelbach¹⁷⁵, G. Redlinger²⁵, R. Reece¹²¹, K. Reeves⁴¹, A. Reinsch¹¹⁵, H. Reisin²⁷, I. Reisinger⁴³, M. Relich¹⁶⁴, C. Rembser³⁰, Z.L. Ren¹⁵², A. Renaud¹¹⁶, M. Rescigno¹³³a, S. Resconi⁹⁰a, B. Resende¹³⁷, P. Reznicek⁹⁹, R. Rezvani⁹⁴, R. Richter¹⁰⁰, M. Ridel⁷⁹, P. Rieck¹⁶, M. Rijssenbeek¹⁴⁹, A. Rimoldi¹²⁰a,¹²⁰b, L. Rinaldi²⁰a, E. Ritsch⁶¹, I. Riu¹², G. Rivoltella⁹⁰a,⁹⁰b, F. Rizatdinova¹¹³, E. Rizvi⁷⁵, S.H. Robertson⁸⁶,ᵢ, A. Robichaud-Veronneau¹¹⁹, D. Robinson²⁸, J.E.M. Robinson⁸³, A. Robson⁵³, J.G. Rocha de Lima¹⁰⁷, C. Roda¹²³a,¹²³b, D. Roda Dos Santos¹²⁶, L. Rodrigues³⁰, S. Roe³⁰, O. Røhne¹¹⁸, S. Rolli¹⁶², A. Romaniouk⁹⁷, M. Romano²⁰a,²⁰b, G. Romeo²⁷, E. Romero Adam¹⁶⁸, N. Rompotis¹³⁹, L. Roos⁷⁹, E. Ros¹⁶⁸, S. Rosati¹³³a, K. Rosbach⁴⁹, A. Rose¹⁵⁰, M. Rose⁷⁶, P.L. Rosendahl¹⁴, O. Rosenthal¹⁴², V. Rossetti¹⁴⁷a,¹⁴⁷b, E. Rossi¹⁰³a,¹⁰³b, L.P. Rossi⁵⁰a, R. Rosten¹³⁹, M. Rotaru²⁶a, I. Roth¹⁷³, J. Rothberg¹³⁹, D. Rousseau¹¹⁶, C.R. Royon¹³⁷, A. Rozanov⁸⁴, Y. Rozen¹⁵³, X. Ruan¹⁴⁶c, F. Rubbo¹², I. Rubinskiy⁴², V.I. Rud⁹⁸, C. Rudolph⁴⁴, M.S. Rudolph¹⁵⁹, F. Rühr⁷, A. Ruiz-Martinez⁶³, L. Rumyantsev⁶⁴, Z. Rurikova⁴⁸, N.A. Rusakovitch⁶⁴, A. Ruschke⁹⁹, J.P. Rutherfoord⁷, N. Ruthmann⁴⁸, P. Ruzicka¹²⁶, Y.F. Ryabov¹²², M. Rybar¹²⁸, G. Rybkin¹¹⁶, N.C. Ryder¹¹⁹, A.F. Saavedra¹⁵¹, S. Sacerdoti²⁷, A. Saddique³, I. Sadeh¹⁵⁴, H.F-W. Sadrozinski¹³⁸, R. Sadykov⁶⁴, F. Safai Tehrani¹³³a, H. Sakamoto¹⁵⁶, Y. Sakurai¹⁷², G. Salamanna⁷⁵, A. Salamon¹³⁴a, M. Saleem¹¹², D. Salek¹⁰⁶, P.H. Sales De Bruin¹³⁹, D. Salihagic¹⁰⁰, A. Salnikov¹⁴⁴, J. Salt¹⁶⁸, B.M. Salvachua Ferrando⁶, D. Salvatore³⁷a,³⁷b, F. Salvatore¹⁵⁰, A. Salvucci¹⁵⁰, A. Salzburger³⁰, D. Sampsonidis¹⁵⁵, A. Sanchez¹⁰³a,¹⁰³b, J. Sánchez¹⁶⁸, V. Sanchez Martinez¹⁶⁸, H. Sandaker¹⁴, H.G. Sander⁸², M.P. Sanders⁹⁹, M. Sandhoff¹⁷⁶, T. Sandoval²⁸, C. Sandoval¹⁶³, R. Sandstroem¹⁰⁰, D.P.C. Sankey¹³⁰, A. Sansoni⁴⁷, C. Santoni³⁴, R. Santonico¹³⁴a,¹³⁴b, H. Santos¹²⁵a, I. Santoyo Castillo¹⁵⁰, K. Sapp¹²⁴, A. Sapronov⁶⁴, J.G. Saraiva¹²⁵a,¹²⁵d, E. Sarkisyan-Grinbaum⁸, B. Sarrazin²¹, G. Sartisohn¹⁷⁶, O. Sasaki⁶⁵, Y. Sasaki¹⁵⁶, N. Sasao⁶⁷, I. Satsounkevitch⁹¹, G. Sauvage⁵,*, E. Sauvan⁵, J.B. Sauvan¹¹⁶, P. Savard¹⁵⁹, d, V. Savinov¹²⁴, D.O. Savu³⁰,
C. Sawyer¹¹⁹, L. Sawyer⁷⁸,⁸, D.H. Saxon⁵³, J. Saxon¹²¹, C. Sbarra²⁰a, A. Sbrizzi³, T. Scanlon³⁰, D.A. Scannicchio¹⁶⁴, M. Scarcella¹⁵¹, J. Schaarschmidt¹⁷³, P. Schacht¹⁰⁰, D. Schaefer¹²¹, A. Schaelicke⁴⁶, S. Schaepe²¹, S. Schaetzel⁵⁸b, U. Schäfer⁸², A.C. Schaffer¹¹⁶, D. Schaile⁹⁹, R.D. Schamberger¹⁴⁹, V. Scharf⁵⁸a, V.A. Schegelsky¹²², D. Scheirich⁸⁸, M. Schernau¹⁶⁴, M.I. Scherzer³⁵, C. Schiavi⁵⁰a,⁵⁰b, J. Schieck⁹⁹, C. Schillo⁴⁸, M. Schioppa³⁷a,³⁷b, S. Schlenker³⁰, E. Schmidt⁴⁸, K. Schmieden³⁰, C. Schmitt⁸², C. Schmitt⁹⁹, S. Schmitt⁵⁸b, B. Schneider¹⁷, Y.J. Schnellbach⁷³, U. Schnoor⁴⁴, L. Schoeffel¹³⁷, A. Schoening⁵⁸b, B.D. Schoenrock⁸⁹, A.L.S. Schorlemmer⁵⁴, M. Schott⁸², D. Schouten¹⁶⁰a, J. Schovancova²⁵, M. Schram⁸⁶, S. Schramm¹⁵⁹, M. Schreyer¹⁷⁵, C. Schroeder⁸², N. Schroer⁵⁸c, N. Schuh⁸², M.J. Schultens²¹, H.-C. Schultz-Coulon⁵⁸a, H. Schulz¹⁶, M. Schumacher⁴⁸, B.A. Schumm¹³⁸, Ph. Schune¹³⁷, A. Schwartzman¹⁴⁴, Ph. Schwegler¹⁰⁰, Ph. Schwemling¹³⁷, R. Schwienhorst⁸⁹, J. Schwindling¹³⁷, T. Schwindt²¹, M. Schwoerer⁵, F.G. Sciacca¹⁷, E. Scifo¹¹⁶, G. Sciolla²³, W.G. Scott¹³⁰, F. Scutti²¹, J. Searcy⁸⁸, G. Sedov⁴², E. Sedykh¹²², S.C. Seidel¹⁰⁴, A. Seiden¹³⁸, F. Seifert⁴⁴, J.M. Seixas²⁴a, G. Sekhniaidze¹⁰³a, S.J. Sekula⁴⁰, K.E. Selbach⁴⁶, D.M. Seliverstov¹²², G. Sellers⁷³, M. Seman¹⁴⁵b, N. Semprini-Cesari²⁰a,²⁰b, C. Serfon³⁰, L. Serin¹¹⁶, L. Serkin⁵⁴, T. Serre⁸⁴, R. Seuster¹⁶⁰a, H. Severini¹¹², F. Sforza¹⁰⁰, A. Sfyrla³⁰, E. Shabalina⁵⁴, M. Shamim¹¹⁵, L.Y. Shan³³a, J.T. Shank²², Q.T. Shao⁸⁷, M. Shapiro¹⁵, P.B. Shatalov⁹⁶, K. Shaw¹⁶⁵a,¹⁶⁵c, P. Sherwood⁷⁷, S. Shimizu⁶⁶, M. Shimojima¹⁰¹, T. Shin⁵⁶, M. Shiyakova⁶⁴, A. Shmeleva⁹⁵, M.J. Shochet³¹, D. Short¹¹⁹, S. Shrestha⁶³, E. Shulga⁹⁷, M.A. Shupe⁷, S. Shushkevich⁴², P. Sicho¹²⁶, D. Sidorov¹¹³, A. Sidoti¹³³a, F. Siegert⁴⁸, Dj. Sijacki¹³a, O. Silbert¹⁷³, J. Silva¹²⁵a,¹²⁵d, Y. Silver¹⁵⁴, D. Silverstein¹⁴⁴, S.B. Silverstein¹⁴⁷a, V. Simak¹²⁷, O. Simard⁵, Lj. Simic¹³a, S. Simion¹¹⁶, E. Simioni⁸², B. Simmons⁷⁷, R. Simoniello⁹⁰a,⁹⁰b, M. Simonyan³⁶, P. Sinervo¹⁵⁹, N.B. Sinev¹¹⁵, V. Sipica¹⁴², G. Siragusa¹⁷⁵, A. Sircar⁷⁸, A.N. Sisakyan⁶⁴,*, S.Yu. Sivoklokov⁹⁸, J. Sjölin¹⁴⁷a,¹⁴⁷b, T.B. Sjursen¹⁴, L.A. Skinnari¹⁵, H.P. Skottowe⁵⁷, K.Yu. Skovpen¹⁰⁸, P. Skubic¹¹², M. Slater¹⁸, T. Slavicek¹²⁷, K. Sliwa¹⁶², V. Smakhtin¹⁷³, B.H. Smart⁴⁶, L. Smestad¹¹⁸, S.Yu. Smirnov⁹⁷, Y. Smirnov⁹⁷, L.N. Smirnova⁹⁸,ag, O. Smirnova⁸⁰, K.M. Smith⁵³, M. Smizanska⁷¹, K. Smolek¹²⁷, A.A. Snesarev⁹⁵, G. Snidero⁷⁵, J. Snow¹¹², S. Snyder²⁵, R. Sobie¹⁷⁰, i, F. Socher⁴⁴, J. Sodomka¹²⁷, A. Soffer¹⁵⁴, D.A. Soh¹⁵², u, C.A. Solans³⁰, M. Solar¹²⁷, J. Solc¹²⁷, E.Yu. Soldatov⁹⁷, U. Soldevila¹⁶⁸, E. Solfaroli Camillocci¹³³a,¹³³b, A.A. Solodkov¹²⁹, O.V. Solovyanov¹²⁹, V. Solovyev¹²², N. Soni¹, A. Sood¹⁵, V. Sopko¹²⁷, B. Sopko¹²⁷, M. Sosebee⁸, R. Soualah¹⁶⁵a,¹⁶⁵c, P. Soueid⁹⁴, A.M. Soukharev¹⁰⁸, D. South⁴², S. Spagnolo⁷²a,⁷²b, F. Spanò⁷⁶, W.R. Spearman⁵⁷, R. Spighi²⁰a, G. Spigo³⁰, M. Spousta¹²⁸, T. Spreitzer¹⁵⁹, B. Spurlock⁸, R.D. St. Denis⁵³, J. Stahlman¹²¹, R. Stamen⁵⁸a, E. Stanecka³⁹, R.W. Stanek⁶, C. Stanescu¹³⁵a, M. Stanescu-Bellu⁴², M.M. Stanitzki⁴², S. Stapnes¹¹⁸, E.A. Starchenko¹²⁹, J. Stark⁵⁵, P. Staroba¹²⁶, P. Starovoitov⁴², R. Staszewski³⁹, P. Stavina¹⁴⁵a,*, G. Steele⁵³, P. Steinbach⁴⁴, P. Steinberg²⁵, I. Stekl¹²⁷, B. Stelzer¹⁴³, H.J. Stelzer⁸⁹, O. Stelzer-Chilton¹⁶⁰a, H. Stenzel⁵², S. Stern¹⁰⁰, G.A. Stewart³⁰, J.A. Stillings²¹, M.C. Stockton⁸⁶, M. Stoebe⁸⁶, K. Stoerig⁴⁸, G. Stoicea²⁶a, S. Stonjek¹⁰⁰, A.R. Stradling⁸, A. Straessner⁴⁴, J. Strandberg¹⁴⁸, S. Strandberg¹⁴⁷a,¹⁴⁷b, A. Strandlie¹¹⁸, E. Strauss¹⁴⁴, M. Strauss¹¹², P. Strizenec¹⁴⁵b, R. Ströhmer¹⁷⁵, D.M. Strom¹¹⁵, R. Stroynowski⁴⁰, S.A. Stucci¹⁷, B. Stugu¹⁴, I. Stumer²⁵,*, J. Stupak¹⁴⁹, P. Sturm¹⁷⁶, N.A. Styles⁴², D. Su¹⁴⁴, J. Su¹²⁴, HS. Subramania³, R. Subramaniam⁷⁸, A. Succurro¹², Y. Sugaya¹¹⁷, C. Suhr¹⁰⁷, M. Suk¹²⁷, V.V. Sulin⁹⁵, S. Sultansoy⁴c, T. Sumida⁶⁷, X. Sun⁵⁵, J.E. Sundermann⁴⁸, K. Suruliz¹⁴⁰, G. Susinno³⁷a,³⁷b, M.R. Sutton¹⁵⁰, Y. Suzuki⁶⁵, M. Svatos¹²⁶, S. Swedish¹⁶⁹, M. Swiatlowski¹⁴⁴, I. Sykora¹⁴⁵a, T. Sykora¹²⁸, D. Ta⁸⁹, K. Tackmann⁴², J. Taenzer¹⁵⁹, A. Taffard¹⁶⁴, R. Tafirout¹⁶⁰a, N. Taiblum¹⁵⁴, Y. Takahashi¹⁰², H. Takai²⁵, R. Takashima⁶⁸, H. Takeda⁶⁶, T. Takeshita¹⁴¹, Y. Takubo⁶⁵, M. Talby⁸⁴, A.A. Talyshev¹⁰⁸, f, J.Y.C. Tam¹⁷⁵, M.C. Tamsett⁷⁸, ah, K.G. Tan⁸⁷, J. Tanaka¹⁵⁶, R. Tanaka¹¹⁶, S. Tanaka¹³², S. Tanaka⁶⁵, A.J. Tanasijczuk¹⁴³, K. Tani⁶⁶, N. Tannoury⁸⁴, S. Tapprogge⁸², S. Tarem¹⁵³, F. Tarrade²⁹, G.F. Tartarelli⁹⁰a, P. Tas¹²⁸, M. Tasevsky¹²⁶, T. Tashiro⁶⁷, E. Tassi³⁷a,³⁷b, A. Tavares Delgado¹²⁵a,¹²⁵b, Y. Tayalati¹³⁶d, C. Taylor⁷⁷, F.E. Taylor⁹³, G.N. Taylor⁸⁷, W. Taylor¹⁶⁰b, F.A. Teischinger³⁰, M. Teixeira Dias Castanheira⁷⁵, P. Teixeira-Dias⁷⁶, K.K. Temming⁴⁸, H. Ten Kate³⁰, P.K. Teng¹⁵², S. Terada⁶⁵, K. Terashi¹⁵⁶, J. Terron⁸¹, S. Terzo¹⁰⁰, M. Testa⁴⁷, R.J. Teuscher¹⁵⁹, i, J. Therhaag²¹, T. ThevenEAux-Pelzer³⁴, S. Thoma⁴⁸, J.P. Thomas¹⁸, E.N. Thompson³⁵, P.D. Thompson¹⁸, P.D. Thompson¹⁵⁹, A.S. Thompson⁵³, L.A. Thomsen³⁶, E. Thomson¹²¹, M. Thomson²⁸, W.M. Thong⁸⁷, R.P. Thun⁸⁸,*, F. Tian³⁵, M.J. Tibbetts¹⁵, T. Tic¹²⁶, V.O. Tikhomirov⁹⁵, ai, Yu.A. Tikhonov¹⁰⁸, f, S. Timoshenko⁹⁷, E. Tiouchicheine⁸⁴, P. Tipton¹⁷⁷, S. Tisserant⁸⁴, T. Todorov⁵, S. Todorova-Nova¹²⁸, B. Toggerson¹⁶⁴, J. Tojo⁶⁹, S. Tokár¹⁴⁵a, K. Tokushuku⁶⁵, K. Tollefson⁸⁹, L. Tomlinson⁸³, M. Tomoto¹⁰², L. Tompkins³¹, K. Toms¹⁰⁴, N.D. Topilin⁶⁴, E. Torrence¹¹⁵, H. Torres¹⁴³, E. Torró Pastor¹⁶⁸, J. Toth⁸⁴, ac, F. Touchard⁸⁴, D.R. Tovey¹⁴⁰, H.L. Tran¹¹⁶, T. Trefzger¹⁷⁵, L. Tremblet³⁰, A. Tricoli³⁰, I.M. Trigger¹⁶⁰a, S. Trincaz-Duvoid⁷⁹, M.F. Tripiana⁷⁰, N. Triplett²⁵, W. Trischuk¹⁵⁹, B. Trocmé⁵⁵, C. Troncon⁹⁰a, M. Trottier-McDonald¹⁴³, M. Trovatelli¹³⁵a,¹³⁵b, P. True⁸⁹, M. Trzebinski³⁹, A. Trzupek³⁹, C. Tsarouchas³⁰, J.C.-L. Tseng¹¹⁹, P.V. Tsiareshka⁹¹, D. Tsionou¹³⁷, G. Tsipolitis¹⁰, N. Tsirintanis⁹, S. Tsiskaridze¹², V. Tsiskaridze⁴⁸, E.G. Tskhadadze⁵¹a, I.I. Tsukerman⁹⁶, V. Tsulaia¹⁵, J.-W. Tsung²¹, S. Tsuno⁶⁵, D. Tsybychev¹⁴⁹, A. Tua¹⁴⁰, A. Tudorache²⁶a, V. Tudorache²⁶a, J.M. Tuggle³¹, A.N. Tuna¹²¹, S.A. Tupputi²⁰a,²⁰b, S. Turchikhin⁹⁸, ag, D. Turecek¹²⁷, I. Turk Cakir⁴d, R. Turra⁹⁰a,⁹⁰b, P.M. Tuts³⁵, A. Tykhonov⁷⁴, M. Tylmad¹⁴⁷a,¹⁴⁷b, M. Tyndel¹³⁰, K. Uchida²¹, I. Ueda¹⁵⁶, R. Ueno²⁹, M. Ughetto⁸⁴, M. Ugland¹⁴, M. Uhlenbrock²¹, F. Ukegawa¹⁶¹, G. Unal³⁰, A. Undrus²⁵, G. Unel¹⁶⁴, F.C. Ungaro⁴⁸, Y. Unno⁶⁵, D. Urbaniec³⁵, P. Urquijo²¹, G. Usai⁸, A. Usanova⁶¹, L. Vacavant⁸⁴, V. Vacek¹²⁷, B. Vachon⁸⁶, N. Valencic¹⁰⁶, S. Valentinieta²⁰a,²⁰b, A. Valero¹⁶⁸, L. Valery³⁴, S. Valkar¹²⁸, E. Valladolid Gallego¹⁶⁸, S. Vallecorsa⁴⁹, J.A. Valls Ferrer¹⁶⁸, R. Van Berg¹²¹, P.C. Van Der Deijl¹⁰⁶, R. van der Geer¹⁰⁶, H. van der Graaf¹⁰⁶, R. Van Der Leeuw¹⁰⁶, D. van der Ster³⁰, N. van Eldik³⁰, P. van Gemmeren⁶, J. Van Nieuwkoop¹⁴³, I. van Vulpen¹⁰⁶, M.C. van Woerden³⁰, M. Vanadia¹⁰⁰, W. Vandelli³⁰, A. Vaniachine⁶, P. Vankov⁴², F. Vannucci⁷⁹, G. Vardanyan¹⁷⁸, R. Vari¹³³a, E.W. Varnes⁷, T. Varol⁸⁵, D. Varouchas¹⁵, A. Vartapetian⁸, K.E. Varvell¹⁵¹, V.I. Vassilakopoulos⁵⁶, F. Vazeille³⁴, T. Vazquez Schroeder⁵⁴, J. Veatch⁷, F. Veloso¹²⁵a,¹²⁵c, S. Veneziano¹³³a, A. Ventura⁷²a,⁷²b, D. Ventura⁸⁵, M. Venturi⁴⁸, N. Venturi¹⁵⁹, A. Venturini²³, V. Vercesi¹²⁰a, M. Verducci¹³⁹,
W. Verkerke¹⁰⁶, J.C. Vermeulen¹⁰⁶, A. Vest⁴⁴, M.C. Vetterli¹⁴³,⁴, O. Viazlo⁸⁰, I. Vichou¹⁶⁶, T. Vickey¹⁴⁶c, a,j,
O.E. Vickey Boeriu¹⁴⁶c, G.H.A. Viehhauser¹¹⁹, S. Viel¹⁶⁹, R. Vigne³⁰, M. Villa²⁰a,²⁰b, M. Villaplana Perez¹⁶⁸, E. Vilucchi⁴⁷,
M.G. Vincter²⁹, V.B. Vinogradov⁶⁴, J. Virzi¹⁵, O. Vitells¹⁷³, M. Viti⁴², I. Vivarelli¹⁵⁰, F. Vives Vaque³, S. Vlachos¹⁰,
D. Vladoiu⁹⁹, M. Vlasak¹²⁷, A. Vogel²¹, P. Vokac¹²⁷, G. Volpi⁴⁷, M. Volpi⁸⁷, G. Volpini⁹⁰a, H. von der Schmitt¹⁰⁰,
H. von Radziewski⁴⁸, E. von Toerne²¹, V. Vorobel¹²⁸, M. Vos¹⁶⁸, R. Voss³⁰, J.H. Vossebeld⁷³, N. Vranjes¹³⁷,
M. Vranjes Milosavljevic¹⁰⁶, V. Vrba¹²⁶, M. Vreeswijk¹⁰⁶, T. Vu Anh⁴⁸, R. Vuillermet³⁰, I. Vukotic³¹, Z. Vykydal¹²⁷,
W. Wagner¹⁷⁶, P. Wagner²¹, S. Wahrmund⁴⁴, J. Wakabayashi¹⁰², S. Walch⁸⁸, J. Walder⁷¹, R. Walker⁹⁹, W. Walkowiak¹⁴²,
R. Wall¹⁷⁷, P. Waller⁷³, B. Walsh¹⁷⁷, C. Wang⁴⁵, H. Wang¹⁵, H. Wang⁴⁰, J. Wang⁴², J. Wang³³a, K. Wang⁸⁶, R. Wang¹⁰⁴,
S.M. Wang¹⁵², T. Wang²¹, X. Wang¹⁷⁷, A. Warburton⁸⁶, C.P. Ward²⁸, D.R. Wardrope⁷⁷, M. Warsinsky⁴⁸, A. Washbrook⁴⁶,
C. Wasicki⁴², I. Watanabe⁶⁶, P.M. Watkins¹⁸, A.T. Watson¹⁸, I.J. Watson¹⁵¹, M.F. Watson¹⁸, G. Watts¹³⁹, S. Watts⁸³,
A.T. Waugh¹⁵¹, B.M. Waugh⁷⁷, S. Webb⁸³, M.S. Weber¹⁷, S.W. Weber¹⁷⁵, J.S. Webster³¹, A.R. Weidberg¹¹⁹, P. Weigell¹⁰⁰,
J. Weingarten⁵⁴, C. Weiser⁴⁸, H. Weits¹⁰⁶, P.S. Wells³⁰, T. Wenaus²⁵, D. Wendland¹⁶, Z. Weng¹⁵², u, T. Wengler³⁰, S. Wenig³⁰,
N. Wermes²¹, M. Werner⁴⁸, P. Werner³⁰, M. Wessels⁵⁸a, J. Wetter¹⁶², K. Whalen²⁹, A. White⁸, M.J. White¹, R. White³²b,
S. White¹²³a,¹²³b, D. Whiteson¹⁶⁴, D. Whittington⁶⁰, D. Wicke¹⁷⁶, F.J. Wickens¹³⁰, W. Wiedenmann¹⁷⁴, M. Wielers⁸⁰, c,
P. Wienemann²¹, C. Wiglesworth³⁶, L.A.M. Wiik-Fuchs²¹, P.A. Wijeratne⁷⁷, A. Wildauer¹⁰⁰, M.A. Wildt⁴², ak, I. Wilhelm¹²⁸,
H.G. Wilkens³⁰, J.Z. Will⁹⁹, H.H. Williams¹²¹, S. Williams²⁸, W. Willis³⁵,*, S. Willocq⁸⁵, J.A. Wilson¹⁸, A. Wilson⁸⁸,
I. Wingerter-Seez⁵, S. Winkelmann⁴⁸, F. Winklmeier¹¹⁵, M. Wittgen¹⁴⁴, T. Wittig⁴³, J. Wittkowski⁹⁹, S.J. Wollstadt⁸²,
M.W. Wolter³⁹, H. Wolters¹²⁵a,¹²⁵c, W.C. Wong⁴¹, B.K. Wosiek³⁹, J. Wotschack³⁰, M.J. Woudstra⁸³, K.W. Wozniak³⁹,
K. Wraight⁵³, M. Wright⁵³, S.L. Wu¹⁷⁴, X. Wu⁴⁹, Y. Wu⁸⁸, E. Wulf³⁵, T.R. Wyatt⁸³, B.M. Wynne⁴⁶, S. Xella³⁶, M. Xiao¹³⁷,
C. Xu³³b,al, D. Xu³³a, L. Xu³³b,am, B. Yabsley¹⁵¹, S. Yacoob¹⁴⁶b,an, M. Yamada⁶⁵, H. Yamaguchi¹⁵⁶, Y. Yamaguchi¹⁵⁶,
A. Yamamoto⁶⁵, K. Yamamoto⁶³, S. Yamamoto¹⁵⁶, T. Yamamura¹⁵⁶, T. Yamanaka¹⁵⁶, K. Yamauchi¹⁰², Y. Yamazaki⁶⁶,
Z. Yan²², H. Yang³³e, H. Yang¹⁷⁴, U.K. Yang⁸³, Y. Yang¹¹⁰, S. Yanush⁹², L. Yao³³a, Y. Yasu⁶⁵, E. Yatsenko⁴²,
K.H. Yau Wong²¹, J. Ye⁴⁰, S. Ye²⁵, A.L. Yen⁵⁷, E. Yildirim⁴², M. Yilmaz⁴b, R. Yoosoofmiya¹²⁴, K. Yorita¹⁷², R. Yoshida⁶,
K. Yoshihara¹⁵⁶, C. Young¹⁴⁴, C.J.S. Young³⁰, S. Youssef²², D.R. Yu¹⁵, J. Yu⁸, J. Yu¹¹³, L. Yuan⁶⁶, A. Yurkewicz¹⁰⁷,
B. Zabinski³⁹, R. Zaidan⁶², A.M. Zaitsev¹²⁹, z, A. Zaman¹⁴⁹, S. Zambito²³, L. Zanello¹³³a,¹³³b, D. Zanzi¹⁰⁰, A. Zaytsev²⁵,
C. Zeitnitz¹⁷⁶, M. Zeman¹²⁷, A. Zemla³⁸a, K. Zengel²³, O. Zenin¹²⁹, T. Ženiš¹⁴⁵a, D. Zerwas¹¹⁶, G. Zevi della Porta⁵⁷,
D. Zhang⁸⁸, H. Zhang⁸⁹, J. Zhang⁶, L. Zhang¹⁵², X. Zhang³³d, Z. Zhang¹¹⁶, Z. Zhao³³b, A. Zhemchugov⁶⁴, J. Zhong¹¹⁹,
B. Zhou⁸⁸, L. Zhou³⁵, N. Zhou¹⁶⁴, C.G. Zhu³³d, H. Zhu³³a, J. Zhu⁸⁸, Y. Zhu³³b, X. Zhuang³³a, A. Zibell⁹⁹, D. Zieminska⁶⁰,
N.I. Zimine⁶⁴, C. Zimmermann⁸², R. Zimmermann²¹, S. Zimmermann²¹, S. Zimmermann⁴⁸, Z. Zinonos⁵⁴, M. Ziolkowski¹⁴²,
R. Zitoun⁵, G. Zobernig¹⁷⁴, A. Zoccoli²⁰a,²⁰b, M. zur Nedden¹⁶, G. Zurzolo¹⁰³a,¹⁰³b, V. Zutshi¹⁰⁷, L. Zwalinski³⁰.

¹ School of Chemistry and Physics, University of Adelaide, Adelaide, Australia

² Physics Department, SUNY Albany, Albany NY, United States of America

³ Department of Physics, University of Alberta, Edmonton AB, Canada

⁴ (a) Department of Physics, Ankara University, Ankara; (b) Department of Physics, Gazi University, Ankara; (c) Division of Physics, TOBB University of Economics and Technology, Ankara; (d) Turkish Atomic Energy Authority, Ankara, Turkey

⁵ LAPP, CNRS/IN2P3 and Université de Savoie, Annecy-le-Vieux, France

⁶ High Energy Physics Division, Argonne National Laboratory, Argonne IL, United States of America

⁷ Department of Physics, University of Arizona, Tucson AZ, United States of America

⁸ Department of Physics, The University of Texas at Arlington, Arlington TX, United States of America

⁹ Physics Department, University of Athens, Athens, Greece

¹⁰ Physics Department, National Technical University of Athens, Zografou, Greece

¹¹ Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan

¹² Institut de Física d'Altes Energies and Departament de Física de la Universitat Autònoma de Barcelona, Barcelona, Spain

¹³ (a) Institute of Physics, University of Belgrade, Belgrade; (b) Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia

¹⁴ Department for Physics and Technology, University of Bergen, Bergen, Norway

¹⁵ Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA, United States of America

¹⁶ Department of Physics, Humboldt University, Berlin, Germany

¹⁷ Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland

¹⁸ School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom

¹⁹ (a) Department of Physics, Bogazici University, Istanbul; (b) Department of Physics, Dogus University, Istanbul; (c) Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey

²⁰ (a) INFN Sezione di Bologna; (b) Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy

²¹ Physikalisches Institut, University of Bonn, Bonn, Germany

²² Department of Physics, Boston University, Boston MA, United States of America

²³ Department of Physics, Brandeis University, Waltham MA, United States of America