<figure><img src="image_1.png" /></figure>

JINST 19 (2024) P08018
DOI: 10.1088/1748-0221/19/08/P08018

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CERN-EP-2024-159
16th December 2024

# Accuracy versus precision in boosted top tagging with the ATLAS detector

The ATLAS Collaboration

The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as *top tagging*, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at $\sqrt{s} = 13$ TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.
# Contents

1 Introduction 2

2 Monte carlo simulation samples 4

3 Jet reconstruction, selection, and pre-processing 4

4 Top quark taggers 7

4.1 High-level-quantity baseline 8

4.2 Densely connected neural network 8

4.3 Energy flow network 8

4.4 Particle flow network 8

4.5 ResNet 50 9

4.6 ParticleNet 10

5 Tagger performance 10

6 Systematic uncertainties 12

6.1 Experimental uncertainties 13

6.2 Theoretical uncertainties 15

6.3 Validation of uncertainties 17

6.4 Total uncertainties 18

7 Conclusion 19

# 1 Introduction

Collisions at the Large Hadron Collider (LHC) [1] can produce short-lived heavy Standard Model (SM) particles such as the W and Z bosons, the Higgs boson, and the top quark. These particles often decay to quarks, which later hadronize and are detected as collimated sprays of particles called jets. In the case that the originating particle's momentum transverse to the beam axis ($p_T$) is large compared to its mass (i.e. it is *boosted*), the decay products are highly collimated in the laboratory frame and are reconstructed as a single jet. The task of distinguishing jets resulting from the decays of heavy particles from the much more numerous jets resulting from light quarks and gluons is known as *boosted jet tagging*. There is a rich history of boosted jet tagging methods at the LHC [2–4]. Recently, the adoption of machine-learning-based algorithms for boosted jet tagging has provided large performance improvements [5, 6]. These algorithms, often called *jet taggers*, have been used in two ways. The first is to make use of *high-level quantities*, which are observables designed to produce different values when the jet is due to the decay of a heavy particle versus due to light quarks and gluons. These are always functions of the kinematic properties of the constituent particles (called *jet constituents*) within a jet, which are experimentally reconstructed from inner detector tracks, calorimeter energy deposits, or some non-trivial combination of the two. A set of high-level quantities is calculated for a given jet, and then used as input to a neural network. The second approach is to directly use the kinematic properties of the constituent particles as input to a neural network (see Ref. [7]). The information contained in a set of high-level quantities is a subset of the information contained in the kinematic properties of the constituent particles, so the second approach has the potential
for higher performance. However, it requires the use of more complex neural networks as there are a varying and possibly large number of constituent particles within a jet.

The performance of the constituent-based approach has been demonstrated [8–13] in the context of highly detailed simulation of the ATLAS [14–16] and CMS [17] detectors, and used to enhance the sensitivity of several physics analyses at the LHC [18–20]. However, open questions remain about the relative size of the systematic uncertainties associated with various constituent-based jet tagging algorithms. The simulated datasets on which ATLAS and CMS train and evaluate ML-based jet tagging algorithms are a useful, but limited approximation of the experimental data gathered by the experiments. There are important differences that can produce different jet tagging efficiencies between simulated and experimental data. These differences in efficiency are accounted for through a measurement of *scale factors*, defined as the ratio of the efficiency in simulated data to the efficiency in experimental data, and its associated uncertainties. These scale factors are required for interpreting the results of any physics analysis in the context of the SM or any beyond the Standard Model (BSM) physics model, so it is crucial to consider the size of scale-factor uncertainties when comparing jet tagging algorithms. Scale-factor measurements are a significant bottleneck in the development of new jet tagging algorithms, as they require access to experimental data and must be independently repeated for each algorithm. As a result they have only been carried out for a few constituent-based taggers [21, 22], and most studies on constituent-based jet tagging have not considered the uncertainties associated with the application of the tagger to experimental data.

This paper presents the performance of several constituent-based taggers, compared to the performance of a baseline high-level-quantity-based tagger, on a benchmark jet tagging task that involves identifying jets originating from the decay of a boosted top quark. This task, known as *top tagging*, is a crucial component of many measurements of SM processes that involve the production of top quarks [23–30] and searches for BSM physics that contain top quarks in their final state [31–36]. It is used as a benchmark jet tagging task since the three-body decay of the top quark produces jets with a distinctive three-subjet radiation pattern, which can be used to distinguish them from the background of jets originating from light quarks and gluons.

Following the performance comparison, the systematic uncertainties associated with the application of each tagger to experimental data are assessed by applying systematic variations directly to the kinematics of the jet constituents used as inputs to the neural networks. The resulting variations in tagger efficiency are used to estimate the size of the systematic uncertainties associated with each tagger. This approach does not require experimental data, and can be easily repeated for an arbitrary tagger by measuring performance on the testing set with the systematic variations applied, allowing the first comparison of the systematic uncertainties produced by various jet tagging algorithms. However, as will be discussed, there are many simplifying assumptions made in this approach. The resulting systematic uncertainties lack the precision needed for use in a physics analysis, but they do provide a useful estimate of the size of the systematic uncertainties associated with each tagger.

The rest of this paper is organized as follows. The samples of simulated collision events used in this study are described in Section 2, and the subsequent jet reconstruction and event selections are described in Section 3. The various jet taggers considered are described in Section 4, and the performance of the taggers is compared in Section 5. Section 6 describes the procedure used to estimate the systematic uncertainties associated with each tagger, and presents the results. Finally, conclusions are drawn in Section 7.
## 2 Monte carlo simulation samples

Simulated proton-proton collisions at $\sqrt{s} = 13$ TeV using Monte Carlo (MC) methods are used throughout this study. The nominal samples are generated at leading-order (LO) with PYTHIA8 [37] using the NNPDF2.3LO [38] set of parton distribution functions (PDFs) and the A14 [39] set of tuned parameters. The effects of pile-up are simulated by overlaying inelastic interactions on top of the underlying hard scattering process. All simulated samples are passed through a GEANT4 [40]-based simulation of the ATLAS detector. For more details on the ATLAS detector, see Ref. [14]. Boosted top quarks are selected from simulated events containing the decay of a heavy BSM Z boson ($Z' \to t\bar{t}$), with $m_{Z'} = 2$ TeV [41]. The cross section of this process is reweighted to produce an approximately flat jet $p_T$ distribution to efficiently populate the kinematic region [0.35, 5] TeV. Light-quark and gluon jets are selected from simulated events containing the production of high $p_T$ light quarks and gluons through quantum chromodynamic (QCD) processes.

Additional samples of simulated collisions are utilized to assess the uncertainties from the modeling of the parton shower and hadronization processes as described in Section 6.2. These uncertainties for boosted top-quark jets are assessed using simulated collision events containing the production of boosted top quarks through SM processes. Two samples are generated with matrix element calculations performed by the POWHEG BOX v2 [42–45] generator at NLO with the NNPDF3.0LO [46] PDF set and the $h_{\text{damp}}$ parameter<fnref n="1" /> set to 1.5 times the mass of the top quark [47]. For both samples the decays of bottom and charm hadrons were performed by EVTGEN 1.6.0 [48]. In one sample the parton shower and hadronization is then modeled with PYTHIA8 [37], and in the other it is modeled with HERWIG7 [49, 50].

The uncertainties from the modeling of the parton shower and hadronization processes in the production of light-quark and gluon jets are assessed using four samples of simulated collisions. All of these samples are generated with matrix element calculations at LO using the NNPDF3.0LO [46] PDF set. The parton shower modeling uncertainty is estimated by comparing performance between samples generated with HERWIG7, one produced with the default angular ordered parton shower model, and the other produced with an alternative dipole parton shower model. The default cluster-based hadronization model is used for both samples [51]. The hadronization model uncertainty is estimated by comparing performance between samples generated with SHERPA2.2 [52], one produced with the default cluster-based hadronization model [51], and the other produced with the SHERPA interface to the Lund string fragmentation model of PYTHIA 6 [53] and its decay tables. The default $p_T$ ordered parton shower model is used for both SHERPA generated samples.

## 3 Jet reconstruction, selection, and pre-processing

Unified Flow Objects (UFOs) [54] are jet clustering input objects that make use of different ATLAS sub-systems in different kinematic ranges<fnref n="2" />. At low $p_T$, the inner tracking detector provides exceptional

<footnote n="1">¹ The $h_{\text{damp}}$ parameter is a resummation damping factor and one of the parameters that controls the matching of POWHEG matrix elements to the parton shower and thus effectively regulates the high-$p_T$ radiation against which the $t\bar{t}$ system recoils.</footnote>

<footnote n="2">² ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Polar coordinates $(r, \phi)$ are used in the transverse plane, $\phi$ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle $\theta$ as $\eta = -\ln \tan(\theta/2)$ and is equal to the rapidity $y = \frac{1}{2} \ln \left( \frac{E+p_z c}{E-p_z c} \right)$ in the relativistic limit. Angular distance is measured in units of $\Delta R \equiv \sqrt{(\Delta y)^2 + (\Delta \phi)^2}$.</footnote>
<table><caption>Table 1: A summary of the requirements applied on all of the jets in the simulation samples to produce the training and testing sets. The additional top-quark jet requirements constitute the truth labeling strategy, and are only applied to jets taken from the $Z' \to t\bar{t}$ and SM $t\bar{t}$ samples of simulated events.</caption><thead><tr><th>Jet requirements</th><th>Top-quark jet requirements</th></tr></thead><tbody><tr><td>ΔR(jet, truth jet) &lt; 0.75</td><td>ΔR(truth jet, top parton) &lt; 0.75</td></tr><tr><td>Jet |η<sub>true</sub>| &lt; 2.0</td><td>Ungroomed truth jet mass &gt; 140 GeV</td></tr><tr><td>Jet p<sub>T,truth</sub> &gt; 350 GeV</td><td>Number ghost-associated b-hadrons ≥ 1</td></tr><tr><td>Number of constituents ≥ 3</td><td>Truth jet &radic;<span style="text-decoration: overline;">d<sub>23</sub></span> &gt; exp(3.3 - 6.98 &times; 10<sup>-4</sup> &times; truth jet p<sub>T</sub> [GeV])</td></tr><tr><td>Jet mass &gt; 40 GeV</td><td></td></tr></tbody></table>

spatial and momentum resolution, so low-$p_T$ charged constituents are reconstructed from tracks using charged Particle Flow objects (PFO) [55]. At high transverse momentum, the tracking detector's momentum resolution degrades but it retains high spatial resolution, and so high-$p_T$ charged constituents are reconstructed using energy measurements from the calorimeters and spatial measurements from the tracking detector using Track Calo Clusters (TCC) [56]. Electrically neutral constituents are reconstructed as neutral PFOs using measurements from the electromagnetic and hadronic calorimeters. This scheme provides accurate reconstruction of constituent particles across a wide kinematic range.

Both the leading and sub-leading jets in $p_T$ in each event are used in these studies. Jets are clustered using the anti-$k_t$ algorithm [57] with a radius parameter of $R = 1.0$, as implemented in the FASTJET package [58]. The Constituent Subtraction [59, 60] and Soft-Killer [61] (CSSK) algorithms are applied to the neutral UFOs to mitigate contamination from any radiation that comes from pile-up collisions rather than the quarks or gluons that initiated the jet. Further, the Soft-Drop algorithm [62] (SD) is applied with parameters $\beta = 1.0$ and $z_{cut} = 0.1$ to remove soft and wide-angle radiation resulting from pile-up or the underlying event.

Some requirements in the jet selection are placed on the *truth jet*, which is a jet formed from stable particles<fnref n="3" /> in the simulated event before the detector response is modeled. All jets are required to have a matched truth jet with $\Delta R(\text{jet}, \text{truth jet}) < 0.75$. The jet itself is required to have a mass of at least 40 GeV, and at least three constituents. This last requirement is included to ensure the preprocessing scheme described below is well defined.

Jets in the signal sample must satisfy additional requirements which ensure the jet is due to the decay of a top quark, and the decay products of the top quark are fully contained within the jet. They require that the truth jet be spatially aligned with the observed jet, have a mass greater than 140 GeV, have a ghost-associated bottom hadron [63], and satisfy a $p_T$-dependent requirement on the $k_t$ splitting scale $\sqrt{d_{23}}$ [64]. For more details on these requirements, see Ref. [65]. All of the requirements are summarized in Table 1.

The training and performance of ML models can often be improved by applying *pre-processing* to the data to eliminate irrelevant features and capitalize on well-known symmetries. One set of irrelevant features is the unphysical bumps in the $p_T$ spectrum of the background light-quark and gluon jets, which result from

<footnote n="3">³ The stable particles are required to have a lifetime greater than 10 ps and muons and neutrinos are excluded as they only leave minimal energy within the calorimeter.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: The number of constituents in a sample of jets obtained from the $t\bar{t}$ enriched region described in Ref. [66]. The number of constituents is shown for both the POWHEG BOX v2 +PYTHIA 8 MC sample and experimental data.</figcaption></figure>

the simulation of QCD multijet events in intervals of jet $p_T$ to allow for efficient generation of high-$p_T$ events. A physical $p_T$ spectrum is not required for tagger training. It is only important that the tagger is trained to classify jets from across the desired kinematic range. To achieve this, the background jet $p_T$ spectrum is re-weighted to match the approximately flat signal jet $p_T$ spectrum. Additionally, to a good approximation the probability of a jet to be due to a top quark or to a light quark or gluon is invariant under translations of the jet in the $\eta-\phi$ plane, and rotations of the jet about the jet axis. In this study, a pre-processing of the angular coordinates modeled after that used in Ref. [7] is applied to the $\eta$ and $\phi$ coordinates of all jet constituents to remove this approximate translational and rotational symmetry. First the coordinates of the jet constituents are shifted such that the highest $p_T$ constituent is located at the origin of the $\eta-\phi$ plane. Then the jet is rotated such that the second highest $p_T$ constituent is located on the negative $\phi$ axis. Finally if the third highest $p_T$ constituent is located in the negative $\eta$ half-plane, the jet is reflected about the $\phi$ axis to place it in the positive $\eta$ half-plane. It is also advantageous to pre-process the constituent $p_T$ and energy values to place them on an $O(1)$ scale. This is done by taking the logarithm of these values. Three other constituent-level quantities are calculated and used as inputs to the constituent-based taggers. The first is the angular distance of the constituent from the jet axis, calculated as

$$R = \sqrt{\eta^2 + \phi^2}, \qquad (1)$$

where $\eta$ and $\phi$ are taken after the pre-processing. The second (third) is calculated by dividing the constituent $p_T$ (energy) by the total $p_T$ (energy) in the jet, and then taking the logarithm of this fraction. All together, seven constituent-level quantities are used as inputs to the constituent-based taggers: the preprocessed $\eta$ and $\phi$ coordinates, the logarithm of the $p_T$ and energy of the constituent, the logarithm of the fraction of the constituent $p_T$ and energy to the total $p_T$ and energy of the jet, and the angular distance of the constituent from the jet axis.

The number of constituents for a selection of jets from the $t\bar{t}$ enriched region described in Ref. [66] is shown in Figure 1 comparing the simulation to a data sample of proton-proton collisions at $\sqrt{s} = 13$ TeV recorded
with the ATLAS detector during Run 2 of the LHC and corresponding to an integrated luminosity of 140 fb⁻¹ [67, 68]. Despite the complexity of the ATLAS detector’s calorimetry the number of constituents in data is well modeled by simulation. The jets in this histogram are representative of those entering the signal regions of most analyses targeting boosted top quarks. Each constituent is characterized by a four-vector, so there is an average of around 200 dimensions used as input to the constituent-based taggers. To reduce the memory and compute requirements for training the constituent-based taggers, the number of constituents used as input is limited to 80. Most boosted top jets have fewer than 80 constituents, but those with more are truncated. The effects of this truncation are mitigated by first sorting the constituents by decreasing $p_T$, ensuring that only the softest constituents are removed.

## 4 Top quark taggers

The top quark taggers considered in this study are described below. The constituent-based taggers use the four-vectors of the UFOs used to reconstruct the jet as inputs, while the high-level-quantity-based tagger uses the 15 high-level quantities listed in Table 3 as inputs. Information useful for identifying heavy-flavor decays, such as the presence of displaced-vertices, is not used as input to any of the taggers. Instead the taggers are trained to identify the “3-pronged” substructure of boosted top-quark jets. Inclusion of displaced-vertex information in the inputs would likely improve the taggers’ performance, but maximizing performance is not the goal of this study and so this is left for future work. The number of trainable parameters and inference time, defined as the amount of time required to run inference for a batch of 256 jets on an NVIDIA Tesla V100 GPU, are shown for each tagger considered in this study in Table 2. Many other proposed jet tagging algorithms have shown promising performance in the context of simplified jet reconstruction and detector simulation [69–74], but these are not considered in this study. The training, validation, and testing sets consisted of about 9 million, 1 million, and 3.8 million jets respectively, each with equal parts signal and background jets. The taggers were trained and the hyper-parameters were chosen as described in Ref. [8].

<table><caption>Table 2: The number of trainable parameters and inference time for each tagger considered in this study. The inference time is measured using a NVIDIA Tesla V100 GPU.</caption><thead><tr><td>Tagger</td><td>Number of parameters</td><td>Inference time</td></tr></thead><tbody><tr><td>hIDNN</td><td>133,381</td><td>3 ms</td></tr><tr><td>DNN</td><td>876,641</td><td>3 ms</td></tr><tr><td>EFN</td><td>959,251</td><td>4 ms</td></tr><tr><td>PFN</td><td>754,501</td><td>3 ms</td></tr><tr><td>ResNet 50</td><td>1,499,585</td><td>20 ms</td></tr><tr><td>ParticleNet</td><td>764,887</td><td>143 ms</td></tr></tbody></table>
<table><caption>Table 3: A listing of the 15 quantities used to train the baseline high-level-quantity-based tagger.</caption><thead><tr><td>Quantity</td><td>Symbols</td><td>References</td></tr></thead><tbody><tr><td>N-subjettiness</td><td>τ<sub>1</sub>, τ<sub>2</sub>, τ<sub>3</sub>, τ<sub>4</sub></td><td>[76] [77]</td></tr><tr><td>k<sub>t</sub> Splitting Scales</td><td>√d<sub>12</sub>, √d<sub>23</sub></td><td>[78]</td></tr><tr><td>Generalized Energy Correlation Functions</td><td>ECF<sub>1</sub>, ECF<sub>2</sub>, ECF<sub>3</sub>, C<sub>2</sub>, D<sub>2</sub>, L<sub>2</sub>, L<sub>3</sub></td><td>[79] [80] [81]</td></tr><tr><td>Minimum Pair-wise Invariant Mass</td><td>Q<sub>w</sub></td><td>[78]</td></tr><tr><td>Thrust Major</td><td>T<sub>m</sub></td><td>[82]</td></tr></tbody></table>

## 4.1 High-level-quantity baseline

The high-level quantity densely connected neural network tagger (hIDNN) is trained on the 15 high-level quantities listed in Table 3. This tagger is modeled after Ref. [65], and serves as the baseline against which the constituent-based taggers are compared. The network is a standard multi-layer perceptron [75] (MLP).

## 4.2 Densely connected neural network

The simplest constituent-based tagger is the densely connected neural network (DNN), which is a multi-layer perceptron operating directly on a vector of the constituent information [83]. When there are less than 80 constituents in a jet, this vector contains zero padding which is used as input to the DNN. The DNN has no mechanism for masking these zero padded inputs, meaning it has no inductive bias, or specialization to the top tagging task, that naturally accounts for the variable number of jet constituents. The DNN uses all 7 of the pre-processed constituent-level quantities described in Section 3 as inputs.

## 4.3 Energy flow network

The Energy Flow Network [84] (EFN) is a model specifically engineered for jet tagging. It uses the DeepSets structure [85], which ensures permutation invariance across the jet constituents used as input and naturally handles the variable number of jet constituents. The EFN also uses a $p_T$ weighting mechanism to ensure that lower $p_T$ constituents have lower impact on the output of the network. This $p_T$ weighting can be interpreted as enforcing infrared and collinear (IRC) safety [86]. The output for any of the other networks considered in this study is not IRC safe. The EFN uses the logarithm of the constituent $p_T$ as input to the $p_T$ weighting mechanism, and does not use the constituent energy as input.

## 4.4 Particle flow network

The particle flow network [84] (PFN) has a very similar structure to the EFN that naturally deals with the variable number of jet constituents and enforces permutation invariance. However it does not use the $p_T$ weighting mechanism, and uses the constituent energy and all other constituent-level quantities described in Section 3 as inputs.
<figure><img src="image_4.png" /><figcaption>Figure 2: (a) An example background jet image. (b) An example signal jet image. (c) The ratio of the average signal and background jet images.</figcaption></figure>

## 4.5 ResNet 50

ResNet 50 [87] is a large-scale convolutional neural network (CNN) designed for image classification tasks. CNNs operate on two dimensional arrays whose values give pixel intensity. Noting the similarity between energy deposits in the ATLAS calorimeter and standard two-dimensional images [88–92], the jets are converted into “jet images” by binning each constituent’s $\eta$ and $\phi$ coordinates into 64 bins, equally spaced in the range of [-2, 2]. The image is then a 64x64 square array where the pixel values are the sum of the raw $p_T$ of the constituents within the pixel, normalized such that the sum of the pixel intensity over the image is one. Pixel intensities are then rescaled by $\log(1 + 100 \times p_T)$ to make lower $p_T$ patterns in the jet substructure visible.

Typically the images produced for single jets are very sparse, with most pixels containing no constituents. Example signal and background jet images are shown in Figure 2, along with an image which shows the ratio of the average signal and background jets. The differences between the average radiation patterns for signal and background jets can be seen in deviations of the ratio from one. An excess of transverse
momentum concentrated just below the origin on the negative φ-axis results from the second prong of boosted top jets which is preprocessed to align with this axis. The diffuse excess of transverse momentum distributed around the center is due to the third prong of the boosted top jets. Finally the deficit of transverse momentum in the center of the image is due to the more collimated nature of light-quark and gluon jets.

## 4.6 ParticleNet

ParticleNet [93] is a graph neural network (GNN) which represents each jet as a graph composed of nodes and edges. Each constituent in a jet is associated with a node, where all of the input quantities are taken as features of the node. In this study, these are the 7 constituent-level quantities defined in Section 3. Each node is connected by an edge to its *k* nearest neighbors in the η-φ plane, where *k* is a network hyper-parameter. ParticleNet applies a specialized form of the EdgeConv operation [94] to this graph. This operation is similar to the two dimensional convolution used in CNNs, but defined on graphs instead of images.

Like the EFN and PFN, ParticleNet naturally handles the variable lengths of jets and enforces permutation invariance. However the EdgeConv operation acts on the feature vectors of pairs of constituents that are spatially close to each other, rather than each constituent separately. These paired inputs allow ParticleNet to exploit the local relations between constituents.

## 5 Tagger performance

Performance metrics for the six taggers evaluated on the testing set are shown in Table 4. The metrics are the area under the receiving-operator-characteristic curve (AUC) [95], the fraction of correct predictions (ACC), and the inverse of the background efficiency (background rejection) at two different working points that fix the signal efficiencies to 50% and 80%. The uncertainties in the performance metrics are the quadrature sum of the uncertainty from the finite size of the testing set (statistical uncertainty), and the uncertainty from the random initialization of the weights and the stochastic nature of network training (training uncertainty). The statistical uncertainty is calculated as the standard error of the performance metrics over 100 bootstrap replicas of the testing set [96]. The training uncertainty is calculated by training each network 10 times on the same training set with different weight initializations and batching of training data, and then evaluating the standard error of the performance metrics over the 10 training runs. The differences in the performance metrics between the taggers are larger than the uncertainties in the performance metrics. In all metrics, ParticleNet achieves the best performance, followed by the PFN, the DNN, and the high-level-quantity-based tagger. The EFN and ResNet50 fail to outperform the hIDNN, despite access to the constituent information. The structure of the EFN ensures it is insensitive to low $p_T$ jet constituents, but at the expense of not fully exploiting the available information. The weaker relative performance of ResNet50 is more suprising, given its strong performance on datasets generated with a parametric detector simulation [7]. The parametric detector simulation assumes uniform calorimeter cell granularity. Realistic calorimeters like those used in ATLAS have a non-uniform granularity, which is captured in the high quality simulation used to produce ATLAS simulated data. Building jet images by applying a uniform pixelization to jet constituents in the context of a non-uniform calorimeter granularity could produce non-physical distortions that are not present in simulated events generated with a parametric detector simulation. This could explain the weaker performance of ResNet50 on ATLAS simulated data.
<table><caption>Table 4: The performance of each top quark tagger is measured with several metrics evaluated on the testing set. AUC is the area under the receiving-operator-characteristic curve, ACC is the accuracy, and $\epsilon_{bkg}^{-1}$ is the inverse background efficiency (or background rejection) evaluated at working points which yield a given signal efficiency ($\epsilon_{sig}$) across the entire testing set. For all metrics, a higher value means better performance, and the table is sorted by increasing AUC. The uncertainty reported on the metrics is the quadrature sum of the uncertainty from the finite statistics of the testing set and the error from the random initialization of network weights and the stochastic nature of network training.</caption><thead><tr><th>Tagger</th><th>AUC</th><th>ACC</th><th>ε<sub>bkg</sub><sup>-1</sup> @ ε<sub>sig</sub> = 0.5</th><th>ε<sub>bkg</sub><sup>-1</sup> @ ε<sub>sig</sub> = 0.8</th></tr></thead><tbody><tr><td>ResNet 50</td><td>0.872 ± 0.006</td><td>0.787 ± 0.006</td><td>18.4 ± 1.1</td><td>4.63 ± 0.2</td></tr><tr><td>EFN</td><td>0.894 ± 0.001</td><td>0.810 ± 0.001</td><td>23.8 ± 0.5</td><td>5.74 ± 0.07</td></tr><tr><td>hIDNN</td><td>0.9374 ± 0.0001</td><td>0.8628 ± 0.0002</td><td>47.2 ± 0.4</td><td>10.36 ± 0.03</td></tr><tr><td>DNN</td><td>0.9447 ± 0.0004</td><td>0.8715 ± 0.0008</td><td>73.0 ± 1.3</td><td>12.5 ± 0.1</td></tr><tr><td>PFN</td><td>0.9502 ± 0.0004</td><td>0.878 ± 0.001</td><td>92.7 ± 1.8</td><td>14.6 ± 0.2</td></tr><tr><td>ParticleNet</td><td>0.9614 ± 0.0005</td><td>0.895 ± 0.001</td><td>155.8 ± 3.8</td><td>20.6 ± 0.4</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>Figure 3: Background rejection, or inverse background efficiency ($\epsilon_{bkg}^{-1}$), of the hIDNN, PFN, and ParticleNet top quark taggers as a function of the jet $p_T$ at the (a) 50% signal efficiency and (b) 80% signal efficiency working points. Shaded error bands are the quadrature sum of the error from the finite size of the testing set and the error from the random initialization of network weights and the stochastic nature of network training.</figcaption></figure>

The background rejection at the 50% and 80% signal efficiency working points is shown as a function of the jet $p_T$ in Figure 3 for the hIDNN, PFN, and ParticleNet taggers. Unlike the high-level-quantity-based tagger, the constituent-based taggers' performances are best in the mid-$p_T$ range around 1-2 TeV. The high $p_T$ decrease in background rejection is expected, since the higher collimation of jets at high-$p_T$ makes it harder for the tracking detector and calorimeters to resolve the 3-pronged substructure of boosted top jets. The low-$p_T$ increase in background rejection appears for all constituent-based taggers.
# 6 Systematic uncertainties

Samples of simulated events generated with Monte Carlo methods are a useful model of the experimental data collected by the ATLAS detector for a given physics process, but as important differences between simulated and experimental data can exist, the efficiency of a tagger in experimental data cannot be assumed to be the same as the efficiency in simulated data. To establish the sensitivity of a physics analysis to a SM or BSM process, this difference in efficiency must be known. The standard method to establish this difference is to measure a *scale-factor* and the accompanying uncertainties. Scale-factor measurements are made using samples of signal and background jets collected from experimental data. These samples are not obtainable for difficult-to-isolate SM or any BSM signature in jet substructure, meaning scale-factors cannot be derived in many applications of jet tagging. Scale-factor measurements are also time intensive, making it difficult to measure scale factors for two taggers and then compare the size of the scale factors and their uncertainties.

An alternative approach to constraining the difference in tagger efficiency between simulated and experimental data is to apply a set of *systematic variations* directly to simulated data, which alter the tagger inputs within their uncertainties. These variations produce many systematic varied datasets, and a tagger is then trained on the nominal and evaluated on the nominal and systematic varied datasets. The differences in tagger efficiency between the nominal and systematic varied datasets can then be used to set an uncertainty in the tagger’s efficiency in simulated data. Provided the systematic variations account for all possible differences between simulated and experimental data at the level of the tagger inputs, the uncertainty provides an estimate of the expected size of the difference in tagger efficiency.

Differences can be classified into two types: possible mis-modeling of the measurements of the kinematic properties of jet constituents by the ATLAS detector (which produce *experimental uncertainties*), and possible mis-modeling of the underlying physical processes that produce the jets (which produce *theoretical uncertainties*). In this paper, the theoretical uncertainties are assessed by evaluating the tagger efficiency over samples of jets generated with alternative models of the underlying physics processes that produce light quarks, gluons, and top quarks. This is similar to the procedure used in evaluating theoretical uncertainties in scale-factor measurements [66]. The experimental uncertainties are assessed by varying the kinematic properties of the jet constituents within the uncertainties of the ATLAS detector’s measurements. These uncertainties are established through auxiliary measurements such as Refs. [97, 98]. This is termed the “bottom-up” approach to experimental uncertainties, which has been used to produce several measurements of jet substructure observables [27, 99, 100]. In practice it is difficult to construct a set of variations that cover all possible experimental uncertainties without over-covering some uncertainties and disregarding others. For this reason the approach of applying systematic variations directly to simulated data is generally less precise than scale-factor measurements, but it offers several advantages. Once the systematically varied datasets are constructed, it is very easy to set uncertainties on the efficiency of an arbitrary tagger, as it only requires running inference over additional datasets. Further, the approach requires no samples of signal and background jets taken from experimental data. This is particularly useful for analyses which use a jet tagger to identify BSM physics signatures.

In this section, the standard approach to theoretical uncertainties is combined with the bottom-up approach to set uncertainties on the background rejection of the taggers. In a realistic physics analysis, scale factors and their uncertainties would be derived for the signal efficiency. This study derives uncertainties on the background rejection to have a single performance metric and its associated uncertainty which can be compared between taggers. Several assumptions are made to simplify the experimental uncertainties assigned in this study which will be mentioned explicitly in the following sections. As a result the
experimental uncertainties are only intended to be an estimate of the relative size of the experimental uncertainties associated with each tagger, and are not meant to be used in a physics analysis.

## 6.1 Experimental uncertainties

The UFOs used as inputs to the constituent-based taggers can be classified into three types: charged, neutral, and merged [54]. Charged UFOs are simply charged PFOs where an inner detector track [101] is matched to a topological cluster [102] and used to subtract the expected calorimeter energy from the cluster in a process called *cell subtraction*. The properties of charged UFOs are determined by the underlying inner detector track, so a set of systematic variations covering track uncertainties are applied to these objects [103, 104].

Neutral UFOs are the topological clusters which remain after both the cell subtraction procedure in the Particle Flow algorithm, and the splitting procedure in the TCC algorithm. Their properties are primarily determined by the underlying topological cluster, but the inner detector tracks can also affect them through the cell subtraction and TCC splitting procedures. In this paper, the simplifying assumption is made that neutral UFOs are only affected by the underlying topological cluster, and so a set of systematic variations covering topological cluster uncertainties are applied to these objects [27, 98, 100].

Merged UFOs start as charged PFOs where the cell subtraction procedure is disabled due to a large amount of calorimeter activity in the vicinity of the track. The TCC algorithm is then run with these charged PFOs as input. The UFOs this algorithm outputs have their $p_T$ determined by the properties of the underlying topological cluster, and their $\eta$ and $\phi$ determined by the properties of the underlying inner detector track. Since merged UFOs have their properties set by a combination of the properties of the underlying inner detector tracks and topological clusters, a selection of the track and topological cluster systematic variations are applied to these objects. This paper makes the simplifying assumption that the $\eta$ and $\phi$ coordinates of merged UFOs are determined solely by the properties of the underlying inner detector track, and the $p_T$ and energy of merged UFOs are determined solely by the properties of the underlying topological cluster. This is not strictly true because of the complex interplay between the inner detector tracks and topological clusters in the particle flow and TCC algorithms.

The track uncertainties are covered by three systematic variations: the track fake rate, the tracking efficiency, and the track bias. The track fake rate systematic variation accounts for uncertainty in the rate of tracks produced by chance alignment of signals in the tracking detector. The size of this uncertainty is estimated by studying the non-linear component of the evolution of the number of inner detector tracks with increasing pile-up in experimental data collected through random triggers [101]. The systematic variation selectively drops charged or merged UFOs, which has the effect of decreasing the track fake rate by its uncertainty. An increase in the track fake rate is then covered by symmetrizing the uncertainty in the tagger background rejection.

The tracking efficiency systematic variation accounts for uncertainty in the efficiency of finding true tracks. It contains components that account for limited knowledge of the inner detector material [101], and the merging of tracks within dense tracking environments such as the cores of jets [105]. Like the track fake rate systematic variation, the track efficiency systematic variation drops charged or merged UFOs, but with different probabilities. This has the effect of decreasing the tracking efficiency, and an increase in the efficiency is covered by symmetrizing the uncertainty in the tagger background rejection.
<figure><img src="image_7.png" /></figure>

Figure 4: The relative uncertainty in the background rejection due to the track systematic uncertainties as a function of the jet $p_T$ for (a) the EFN and (b) ParticleNet taggers. The total uncertainty is the quadrature sum of all track uncertainties. The fluctuations in the uncertainty result from the finite statistics of the nominal and systematic varied datasets. All uncertainties are evaluated at the 50% signal efficiency working point.

<figure><img src="image_8.png" /></figure>

Figure 5: The relative uncertainty in the background rejection due to the cluster systematic uncertainties as a function of the jet $p_T$ for (a) the EFN and (b) ParticleNet taggers. The total uncertainty is the quadrature sum of all cluster uncertainties. All uncertainties are evaluated at the 50% signal efficiency working point.

The track bias systematic variation accounts for possible biases in track $p_T$ measurements due to residual misalignments in the ATLAS tracking detector [106]. This systematic variation biases the ratio of the track's charge to its momentum for charged UFOs, which in turn shifts the UFO $p_T$. It is only applied to charged UFOs, since merged UFOs have their $p_T$ set by the properties of the underlying topological cluster.

The relative uncertainty in background rejection due to the track uncertainties as a function of the jet $p_T$ is shown in Figure 4 for the EFN and ParticleNet taggers. Fluctuations in the track uncertainties result from the finite statistics of the nominal and systematic varied datasets. For both taggers the track fake rate is the dominant uncertainty in most $p_T$ bins. ParticleNet is found to be more sensitive to the track systematic uncertainties than the EFN.
The cluster uncertainties are covered by three systematic variations: the cluster energy scale, the cluster energy resolution, and the cluster position resolution. The cluster reconstruction efficiency is a negligible source of uncertainty at the $p_T$ scale of the clusters contained within the jets in this study. The cluster energy scale systematic variation accounts for uncertainty in the response of the ATLAS calorimeter. A difference in the cluster energy scale between simulated and experimental data would produce a coherent shift in the energy and $p_T$ measurements of topological clusters toward higher or lower energy. The size of these possible differences is estimated by matching topological clusters to isolated inner detector tracks, and studying the differences between the ratio of the topological cluster's energy to the inner detector track's momentum (this quantity is often termed E/p) between simulated and experimental data [97]. The difference in the mean of the E/p distributions between simulated and experimental data gives the cluster energy scale uncertainty. The energy and $p_T$ measurements of the neutral and merged UFOs are then shifted either up or down by an amount within the cluster energy scale uncertainty to create the systematic varied datasets. The final uncertainty is then the maximum of the differences in background rejection between the nominal and the cluster energy scale varied up and down datasets.

The cluster energy resolution systematic variation accounts for uncertainty in the energy resolution of the ATLAS calorimeter. The size of this uncertainty is estimated by comparing the standard deviations of the distributions of E/p between simulated and experimental data. The cluster energy resolution is varied up by an amount within the uncertainty by applying Gaussian smearing to the energy and $p_T$ measurements. It is then varied down by symmetrizing the uncertainty in the tagger efficiency.

Finally, the cluster position resolution systematic variation accounts for uncertainty in the position resolution of the ATLAS calorimeter. The size of this uncertainty is set by comparing the angular coordinates between isolated tracks matched to isolated clusters. Like the cluster energy resolution systematic, the cluster position resolution is varied up by applying Gaussian noise, but it acts on the $\eta$ and $\phi$ coordinates instead of the energy and $p_T$. It is varied down by symmetrizing the uncertainty in the tagger efficiency. This variation is applied only to neutral UFOs since the $\eta$ and $\phi$ coordinates of merged UFOs are set by the underlying inner detector track.

In addition to these systematic variations, the taggers may be sensitive to the splitting and merging of topological clusters, where a single particle is reconstructed as two or more clusters, or two or more particles are reconstructed as single cluster. This paper makes the simplifying assumption that this effect, and any other effects that may bias the reconstruction of jet constituents, is negligible.

The relative uncertainty in background rejection due to the cluster uncertainties as a function of the jet $p_T$ is shown in Figure 5 for the EFN and ParticleNet taggers. For both taggers the cluster energy scale uncertainty is dominant in all $p_T$ bins.

## 6.2 Theoretical uncertainties

Quarks and gluons produced in proton-proton collisions undergo a parton shower and hadronization before they form jets. Given the strongly coupled nature of QCD, the description of the parton shower and hadronization processes within the MC simulation is not exact. The possible differences between simulated and experimental data that result are covered by parton shower and hadronization modeling uncertainties. For the $Z' \to t\bar{t}$ process used to obtain a sample of boosted top jets, these uncertainties are estimated by evaluating the tagger background rejection with jets obtained from the two samples of simulated SM $t\bar{t}$ events described in Section 2. These are pure signal jets, so to set an uncertainty in the background rejection the requirements which produce a 50% signal efficiency are re-calculated using the jets obtained
<figure><img src="image_9.png" /><figcaption>Figure 6: The relative uncertainty in the background rejection due to the parton shower and hadronization modeling as a function of the jet $p_T$ for (a) the EFN and (b) ParticleNet taggers. The total uncertainty is the quadrature sum of all parton shower and hadronization modeling uncertainties. All uncertainties are evaluated at the 50% signal efficiency working point. The uncertainty due to the modeling in the signal process is larger than for the background process, but the signal process uncertainty covers both the parton shower and hadronization modeling uncertainties together.</figcaption></figure>

from the SM $t\bar{t}$ events, and then the background rejection is re-calculated using these requirements. The difference between the background rejections calculated with the two sets of requirements is taken as the uncertainty. Since the SM $t\bar{t}$ process only produces jets in a limited kinematic range, the uncertainty is extrapolated to high $p_T$ by assigning the maximum measured uncertainty to all $p_T$ bins above 1.5 TeV.

For the QCD multijet process used to obtain light-quark and gluon jets, the uncertainty due to the parton shower and hadronization modeling is estimated by comparing background rejections between the four alternative samples described in Section 2. The parton shower modeling uncertainty is estimated by comparing background rejections between the two HERWIG generated samples which differ only by the parton shower model. The hadronization modeling uncertainty is likewise estimated by comparing background rejections between the SHERPA generated samples which differ only by the hadronization model. In both cases the difference in background rejection between the two samples is taken as the uncertainty. In a realistic physics analysis involving top quarks, backgrounds would typically be estimated with data-driven methods instead of with simulated data, so the parton shower and hadronization modeling uncertainties for the QCD multijet process would not be relevant. However the taggers are trained on simulated data, so it is useful to know how the tagger performance is affected by these uncertainties.

The relative uncertainty in background rejection due to the parton shower and hadronization uncertainties as a function of the jet $p_T$ is shown in Figure 6 for the EFN and ParticleNet taggers. Overall the signal process modeling uncertainties are larger than the background process modeling uncertainties. However the signal process uncertainty covers both the parton shower and hadronization modeling uncertainties together. The modeling uncertainties for the background process tend to decrease with increasing jet $p_T$. The fluctuations in the uncertainties at high $p_T$, where the uncertainties are small, are due to the finite statistics of the nominal and systematic varied datasets. It is difficult to discern a trend for the modeling uncertainties for the signal process due to the limited kinematic range of the SM $t\bar{t}$ process. As with the experimental uncertainties, ParticleNet is much more sensitive to the parton shower and hadronization modeling uncertainties than the EFN.
<figure><img src="image_10.png" /></figure>

Figure 7: The relative uncertainty in the background rejection due to the choice of renormalization and factorization scales as a function of the jet $p_T$ for (a) the EFN and (b) ParticleNet taggers. The total uncertainty is the quadrature sum of all renormalization and factorization scale uncertainties. All uncertainties are evaluated at the 50% signal efficiency working point.

The fixed-order matrix element calculations used in the simulation of the $Z' \to t\bar{t}$ and QCD multijet processes require a choice of factorization and renormalization scales to remove divergences. The choice of this scale is unphysical and should not affect the final result of the calculation, but in practice this can happen due to the truncation of the perturbative series. The scale uncertainties cover possible mis-modeling of the underlying physical processes due to the choice of these scales. It is assessed by applying PYTHIA parton shower weights [107] which vary the renormalization and factorization scales for both the initial state radiation (ISR) and final state radiation (FSR) up and down by factors of two. The uncertainty is then estimated by comparing the background rejection of the taggers calculated with the nominal sample to the background rejection calculated with each jet weighted by the relevant shower weight. The uncertainties are then symmetrized by taking the envelope over the up and down variations. This process is performed separately for the $Z' \to t\bar{t}$ and QCD multijet simulation samples. The relative uncertainty in the background rejection due to the choice of renormalization and factorization scales as a function of the jet $p_T$ is shown in Figure 7 for the EFN and ParticleNet taggers. The fluctuations in these uncertainties are due to the finite statistics of the testing set. Again, ParticleNet is associated with larger uncertainties than the EFN. Both taggers show much larger sensitivity to the FSR scales used in generating the $Z' \to t\bar{t}$ sample compared to the other scales.

## 6.3 Validation of uncertainties

To place the uncertainties derived in this study in the context of the scale factor approach utilized by realistic physics analyses, a comparison is made between the two methods for the hIDNN tagger. For comparing uncertainties between taggers, it is useful to calculate uncertainties on the background rejection so that a single metric can be used to characterize both tagger performance and the size of the uncertainties. However physics analyses targeting boosted top-quarks are interested in the uncertainties on the signal efficiency, since backgrounds are typically estimated with data-driven methods. Therefore boosted top tagger scale factors are derived for the signal efficiency, so the uncertainties in this study must be transferred to this quantity for a direct comparison. This excludes the parton shower and hadronization modeling
<figure><img src="image_11.png" /><figcaption>Figure 8: (a) The uncertainty in the signal efficiency for the hIDNN tagger, calculated as the quadrature sum of all uncertainties considered in this study except the parton shower and hadronization modeling uncertainty for the QCD multijet process. (b) A comparison between the total uncertainty derived in this study and the scale factor and its uncertainty for the hIDNN tagger. The scale factor is measured using the same methods as described in Ref. [66]</figcaption></figure>

uncertainty for the QCD multijet process, since it is not relevant for the signal efficiency. The uncertainty budget for the signal efficiency is shown for the hIDNN tagger in Figure 8(a). Within the jet $p_T$ range of [350 – 1000] GeV in which scale factors are derived, the parton shower and hadronization modeling uncertainty for the $Z' \to t\bar{t}$ process is the dominant uncertainty.

To calculate the scale factor, the signal efficiency in experimental data is measured using the highest $p_T$ large-radius jet in events taken from a semi-leptonic $t\bar{t}$ enriched signal region. The uncertainty in the scale factor is calculated by propagating experimental and theoretical uncertainties through the template fit used to extract the signal efficiency. For details on scale-factor measurements see Ref. [66]. The scale factor is a measurement of the ratio of the signal efficiencies in simulated and experimental data, along with the uncertainty in this ratio. In contrast the uncertainties derived in this study provide an upper bound, or maximum possible value, for the magnitude of the difference in tagger efficiency between simulated and experimental data. The deviation of the scale factors from one should then be smaller than the uncertainties derived in this study. Figure 8(b) shows a comparison between the upper bound on the magnitude of the difference in efficiency derived in this study and the scale factor and its uncertainty for the hIDNN tagger. The upper bound covers the central value of the scale factor in all $p_T$ bins, providing good validation of the approach.

## 6.4 Total uncertainties

The total uncertainty, calculated as the quadrature sum of all sources of uncertainty described above, is shown as a function of the jet $p_T$ for the EFN and ParticleNet taggers in Figure 9. For both the EFN and ParticleNet, the parton shower and hadronization modeling uncertainties for the $Z' \to t\bar{t}$ process are the largest in most $p_T$ bins. These are followed by the factorization and renormalization scales and background parton shower and hadronization modeling uncertainties. The total uncertainty as a function of the jet $p_T$ is shown for all constituent-based taggers in Figure 10(a). All taggers have the largest uncertainties at low jet $p_T$ where the theoretical uncertainties produce large and equal contributions to the total uncertainty. Figure 10(b) shows the total uncertainty in the background rejection across the entire $p_T$ range plotted against the
<figure><img src="image_12.png" /><figcaption>Figure 9: The total uncertainty budget in the background rejection as a function of the jet $p_T$ for (a) the EFN and (b) ParticleNet taggers. All uncertainties are evaluated at the 50% signal efficiency working point.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Figure 10: (a) The total uncertainty for each of the constituent-based taggers considered in this study, and (b) the total uncertainty in the background rejection across the entire testing set plotted against the background rejection for each constituent-based tagger. All uncertainties are evaluated at the 50% signal efficiency working point.</figcaption></figure>

background rejection for all taggers considered. A clear correlation between background rejection and uncertainty in the background rejection is visible, with the most powerful taggers producing the largest uncertainties. The exception to this correlation is ResNet50, which is associated with large uncertainties while also having poor performance.

# 7 Conclusion

This paper presents the performance of selected constituent-based jet taggers on a top tagging task. Several constituent-based taggers (DNN, PFN, and ParticleNet) outperform the high-level-quantity-based baseline tagger, while the EFN and ResNet50 underperform. This underperformance was not observed in studies
performed with a parametric detector simulation [7], highlighting the importance of developing taggers in
the context of realistic detector simulation.

The systematic uncertainties that would result from the application of taggers to experimental data are
then probed by applying systematic variations directly to the simulated data. A strong correlation between
tagger performance and the size of the uncertainties is observed. The theoretical uncertainties were found
to be dominant for all taggers considered. In particular the parton shower and hadronization modeling
uncertainties are dominant in most p<sub>T</sub> bins for all taggers. Compared with the theoretical uncertainties,
the experimental uncertainties are found to be small. The uncertainties derived in this study are not
a scale-factor measurement, or uncertainties on a scale factor. The experimental uncertainties rely on
simplifying assumptions which ignore possible differences between simulated and experimental data. They
should be considered as an illustration of the bottom-up approach to experimental systematic uncertainties,
rather than a set of uncertainties that could be used in a physics analysis. However they are useful
for establishing the sensitivity of the different taggers to possible differences between simulated and
experimental data.

The systematic uncertainties derived in this study stem from performance differences between the nominal and systematic varied datasets. Therefore large uncertainties suggest that large performance differences between the nominal datasets and experimental data are possible. The highly performant jet taggers that produce large systematic uncertainties in this study could have lower performance in experimental data than in simulated data, meaning they would not deliver the sensitivity improvements expected from performance studies which do not consider systematic uncertainties. Even if these performance differences are accounted for by scale-factor measurements, such a degradation in tagger performance is not recoverable. Additionally scale-factor measurements also have uncertainties which are expected to correlate with the uncertainties derived in this study. This was verified by comparing the size of the uncertainties derived for the hIDNN in this study to scale factors and scale-factor uncertainties derived using semi-leptonic t̄t events with the methods described in Ref. [66], and finding good agreement. Scale-factor uncertainties will be included as a systematic uncertainty in any physics analysis which uses a jet tagger, so larger scale-factor uncertainties will also degrade the sensitivity improvements from a more performant tagger.

The size of scale-factor uncertainties relative to other uncertainties in a physics analysis will be highly
dependent on the context, so it is difficult to predict if larger scale-factor uncertainties would be limiting
for a given physics analysis. In some cases larger scale-factor uncertainties could present a limitation
in the sensitivity improvements expected from ML-based jet tagging techniques. Reducing the size of
these systematic uncertainties without compromising the tagger performance is then an important direction
for future research. Two possible directions are to design taggers which are robust against, or aware of
systematic effects [70–74, 108–112], or to train taggers directly on experimental data in a weakly supervised
setting [113–115]. To support progress in the first direction and to allow a more realistic assessment of the
performance of other existing taggers, the datasets used in this study are made publicly available [116].
This includes all nominal and systematic varied datasets, including the alternative Monte Carlo samples
used to assess the parton shower and hadronization modeling uncertainties. Additional documentation
is provided, which details how to use the datasets to assess the uncertainties associated with an arbitrary
tagger.
Acknowledgements

We thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently.

The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [117].

We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Türkiye; STFC/UKRI, United Kingdom; DOE and NSF, United States of America.

Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.

In addition, individual members wish to acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigación y Desarrollo (FONDECYT 1230812, FONDECYT 1230987, FONDECYT 1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), National Natural Science Foundation of China (NSFC - 12175119, NSFC 12275265, NSFC-12075060); Czech Republic: Czech Science Foundation (GACR - 24-11373S), Ministry of Education Youth and Sports (FORTE CZ.02.01.01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC - 101002463); European Union: European Research Council (ERC - 948254, ERC 101089007), Horizon 2020 Framework Programme (MUCCA - CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Württemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG - 469666862, DFG - CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Università e della Ricerca (PRIN - 20223N7F8K - PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP22H01227, JSPS
KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAKENHI JP23KK0245); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020 - VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 no 9722), Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085); Slovenia: Slovenian Research Agency (ARIS grant J1-3010); Spain: Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN & NextGenEU PCI2022-135018-2, MICIN & FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/027); Sweden: Swedish Research Council (Swedish Research Council 2023-04654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF - PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIF-R1-231091); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.

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The ATLAS Collaboration

G. Aad (104), E. Aakvaag (17), B. Abbott (123), S. Abdelhameed (119a), K. Abeling (56), N.J. Abicht (50), S.H. Abidi (30), M. Aboelela (45), A. Aboulhorma (36e), H. Abramowicz (154), H. Abreu (153), Y. Abulaiti (120), B.S. Acharya (70a,70b), A. Ackermann (64a), C. Adam Bourdarios (4), L. Adamczyk (87a), S.V. Addepalli (27), M.J. Addison (103), J. Adelman (118), A. Adiguzel (22c), T. Adye (137), A.A. Affolder (139), Y. Afik (40), M.N. Agaras (13), J. Agarwala (74a,74b), A. Aggarwal (102), C. Agheorghiesei (28c), F. Ahmadov (39,y), W.S. Ahmed (106), S. Ahuja (97), X. Ai (63e), G. Aielli (77a,77b), A. Aikot (166), M. Ait Tamlihat (36e), B. Aitbenchikh (36a), M. Akbiyik (102), T.P.A. Åkesson (100), A.V. Akimov (38), D. Akiyama (171), N.N. Akolkar (25), S. Aktas (22a), K. Al Khoury (42), G.L. Alberghi (24b), J. Albert (168), P. Albicocco (54), G.L. Albouy (61), S. Alderweireldt (53), Z.L. Alegria (124), M. Aleksa (37), I.N. Aleksandrov (39), C. Alexa (28b), T. Alexopoulos (10), F. Alfonsi (24b), M. Algren (57), M. Alhroob (170), B. Ali (135), H.M.J. Ali (93), S. Ali (32), S.W. Alibocus (94), M. Aliev (34c), G. Alimonti (72a), W. Alkakhi (56), C. Allaire (67), B.M.M. Allbrooke (149), J.F. Allen (53), C.A. Allendes Flores (140f), P.P. Allport (21), A. Aloisio (73a,73b), F. Alonso (92), C. Alpigiani (141), Z.M.K. Alsolami (93), M. Alvarez Estevez (101), A. Alvarez Fernandez (102), M. Alves Cardoso (57), M.G. Alviggi (73a,73b), M. Aly (103), Y. Amaral Coutinho (84b), A. Ambler (106), C. Amelung (37), M. Amerl (103), C.G. Ames (111), D. Amidei (108), B. Amini (55), K.J. Amirie (158), S.P. Amor Dos Santos (133a), K.R. Amos (166), S. An (85), V. Ananiev (128), C. Anastopoulos (142), T. Andeen (11), J.K. Anders (37), A.C. Anderson (60), S.Y. Andrean (48a,48b), A. Andreazza (72a,72b), S. Angelidakis (9), A. Angerami (42), A.V. Anisenkov (38), A. Annovi (75a), C. Antel (57), E. Antipov (148), M. Antonelli (54), F. Anulli (76a), M. Aoki (85), T. Aoki (156), M.A. Aparo (149), L. Aperio Bella (49), C. Appelt (19), A. Apyan (27), S.J. Arbiol Val (88), C. Arcangeletti (54), A.T.H. Arce (52), J-F. Arguin (110), S. Argyropoulos (55), J.-H. Arling (49), O. Arnaez (4), H. Arnold (148), G. Artoni (76a,76b), H. Asada (113), K. Asai (121), S. Asai (156), N.A. Asbah (37), R.A. Ashby Pickering (170), K. Assamagan (30), R. Astalos (29a), K.S.V. Astrand (100), S. Atashi (162), R.J. Atkin (34a), M. Atkinson (165), H. Atmani (36f), P.A. Atmasiddha (131), K. Augsten (135), S. Auricchio (73a,73b), A.D. Auriol (21), V.A. Austrup (103), G. Avolio (37), K. Axiotis (57), G. Azuelos (110,ad), D. Babal (29b), H. Bachacou (138), K. Bachas (155,p), A. Bachiu (35), F. Backman (48a,48b), A. Badea (40), T.M. Baer (108), P. Bagnaia (76a,76b), M. Bahmani (19), D. Bahner (55), K. Bai (126), J.T. Baines (137), L. Baines (96), O.K. Baker (175), E. Bakos (16), D. Bakshi Gupta (8), L.E. Balabram Filho (84b), V. Balakrishnan (123), R. Balasubramanian (117), E.M. Baldin (38), P. Balek (87a), E. Ballabene (24b,24a), F. Balli (138), L.M. Baltes (64a), W.K. Balunas (33), J. Balz (102), I. Bamwidhi (119b), E. Banas (88), M. Bandieramonte (132), A. Bandyopadhyay (25), S. Bansal (25), L. Barak (154), M. Barakat (49), E.L. Barberio (107), D. Barberis (58b,58a), M. Barbero (104), M.Z. Barel (117), T. Barillari (112), M-S. Barisits (37), T. Barklow (146), P. Baron (125), D.A. Baron Moreno (103), A. Baroncelli (63a), A.J. Barr (129), J.D. Barr (98), F. Barreiro (101), J. Barreiro Guimarães da Costa (14), U. Barron (154), M.G. Barros Teixeira (133a), S. Barsov (38), F. Bartels (64a), R. Bartoldus (146), A.E. Barton (93), P. Bartos (29a), A. Basan (102), M. Baselga (50), A. Bassalat (67,b), M.J. Basso (159a), S. Bataju (45), R. Bate (167), R.L. Bates (60), S. Batlamous (101), B. Batool (144), M. Battaglia (139), D. Battulga (19), M. Bauce (76a,76b), M. Bauer (80), P. Bauer (25), L.T. Bazzano Hurrell (31), J.B. Beacham (52), T. Beau (130), J.Y. Beaucamp (92), P.H. Beauchemin (161), P. Bechtle (25), H.P. Beck (20,o), K. Becker (170), A.J. Beddall (83), V.A. Bednyakov (39), C.P. Bee (148), L.J. Beemster (16), T.A. Beermann (37), M. Begalli (84d), M. Begel (30), A. Behera (148), J.K. Behr (49), J.F. Beirer (37), F. Beisiegel (25), M. Belfkir (119b), G. Bella (154), L. Bellagamba (24b), A. Bellerive (35), P. Bellos (21), K. Beloborodov (38), D. Benchekroun (36a), F. Bendebba (36a), Y. Benhammou (154),
K.C. Benkendorfer (62), L. Beresford (49), M. Beretta (54), E. Bergeaas Kuutmann (164), N. Berger (4), B. Bergmann (135), J. Beringer (18a), G. Bernardi (5), C. Bernius (146), F.U. Bernlochner (25), F. Bernon (37,104), A. Berrocal Guardia (13), T. Berry (97), P. Berta (136), A. Berthold (51), S. Bethke (112), A. Betti (76a,76b), A.J. Bevan (96), N.K. Bhalla (55), S. Bhatta (148), D.S. Bhattacharya (169), P. Bhattarai (146), K.D. Bhide (55), V.S. Bhopatkar (124), R.M. Bianchi (132), G. Bianco (24b,24a), O. Biebel (111), R. Bielski (126), M. Biglietti (78a), C.S. Billingsley (45), Y. Bimgdi (36f), M. Bindi (56), A. Bingul (22b), C. Bini (76a,76b), G.A. Bird (33), M. Birman (172), M. Biros (136), S. Biryukov (149), T. Bisanz (50), E. Bisceglie (44b,44a), J.P. Biswal (137), D. Biswas (144), I. Bloch (49), A. Blue (60), U. Blumenschein (96), J. Blumenthal (102), V.S. Bobrovnikov (38), M. Boehler (55), B. Boehm (169), D. Bogavac (37), A.G. Bogdanchikov (38), C. Bohm (48a), V. Boisvert (97), P. Bokan (37), T. Bold (87a), M. Bomben (5), M. Bona (96), M. Boonekamp (138), C.D. Booth (97), A.G. Borbély (60), I.S. Bordulev (38), G. Borissov (93), D. Bortoletto (129), D. Boscherini (24b), M. Bosman (13), J.D. Bossio Sola (37), K. Bouaouda (36a), N. Bouchhar (166), L. Boudet (4), J. Boudreau (132), E.V. Bouhova-Thacker (93), D. Boumediene (41), R. Bouquet (58b,58a), A. Boveia (122), J. Boyd (37), D. Boye (30), I.R. Boyko (39), L. Bozianu (57), J. Bracinik (21), N. Brahimi (4), G. Brandt (174), O. Brandt (33), F. Braren (49), B. Brau (105), J.E. Brau (126), R. Brener (172), L. Brenner (117), R. Brenner (164), S. Bressler (172), G. Brianti (79a,79b), D. Britton (60), D. Britzger (112), I. Brock (25), G. Brooijmans (42), E.M. Brooks (159b), E. Brost (30), L.M. Brown (168), L.E. Bruce (62), T.L. Bruckler (129), P.A. Bruckman de Renstrom (88), B. Brüers (49), A. Bruni (24b), G. Bruni (24b), M. Bruschi (24b), N. Bruscino (76a,76b), T. Buanes (17), Q. Buat (141), D. Buchin (112), A.G. Buckley (60), O. Bulekov (38), B.A. Bullard (146), S. Burdin (94), C.D. Burgard (50), A.M. Burger (37), B. Burghgrave (8), O. Burlayenko (55), J. Burleson (165), J.T.P. Burr (33), J.C. Burzynski (145), E.L. Busch (42), V. Büscher (102), P.J. Bussey (60), J.M. Butler (26), C.M. Buttar (60), J.M. Butterworth (98), W. Buttinger (137), C.J. Buxo Vazquez (109), A.R. Buzykaev (38), S. Cabrera Urbán (166), L. Cadamuro (67), D. Caforio (59), H. Cai (132), Y. Cai (14,114c), Y. Cai (114a), V.M.M. Cairo (37), O. Cakir (3a), N. Calace (37), P. Calafiura (18a), G. Calderini (130), P. Calfayan (69), G. Callea (60), L.P. Caloba (84b), D. Calvet (41), S. Calvet (41), M. Calvetti (75a,75b), R. Camacho Toro (130), S. Camarda (37), D. Camarero Munoz (27), P. Camarri (77a,77b), M.T. Camerlingo (73a,73b), D. Cameron (37), C. Camincher (168), M. Campanelli (98), A. Camplani (43), V. Canale (73a,73b), A.C. Canbay (3a), E. Canonero (97), J. Cantero (166), Y. Cao (165), F. Capocasa (27), M. Capua (44b,44a), A. Carbone (72a,72b), R. Cardarelli (77a), J.C.J. Cardenas (8), G. Carducci (44b,44a), T. Carli (37), G. Carlino (73a), J.I. Carlotto (13), B.T. Carlson (132,q), E.M. Carlson (168,159a), J. Carmignani (94), L. Carminati (72a,72b), A. Carnelli (138), M. Carnesale (76a,76b), S. Caron (116), E. Carquin (140f), S. Carrá (72a), G. Carratta (24b,24a), A.M. Carroll (126), T.M. Carter (53), M.P. Casado (13,i), M. Caspar (49), F.L. Castillo (4), L. Castillo Garcia (13), V. Castillo Gimenez (166), N.F. Castro (133a,133e), A. Catinaccio (37), J.R. Catmore (128), T. Cavaliere (4), V. Cavaliere (30), N. Cavalli (24b,24a), L.J. Caviedes Betancourt (23b), Y.C. Cekmecelioglu (49), E. Celebi (83), S. Cella (37), F. Celli (129), M.S. Centonze (71a,71b), V. Cepaitis (57), K. Cerny (125), A.S. Cerqueira (84a), A. Cerri (149), L. Cerrito (77a,77b), F. Cerutti (18a), B. Cervato (144), A. Cervelli (24b), G. Cesarini (54), S.A. Cetin (83), D. Chakraborty (118), J. Chan (18a), W.Y. Chan (156), J.D. Chapman (33), E. Chapon (138), B. Chargeishvili (152b), D.G. Charlton (21), M. Chatterjee (20), C. Chauhan (136), Y. Che (114a), S. Chekanov (6), S.V. Chekulaev (159a), G.A. Chelkov (39,a), A. Chen (108), B. Chen (154), B. Chen (168), H. Chen (114a), H. Chen (30), J. Chen (63c), J. Chen (145), M. Chen (129), S. Chen (156), S.J. Chen (114a), X. Chen (63c), X. Chen (15,ac), Y. Chen (63a), C.L. Cheng (173), H.C. Cheng (65a), S. Cheong (146), A. Cheplakov (39), E. Cheremushkina (49), E. Cherepanova (117), R. Cherkaoui El Moursli (36e), E. Cheu (7), K. Cheung (66), L. Chevalier (138), V. Chiarella (54), G. Chiarelli (75a), N. Chiedde (104), G. Chiodini (71a), A.S. Chisholm (21), A. Chitan (28b), M. Chitishvili (166), M.V. Chizhov (39,r,
K. Choi ID11, Y. Chou ID141, E.Y.S. Chow ID116, K.L. Chu ID172, M.C. Chu ID65a, X. Chu ID14,114c,
Z. Chubinidze ID54, J. Chudoba ID134, J.J. Chwastowski ID88, D. Cieri ID112, K.M. Ciesla ID87a,
V. Cindro ID95, A. Ciocio ID18a, F. Cirotto ID73a,73b, Z.H. Citron ID172, M. Citterio ID72a, D.A. Ciubotaru²⁸b,
A. Clark ID57, P.J. Clark ID53, N. Clarke Hall ID98, C. Clarry ID158, J.M. Clavijo Columbie ID49,
S.E. Clawson ID49, C. Clement ID48a,48b, Y. Coadou ID104, M. Cobal ID70a,70c, A. Coccaro ID58b,
R.F. Coelho Barrue ID133a, R. Coelho Lopes De Sa ID105, S. Coelli ID72a, B. Cole ID42, J. Collot ID61,
P. Conde Muiño ID133a,133g, M.P. Connell ID34c, S.H. Connell ID34c, E.I. Conroy ID129, F. Conventi ID73a,ae,
H.G. Cooke ID21, A.M. Cooper-Sarkar ID129, F.A. Corchia ID24b,24a, A. Cordeiro Oudot Choi ID130,
L.D. Corpe ID41, M. Corradi ID76a,76b, F. Corriveau ID106x, A. Cortes-Gonzalez ID19, M.J. Costa ID166,
F. Costanza ID4, D. Costanzo ID142, B.M. Cote ID122, J. Couthures ID4, G. Cowan ID97, K. Cranmer ID173,
D. Cremonini ID24b,24a, S. Crépé-Renaudin ID61, F. Crescioli ID130, M. Cristinziani ID144,
M. Cristoforetti ID79a,79b, V. Croft ID117, J.E. Crosby ID124, G. Crosetti ID44b,44a, A. Cueto ID101, H. Cui ID98,
Z. Cui ID7, W.R. Cunningham ID60, F. Curcio ID166, J.R. Curran ID53, P. Czodrowski ID37,
M.J. Da Cunha Sargedas De Sousa ID58b,58a, J.V. Da Fonseca Pinto ID84b, C. Da Via ID103,
W. Dabrowski ID87a, T. Dado ID37, S. Dahbi ID151, T. Dai ID108, D. Dal Santo ID20, C. Dallapiccola ID105,
M. Dam ID43, G. D'amen ID30, V. D'Amico ID111, J. Damp ID102, J.R. Dandoy ID35, D. Dannheim ID37,
M. Danninger ID145, V. Dao ID148, G. Darbo ID58b, S.J. Das ID30,af, F. Dattola ID49, S. D'Auria ID72a,72b,
A. D'avanzo ID73a,73b, C. David ID34a, T. Davidek ID136, I. Dawson ID96, H.A. Day-hall ID135, K. De ID8,
R. De Asmundis ID73a, N. De Biase ID49, S. De Castro ID24b,24a, N. De Groot ID116, P. de Jong ID117,
H. De la Torre ID118, A. De Maria ID114a, A. De Salvo ID76a, U. De Sanctis ID77a,77b, F. De Santis ID71a,71b,
A. De Santo ID149, J.B. De Vivie De Regie ID61, D.V. Dedovich³⁹, J. Degens ID94, A.M. Deiana ID45,
F. Del Corso ID24b,24a, J. Del Peso ID101, F. Del Rio ID64a, L. Delagrange ID130, F. Deliot ID138,
C.M. Delitzsch ID50, M. Della Pietra ID73a,73b, D. Della Volpe ID57, A. Dell'Acqua ID37,
L. Dell'Asta ID72a,72b, M. Delmastro ID4, P.A. Delsart ID61, S. Demers ID175, M. Demichev ID39,
S.P. Denisov ID38, L. D'Eramo ID41, D. Derendarz ID88, F. Derue ID130, P. Dervan ID94, K. Desch ID25,
C. Deutsch ID25, F.A. Di Bello ID58b,58a, A. Di Ciaccio ID77a,77b, L. Di Ciaccio ID4,
A. Di Domenico ID76a,76b, C. Di Donato ID73a,73b, A. Di Girolamo ID37, G. Di Gregorio ID37,
A. Di Luca ID79a,79b, B. Di Micco ID78a,78b, R. Di Nardo ID78a,78b, K.F. Di Petrillo ID40,
M. Diamantopoulou ID35, F.A. Dias ID117, T. Dias Do Vale ID145, M.A. Diaz ID140a,140b,
F.G. Diaz Capriles ID25, A.R. Didenko³⁹, M. Didenko ID166, E.B. Diehl ID108, S. Díez Cornell ID49,
C. Diez Pardos ID144, C. Dimitriadi ID164, A. Dimitrievska ID21, J. Dingfelder ID25, T. Dingley ID129,
I-M. Dinu ID28b, S.J. Dittmeier ID64b, F. Dittus ID37, M. Divisek ID136, F. Djama ID104, T. Djobava ID152b,
C. Doglioni ID103,100, A. Dohnalova ID29a, J. Dolejsi ID136, Z. Dolezal ID136, K. Domijan ID87a,
K.M. Dona ID40, M. Donadelli ID84d, B. Dong ID109, J. Donini ID41, A. D'Onofrio ID73a,73b,
M. D'Onofrio ID94, J. Dopke ID137, A. Doria ID73a, N. Dos Santos Fernandes ID133a, P. Dougan ID103,
M.T. Dova ID92, A.T. Doyle ID60, M.A. Draguet ID129, E. Dreyer ID172, I. Drivas-koulouris ID10,
M. Drnevich ID120, M. Drozdova ID57, D. Du ID63a, T.A. du Pree ID117, F. Dubinin ID38, M. Dubovsky ID29a,
E. Duchovni ID172, G. Duckeck ID111, O.A. Ducu ID28b, D. Duda ID53, A. Dudarev ID37, E.R. Duden ID27,
M. D'uffizi ID103, L. Duflot ID67, M. Dührssen ID37, I. Duminica ID28g, A.E. Dumitriu ID28b,
M. Dunford ID64a, S. Dungs ID50, K. Dunne ID48a,48b, A. Duperrin ID104, H. Duran Yildiz ID3a,
M. Düren ID59, A. Durglishvili ID152b, B.L. Dwyer ID118, G.I. Dyckes ID18a, M. Dyndal ID87a,
B.S. Dziedzic ID37, Z.O. Earnshaw ID149, G.H. Eberwein ID129, B. Eckerova ID29a, S. Eggebrecht ID56,
E. Egidio Purcino De Souza ID84e, L.F. Ehrke ID57, G. Eigen ID17, K. Einsweiler ID18a, T. Ekelof ID164,
P.A. Ekman ID100, S. El Farkh ID36b, Y. El Ghazali ID63a, H. El Jarrari ID37, A. El Moussaouy ID36a,
V. Ellajosyula ID164, M. Ellert ID164, F. Ellinghaus ID174, N. Ellis ID37, J. Elmsheuser ID30, M. Elsawy ID119a,
M. Elsing ID37, D. Emeliyanov ID137, Y. Enari ID85, I. Ene ID18a, S. Epari ID13, P.A. Erland ID88,
D. Ernani Martins Neto ID88, M. Errenst ID174, M. Escalier ID67, C. Escobar ID166, E. Etzion ID154,
G. Evans (133a), H. Evans (69), L.S. Evans (97), A. Ezhilov (38), S. Ezzarqtouni (36a), F. Fabbri (24b,24a), L. Fabbri (24b,24a), G. Facini (98), V. Fadeyev (139), R.M. Fakhrutdinov (38), D. Fakoudis (102), S. Falciano (76a), L.F. Falda Ulhoa Coelho (37), F. Fallavollita (112), G. Falsetti (44b,44a), J. Faltova (136), C. Fan (165), Y. Fan (14), Y. Fang (14,114c), M. Fanti (72a,72b), M. Faraj (70a,70b), Z. Farazpay (99), A. Farbin (8), A. Farilla (78a), T. Farooque (109), S.M. Farrington (53), F. Fassi (36e), D. Fassouliotis (9), M. Faucci Giannelli (77a,77b), W.J. Fawcett (33), L. Fayard (67), P. Federic (136), P. Federicova (134), O.L. Fedin (38a), M. Feickert (173), L. Feligioni (104), D.E. Fellers (126), C. Feng (63b), Z. Feng (117), M.J. Fenton (162), L. Ferencz (49), R.A.M. Ferguson (93), S.I. Fernandez Luengo (140f), P. Fernandez Martinez (13), M.J.V. Fernoux (104), J. Ferrando (93), A. Ferrari (164), P. Ferrari (117,116), R. Ferrari (74a), D. Ferrere (57), C. Ferretti (108), D. Fiacco (76a,76b), F. Fiedler (102), P. Fiedler (135), A. Filipčič (95), E.K. Filmer (1), F. Filthaut (116), M.C.N. Fiolhais (133a,133c,c), L. Fiorini (166), W.C. Fisher (109), T. Fitschen (103), P.M. Fitzhugh (138), I. Fleck (144), P. Fleischmann (108), T. Flick (174), M. Flores (34d,aa), L.R. Flores Castillo (65a), L. Flores Sanz De Acedo (37), F.M. Follega (79a,79b), N. Fomin (33), J.H. Foo (158), A. Formica (138), A.C. Forti (103), E. Fortin (37), A.W. Fortman (18a), M.G. Foti (18a), L. Fountas (9j), D. Fournier (67), H. Fox (93), P. Francavilla (75a,75b), S. Francescato (62), S. Franchellucci (57), M. Franchini (24b,24a), S. Franchino (64a), D. Francis (37), L. Franco (116), V. Franco Lima (37), L. Franconi (49), M. Franklin (62), G. Frattari (27), Y.Y. Frid (154), J. Friend (60), N. Fritzsche (37), A. Froch (55), D. Froidevaux (37), J.A. Frost (129), Y. Fu (63a), S. Fuenzalida Garrido (140f), M. Fujimoto (104), K.Y. Fung (65a), E. Furtado De Simas Filho (84e), M. Furukawa (156), J. Fuster (166), A. Gaa (56), A. Gabrielli (24b,24a), A. Gabrielli (158), P. Gadow (37), G. Gagliardi (58b,58a), L.G. Gagnon (18a), S. Gaid (163), S. Galantzan (154), E.J. Gallas (129), B.J. Gallop (137), K.K. Gan (122), S. Ganguly (156), Y. Gao (53), F.M. Garay Walls (140a,140b), B. Garcia (30), C. Garcia (166), A. Garcia Alonso (117), A.G. Garcia Caffaro (175), J.E. Garcia Navarro (166), M. Garcia-Sciveres (18a), G.L. Gardner (131), R.W. Gardner (40), N. Garelli (161), D. Garg (81), R.B. Garg (146), J.M. Gargan (53), C.A. Garner (158), C.M. Garvey (34a), V.K. Gassmann (161), G. Gaudio (74a), V. Gautam (13), P. Gauzzi (76a,76b), J. Gavranovic (95), I.L. Gavrilenko (38), A. Gavrilyuk (38), C. Gay (167), G. Gaycken (126), E.N. Gazis (10), A.A. Geanta (28b), C.M. Gee (139), A. Gekow (122), C. Gemme (58b), M.H. Genest (61), A.D. Gentry (115), S. George (97), W.F. George (21), T. Geralis (47), P. Gessinger-Befurt (37), M.E. Geyik (174), M. Ghani (170), K. Ghorbanian (96), A. Ghosal (144), A. Ghosh (162), A. Ghosh (7), B. Giacobbe (24b), S. Giagu (76a,76b), T. Giani (117), A. Giannini (63a), S.M. Gibson (97), M. Gignac (139), D.T. Gil (87b), A.K. Gilbert (87a), B.J. Gilbert (42), D. Gillberg (35), G. Gilles (117), L. Ginabat (130), D.M. Gingrich (2,ad), M.P. Giordani (70a,70c), P.F. Giraud (138), G. Giugliarelli (70a,70c), D. Giugni (72a), F. Giuli (37), I. Gkialas (9j), L.K. Gladilin (58), C. Glasman (101), G.R. Gledhill (126), G. Glemža (49), M. Glisic (126), I. Gnesi (44b,e), Y. Go (30), M. Goblirsch-Kolb (37), B. Gocke (50), D. Godin (110), B. Gokturk (22a), S. Goldfarb (107), T. Golling (57), M.G.D. Gololo (34g), D. Golubkov (38), J.P. Gombas (109), A. Gomes (133a,133b), G. Gomes Da Silva (144), A.J. Gomez Delegido (166), R. Gonçalo (133a), L. Gonella (21), A. Gongadze (152c), F. Gonnella (21), J.L. Gonski (146), R.Y. González Andana (53), S. González de la Hoz (166), R. Gonzalez Lopez (94), C. Gonzalez Renteria (18a), M.V. Gonzalez Rodrigues (49), R. Gonzalez Suarez (164), S. Gonzalez-Sevilla (57), L. Goossens (37), B. Gorini (37), E. Gorini (71a,71b), A. Gorišek (95), T.C. Gosart (131), A.T. Goshaw (52), M.I. Gostkin (39), S. Goswami (124), C.A. Gottardo (37), S.A. Gotz (111), M. Gouighri (36b), V. Goumarre (49), A.G. Goussiou (141), N. Govender (34c), R.P. Grabarczyk (129), I. Grabowska-Bold (87a), K. Graham (35), E. Gramstad (128), S. Grancagnolo (71a,71b), C.M. Grant (1,138), P.M. Gravila (28f), F.G. Gravili (71a,71b), H.M. Gray (18a), M. Greco (71a,71b), M.J. Green (1), C. Grefe (25), A.S. Grefsrud (17), I.M. Gregor (49), K.T. Greif (162), P. Grenier (146), S.G. Grewe (112), A.A. Grillo (139), K. Grimm (32), S. Grinstein (13,t), J.-F. Grivaz (67), E. Gross (172), J. Grosse-Knetter (56), J.C. Grundy (129), L. Guan (108), J.G.R. Guerrero Rojas (166),
G. Guerrieri (37), R. Gugel (102), J.A.M. Guhit (108), A. Guida (19), E. Guilloton (170), S. Guindon (37), F. Guo (14,114c), J. Guo (63c), L. Guo (49), Y. Guo (108), R. Gupta (132), S. Gurbuz (25), S.S. Gurdasani (55), G. Gustavino (76a,76b), P. Gutierrez (123), L.F. Gutierrez Zagazeta (131), M. Gutsche (51), C. Gutschow (98), C. Gwenlan (129), C.B. Gwilliam (94), E.S. Haaland (128), A. Haas (120), M. Habedank (49), C. Haber (18a), H.K. Hadavand (8), A. Hadef (51), S. Hadzic (112), A.I. Hagan (93), J.J. Hahn (144), E.H. Haines (98), M. Haleem (169), J. Haley (124), J.J. Hall (142), G.D. Hallewell (104), L. Halser (20), K. Hamano (168), M. Hamer (25), G.N. Hamity (53), E.J. Hampshire (97), J. Han (63b), K. Han (63a), L. Han (114a), L. Han (63a), S. Han (18a), Y.F. Han (158), K. Hanagaki (85), M. Hance (139), D.A. Hangal (42), H. Hanif (145), M.D. Hank (131), J.B. Hansen (43), P.H. Hansen (43), D. Harada (57), T. Harenberg (174), S. Harkusha (38), M.L. Harris (105), Y.T. Harris (129), J. Harrison (13), N.M. Harrison (122), P.F. Harrison (170), N.M. Hartman (112), N.M. Hartmann (111), R.Z. Hasan (97,137), Y. Hasegawa (143), F. Haslbeck (129), S. Hassan (17), R. Hauser (109), C.M. Hawkes (21), R.J. Hawkings (37), Y. Hayashi (156), D. Hayden (109), C. Hayes (108), R.L. Hayes (117), C.P. Hays (129), J.M. Hays (96), H.S. Hayward (94), F. He (63a), M. He (14,114c), Y. He (49), Y. He (98), N.B. Heatley (96), V. Hedberg (100), A.L. Heggelund (128), N.D. Hehir (96,*), C. Heidegger (55), K.K. Heidegger (55), J. Heilman (35), S. Heim (49), T. Heim (18a), J.G. Heinlein (131), J.J. Heinrich (126), L. Heinrich (112,ab), J. Hejbal (134), A. Held (173), S. Hellesund (17), C.M. Helling (167), S. Hellman (48a,48b), R.C.W. Henderson (93), L. Henkelmann (33), A.M. Henriques Correia (37), H. Herde (100), Y. Hernández Jiménez (148), L.M. Herrmann (25), T. Herrmann (51), G. Herten (55), R. Hertenberger (111), L. Hervas (37), M.E. Hesping (102), N.P. Hessey (159a), M. Hidaoui (36b), N. Hidic (136), E. Hill (158), S.J. Hillier (21), J.R. Hinds (109), F. Hinterkeuser (25), M. Hirose (127), S. Hirose (160), D. Hirschbuehl (174), T.G. Hitchings (103), B. Hiti (95), J. Hobbs (148), R. Hobincu (28e), N. Hod (172), M.C. Hodgkinson (142), B.H. Hodkinson (129), A. Hoecker (37), D.D. Hofer (108), J. Hofer (49), T. Holm (25), M. Holzbock (37), L.B.A.H. Hommels (33), B.P. Honan (103), J.J. Hong (69), J. Hong (63c), T.M. Hong (132), B.H. Hooberman (165), W.H. Hopkins (6), M.C. Hoppesch (165), Y. Horii (113), S. Hou (151), A.S. Howard (95), J. Howarth (60), J. Hoya (6), M. Hrabovsky (125), A. Hrynevich (49), T. Hryn'ova (4), P.J. Hsu (66), S.-C. Hsu (141), T. Hsu (67), M. Hu (18a), Q. Hu (63a), S. Huang (65b), X. Huang (14,114c), Y. Huang (142), Y. Huang (102), Y. Huang (14), Z. Huang (103), Z. Hubacek (135), M. Huebner (25), F. Huegging (25), T.B. Huffman (129), C.A. Hugli (49), M. Huhtinen (37), S.K. Huiberts (17), R. Hulsken (106), N. Huseynov (12,g), J. Huston (109), J. Huth (62), R. Hyneman (146), G. Iacobucci (57), G. Iakovidis (30), L. Iconovidou-Fayard (67), J.P. Iddon (37), P. Iengo (73a,73b), R. Iguchi (156), Y. Iiyama (156), T. Iizawa (129), Y. Ikegami (85), N. Ilic (158), H. Imam (84c), M. Ince Lezki (57), T. Ingebretsen Carlson (48a,48b), J.M. Inglis (96), G. Introzzi (74a,74b), M. Iodice (78a), V. Ippolito (76a,76b), R.K. Irwin (94), M. Ishino (156), W. Islam (173), C. Issever (19,49), S. Istin (22a,ah), H. Ito (171), R. Iuppa (79a,79b), A. Ivina (172), J.M. Izen (46), V. Izzo (73a), P. Jacka (134), P. Jackson (1), C.S. Jagfeld (111), G. Jain (159a), P. Jain (49), K. Jakobs (55), T. Jakoubek (172), J. Jamieson (60), W. Jang (156), M. Javurkova (105), P. Jawahar (103), L. Jeanty (126), J. Jejelava (152a,z), P. Jenni (55,f), C.E. Jessiman (35), C. Jia (63b), J. Jia (148), X. Jia (14,114c), Z. Jia (114a), C. Jiang (53), S. Jiggins (49), J. Jimenez Pena (13), S. Jin (114a), A. Jinaru (28b), O. Jinnouchi (157), P. Johansson (142), K.A. Johns (7), J.W. Johnson (139), F.A. Jolly (49), D.M. Jones (149), E. Jones (49), K.S. Jones (8), P. Jones (33), R.W.L. Jones (93), T.J. Jones (94), H.L. Joos (56,37), R. Joshi (122), J. Jovicevic (16), X. Ju (18a), J.J. Junggeburth (105), T. Junkermann (64a), A. Juste Rozas (13,t), M.K. Juzek (88), S. Kabana (140e), A. Kaczmarska (88), M. Kado (112), H. Kagan (122), M. Kagan (146), A. Kahn (131), C. Kahra (102), T. Kaji (156), E. Kajomovitz (153), N. Kakati (172), I. Kalaitzidou (55), C.W. Kalderon (30), N.J. Kang (139), D. Kar (34g), K. Karava (129), M.J. Kareem (159b), E. Karentzos (55), O. Karkout (117), S.N. Karpov (39), Z.M. Karpova (39), V. Kartvelishvili (93), A.N. Karyukhin (38), E. Kasimi (155),
J. Katzy ID49, S. Kaur ID35, K. Kawade ID143, M.P. Kawale ID123, C. Kawamoto ID89, T. Kawamoto ID63a, E.F. Kay ID37, F.I. Kaya ID161, S. Kazakos ID109, V.F. Kazanin ID38, Y. Ke ID148, J.M. Keaveney ID34a, R. Keeler ID168, G.V. Kehris ID62, J.S. Keller ID35, A.S. Kelly98, J.J. Kempster ID149, P.D. Kennedy ID102, O. Kepka ID134, B.P. Kerridge ID137, S. Kersten ID174, B.P. Kerševan ID95, L. Keszeghova ID29a, S. Ketabchi Haghighat ID158, R.A. Khan ID132, A. Khanov ID124, A.G. Kharlamov ID38, T. Kharlamova ID38, E.E. Khoda ID141, M. Kholodenko ID133a, T.J. Khoo ID19, G. Khoriauli ID169, J. Khubua ID152b,*,
Y.A.R. Khwaira ID130, B. Kibirige34g, D. Kim ID6, D.W. Kim ID48a,48b, Y.K. Kim ID40, N. Kimura ID98, M.K. Kingston ID56, A. Kirchhoff ID56, C. Kirfel ID25, F. Kirfel ID25, J. Kirk ID137, A.E. Kiryunin ID112, C. Kitsaki ID10, O. Kivernyk ID25, M. Klassen ID161, C. Klein ID35, L. Klein ID169, M.H. Klein ID45, S.B. Klein ID57, U. Klein ID94, P. Klimek ID37, A. Klimentov ID30, T. Klioutchnikova ID37, P. Kluit ID117, S. Kluth ID112, E. Kneringer ID80, T.M. Knight ID158, A. Knue ID50, D. Kobylianskii ID172, S.F. Koch ID129, M. Kocian ID146, P. Kodyš ID136, D.M. Koeck ID126, P.T. Koenig ID25, T. Koffas ID35, O. Kolay ID51, I. Koletsou ID4, T. Komarek ID88, K. Köneke ID55, A.X.Y. Kong ID1, T. Kono ID121, N. Konstantinidis ID98, P. Kontaxakis ID57, B. Konya ID100, R. Kopeliansky ID42, S. Koperny ID87a, K. Korcyl ID88, K. Kordas ID155,d, A. Korn ID98, S. Korn ID56, I. Korolkov ID13, N. Korotkova ID38, B. Kortman ID117, O. Kortner ID112, S. Kortner ID112, W.H. Kostecka ID118, V.V. Kostyukhin ID144, A. Kotsokechagia ID37, A. Kotwal ID52, A. Koulouris ID37, A. Kourkoumeli-Charalampidi ID74a,74b, C. Kourkoumelis ID9, E. Kourlitis ID112,ab, O. Kovanda ID126, R. Kowalewski ID168, W. Kozanecki ID138, A.S. Kozhin ID38, V.A. Kramarenko ID38, G. Kramberger ID95, P. Kramer ID102, M.W. Krasny ID130, A. Krasznahorkay ID37, A.C. Kraus ID118, J.W. Kraus ID174, J.A. Kremer ID49, T. Kresse ID51, L. Kretschmann ID174, J. Kretzschmar ID94, K. Kreul ID19, P. Krieger ID158, M. Krivos ID136, K. Krizka ID21, K. Kroeninger ID50, H. Kroha ID112, J. Kroll ID134, J. Kroll ID131, K.S. Krowpman ID109, U. Kruchonak ID39, H. Krüger ID25, N. Krumnack82, M.C. Kruse ID52, O. Kuchinskaia ID38, S. Kuday ID3a, S. Kuehn ID37, R. Kuesters ID55, T. Kuhl ID49, V. Kukhtin ID39, Y. Kulchitsky ID38a, S. Kuleshov ID140d,140b, M. Kumar ID34g, N. Kumari ID49, P. Kumari ID159b, A. Kupco ID134, T. Kupfer50, A. Kupich ID38, O. Kuprash ID55, H. Kurashige ID86, L.L. Kurchaninov ID159a, O. Kurdysh ID67, Y.A. Kurochkin ID38, A. Kurova ID38, M. Kuze ID157, A.K. Kvam ID105, J. Kvita ID125, T. Kwan ID106, N.G. Kyriacou ID108, L.A.O. Laatu ID104, C. Lacasta ID166, F. Lacava ID76a,76b, H. Lacker ID19, D. Lacour ID130, N.N. Lad ID98, E. Ladygin ID39, A. Lafarge ID41, B. Laforge ID130, T. Lagouri ID175, F.Z. Lahbabi ID36a, S. Lai ID56, J.E. Lambert ID168, S. Lammers ID69, W. Lampl ID7, C. Lampoudis ID155,d, G. Lamprinoudis102, A.N. Lancaster ID118, E. Lançon ID30, U. Landgraf ID55, M.P.J. Landon ID96, V.S. Lang ID55, O.K.B. Langrekken ID128, A.J. Lankford ID162, F. Lanni ID37, K. Lantzsch ID25, A. Lanza ID74a, J.F. Laporte ID138, T. Lari ID72a, F. Lasagni Manghi ID24b, M. Lassnig ID37, V. Latonova ID134, A. Laurier ID153, S.D. Lawlor ID142, Z. Lawrence ID103, R. Lazaridou170, M. Lazzaroni ID72a,72b, B. Le103, E.M. Le Boulicaut ID52, L.T. Le Pottier ID18a, B. Lebanese ID24b,24a, A. Lebedev ID82, M. LeBlanc ID103, F. Ledroit-Guillon ID61, S.C. Lee ID151, S. Lee ID48a,48b, T.F. Lee ID94, L.L. Leeuw ID34c, H.P. Lefebvre ID97, M. Lefebvre ID168, C. Leggett ID18a, G. Lehmann Miotto ID37, M. Leigh ID57, W.A. Leight ID105, W. Leinonen ID116, A. Leisos ID155,s, M.A.L. Leite ID84c, C.E. Leitgeb ID19, R. Leitner ID136, K.J.C. Leney ID45, T. Lenz ID25, S. Leone ID75a, C. Leonidopoulos ID53, A. Leopold ID147, R. Les ID109, C.G. Lester ID33, M. Levchenko ID38, J. Levêque ID4, L.J. Levinson ID172, G. Levrini ID24b,24a, M.P. Lewicki ID88, C. Lewis ID141, D.J. Lewis ID4, A. Li ID5, B. Li ID63b, C. Li63a, C-Q. Li ID112, H. Li ID63a, H. Li ID63b, H. Li ID114a, H. Li ID15, H. Li ID63b, J. Li ID63c, K. Li ID141, L. Li ID63c, M. Li ID14,114c, S. Li ID14,114c, S. Li ID63d,63c, T. Li ID5, X. Li ID106, Z. Li ID129, Z. Li ID156, Z. Li ID14,114c, Z. Li ID63a, S. Liang ID14,114c, Z. Liang ID14, M. Liberatore ID138, B. Liberti ID77a, K. Lie ID65c, J. Lieber Marin ID84c, H. Lien ID69, H. Lin ID108, K. Lin ID109, R.E. Lindley ID7, J.H. Lindon ID2, J. Ling ID62, E. Lipeles ID131, A. Lipniacka ID17, A. Lister ID167, J.D. Little ID69, B. Liu ID14, B.X. Liu ID114b, D. Liu ID63d,63c, E.H.L. Liu ID21, J.B. Liu ID63a, J.K.K. Liu ID33, K. Liu ID63d, K. Liu ID63d,63c, M. Liu ID63a, M.Y. Liu ID63a,
P. Liu <sup>14</sup>, Q. Liu <sup>63d,141,63c</sup>, X. Liu <sup>63a</sup>, X. Liu <sup>63b</sup>, Y. Liu <sup>114b,114c</sup>, Y.L. Liu <sup>63b</sup>, Y.W. Liu <sup>63a</sup>, S.L. Lloyd <sup>96</sup>, E.M. Lobodzinska <sup>49</sup>, P. Loch <sup>7</sup>, T. Lohse <sup>19</sup>, K. Lohwasser <sup>142</sup>, E. Loiacono <sup>49</sup>, M. Lokajicek <sup>134,*</sup>, J.D. Lomas <sup>21</sup>, J.D. Long <sup>165</sup>, I. Longarini <sup>162</sup>, R. Longo <sup>165</sup>, I. Lopez Paz <sup>68</sup>, A. Lopez Solis <sup>49</sup>, N.A. Lopez-canelas <sup>7</sup>, N. Lorenzo Martinez <sup>4</sup>, A.M. Lory <sup>111</sup>, M. Losada <sup>119a</sup>, G. Löschcke Centeno <sup>149</sup>, O. Loseva <sup>38</sup>, X. Lou <sup>48a,48b</sup>, X. Lou <sup>14,114c</sup>, A. Lounis <sup>67</sup>, P.A. Love <sup>93</sup>, G. Lu <sup>14,114c</sup>, M. Lu <sup>67</sup>, S. Lu <sup>131</sup>, Y.J. Lu <sup>66</sup>, H.J. Lubatti <sup>141</sup>, C. Luci <sup>76a,76b</sup>, F.L. Lucio Alves <sup>114a</sup>, F. Luehring <sup>69</sup>, I. Luise <sup>148</sup>, O. Lukianchuk <sup>67</sup>, O. Lundberg <sup>147</sup>, B. Lund-Jensen <sup>147,*</sup>, N.A. Luongo <sup>6</sup>, M.S. Lutz <sup>37</sup>, A.B. Lux <sup>26</sup>, D. Lynn <sup>30</sup>, R. Lysak <sup>134</sup>, E. Lytken <sup>100</sup>, V. Lyubushkin <sup>39</sup>, T. Lyubushkina <sup>39</sup>, M.M. Lyukova <sup>148</sup>, M. Firdaus M. Soberi <sup>53</sup>, H. Ma <sup>30</sup>, K. Ma <sup>63a</sup>, L.L. Ma <sup>63b</sup>, W. Ma <sup>63a</sup>, Y. Ma <sup>124</sup>, J.C. MacDonald <sup>102</sup>, P.C. Machado De Abreu Farias <sup>84c</sup>, R. Madar <sup>41</sup>, T. Madula <sup>98</sup>, J. Maeda <sup>86</sup>, T. Maeno <sup>30</sup>, H. Maguire <sup>142</sup>, V. Maiboroda <sup>138</sup>, A. Maio <sup>133a,133b,133d</sup>, K. Maj <sup>87a</sup>, O. Majersky <sup>49</sup>, S. Majewski <sup>126</sup>, N. Makovec <sup>67</sup>, V. Maksimovic <sup>16</sup>, B. Malaescu <sup>130</sup>, Pa. Malecki <sup>88</sup>, V.P. Maleev <sup>38</sup>, F. Malek <sup>61,n</sup>, M. Mali <sup>95</sup>, D. Malito <sup>97</sup>, U. Mallik <sup>81</sup>, S. Maltezos <sup>10</sup>, S. Malyukov <sup>39</sup>, J. Mamuzic <sup>13</sup>, G. Mancini <sup>54</sup>, M.N. Mancini <sup>27</sup>, G. Manco <sup>74a,74b</sup>, J.P. Mandalia <sup>96</sup>, S.S. Mandarry <sup>149</sup>, I. Mandić <sup>95</sup>, L. Manhaes de Andrade Filho <sup>84a</sup>, I.M. Maniatis <sup>172</sup>, J. Manjarres Ramos <sup>91</sup>, D.C. Mankad <sup>172</sup>, A. Mann <sup>111</sup>, S. Manzoni <sup>37</sup>, L. Mao <sup>63c</sup>, X. Mapekula <sup>34c</sup>, A. Marantis <sup>155,s</sup>, G. Marchiori <sup>5</sup>, M. Marcisovsky <sup>134</sup>, C. Marcon <sup>72a</sup>, M. Marinescu <sup>21</sup>, S. Marium <sup>49</sup>, M. Marjanovic <sup>123</sup>, A. Markhoos <sup>55</sup>, M. Markovitch <sup>67</sup>, E.J. Marshall <sup>93</sup>, Z. Marshall <sup>18a</sup>, S. Marti-Garcia <sup>166</sup>, J. Martin <sup>98</sup>, T.A. Martin <sup>137</sup>, V.J. Martin <sup>53</sup>, B. Martin dit Latour <sup>17</sup>, L. Martinelli <sup>76a,76b</sup>, M. Martinez <sup>13,t</sup>, P. Martinez Agullo <sup>166</sup>, V.I. Martinez Outschoorn <sup>105</sup>, P. Martinez Suarez <sup>13</sup>, S. Martin-Haugh <sup>137</sup>, G. Martinovicova <sup>136</sup>, V.S. Martoiu <sup>28b</sup>, A.C. Martyniuk <sup>98</sup>, A. Marzin <sup>37</sup>, D. Mascione <sup>79a,79b</sup>, L. Masetti <sup>102</sup>, J. Masik <sup>103</sup>, A.L. Maslennikov <sup>38</sup>, P. Massarotti <sup>73a,73b</sup>, P. Mastrandrea <sup>75a,75b</sup>, A. Mastroberardino <sup>44b,44a</sup>, T. Masubuchi <sup>127</sup>, T. Mathisen <sup>164</sup>, J. Matousek <sup>136</sup>, J. Maurer <sup>28b</sup>, A.J. Maury <sup>67</sup>, B. Maček <sup>95</sup>, D.A. Maximov <sup>38</sup>, A.E. May <sup>103</sup>, R. Mazini <sup>151</sup>, I. Maznas <sup>118</sup>, M. Mazza <sup>109</sup>, S.M. Mazza <sup>139</sup>, E. Mazzeo <sup>72a,72b</sup>, C. Mc Ginn <sup>30</sup>, J.P. Mc Gowan <sup>168</sup>, S.P. Mc Kee <sup>108</sup>, C.C. McCracken <sup>167</sup>, E.F. McDonald <sup>107</sup>, A.E. McDougall <sup>117</sup>, J.A. Mcfayden <sup>149</sup>, R.P. McGovern <sup>131</sup>, R.P. Mckenzie <sup>34g</sup>, T.C. Mclachlan <sup>49</sup>, D.J. Mclaughlin <sup>98</sup>, S.J. McMahon <sup>137</sup>, C.M. Mcpartland <sup>94</sup>, R.A. McPherson <sup>168,x</sup>, S. Mehlhase <sup>111</sup>, A. Mehta <sup>94</sup>, D. Melini <sup>166</sup>, B.R. Mellado Garcia <sup>34g</sup>, A.H. Melo <sup>56</sup>, F. Meloni <sup>49</sup>, A.M. Mendes Jacques Da Costa <sup>103</sup>, H.Y. Meng <sup>158</sup>, L. Meng <sup>93</sup>, S. Menke <sup>112</sup>, M. Mentink <sup>37</sup>, E. Meoni <sup>44b,44a</sup>, G. Mercado <sup>118</sup>, S. Merianos <sup>155</sup>, C. Merlassino <sup>70a,70c</sup>, L. Merola <sup>73a,73b</sup>, C. Meroni <sup>72a,72b</sup>, J. Metcalfe <sup>6</sup>, A.S. Mete <sup>6</sup>, E. Meuser <sup>102</sup>, C. Meyer <sup>69</sup>, J-P. Meyer <sup>138</sup>, R.P. Middleton <sup>137</sup>, L. Mijović <sup>53</sup>, G. Mikenberg <sup>172</sup>, M. Mikestikova <sup>134</sup>, M. Mikuž <sup>95</sup>, H. Mildner <sup>102</sup>, A. Milic <sup>37</sup>, D.W. Miller <sup>40</sup>, E.H. Miller <sup>146</sup>, L.S. Miller <sup>35</sup>, A. Milov <sup>172</sup>, D.A. Milstead <sup>48a,48b</sup>, T. Min <sup>114a</sup>, A.A. Minaenko <sup>38</sup>, I.A. Minashvili <sup>152b</sup>, L. Mince <sup>60</sup>, A.I. Mincer <sup>120</sup>, B. Mindur <sup>87a</sup>, M. Mineev <sup>39</sup>, Y. Mino <sup>89</sup>, L.M. Mir <sup>13</sup>, M. Miralles Lopez <sup>60</sup>, M. Mironova <sup>18a</sup>, M.C. Missio <sup>116</sup>, A. Mitra <sup>170</sup>, V.A. Mitsou <sup>166</sup>, Y. Mitsumori <sup>113</sup>, O. Miu <sup>158</sup>, P.S. Miyagawa <sup>96</sup>, T. Mkrtchyan <sup>64a</sup>, M. Mlinarevic <sup>98</sup>, T. Mlinarevic <sup>98</sup>, M. Mlynarikova <sup>37</sup>, S. Mobius <sup>20</sup>, P. Mogg <sup>111</sup>, M.H. Mohamed Farook <sup>115</sup>, A.F. Mohammed <sup>14,114c</sup>, S. Mohapatra <sup>42</sup>, G. Mokgatitswane <sup>34g</sup>, L. Moleri <sup>172</sup>, B. Mondal <sup>144</sup>, S. Mondal <sup>135</sup>, K. Mönig <sup>49</sup>, E. Monnier <sup>104</sup>, L. Monsonis Romero <sup>166</sup>, J. Montejo Berlingen <sup>13</sup>, A. Montella <sup>48a,48b</sup>, M. Montella <sup>122</sup>, F. Montereali <sup>78a,78b</sup>, F. Monticelli <sup>92</sup>, S. Monzani <sup>70a,70c</sup>, A. Morancho Tarda <sup>43</sup>, N. Morange <sup>67</sup>, A.L. Moreira De Carvalho <sup>49</sup>, M. Moreno Llácer <sup>166</sup>, C. Moreno Martinez <sup>57</sup>, P. Morettini <sup>58b</sup>, S. Morgenstern <sup>37</sup>, M. Morii <sup>62</sup>, M. Morinaga <sup>156</sup>, F. Morodei <sup>76a,76b</sup>, L. Morvaj <sup>37</sup>, P. Moschovakos <sup>37</sup>, B. Moser <sup>129</sup>, M. Mosidze <sup>152b</sup>, T. Moskalets <sup>45</sup>,
P. Moskvitina ¹¹⁶, J. Moss ³²,⁴, P. Moszkowicz ⁸⁷ª, A. Moussa ³⁶d, E.J.W. Moyse ¹⁰⁵,
O. Mtintsilana ³⁴g, S. Muanza ¹⁰⁴, J. Mueller ¹³², D. Muenstermann ⁹³, R. Müller ³⁷,
G.A. Mullier ¹⁶⁴, A.J. Mullin³³, J.J. Mullin¹³¹, D.P. Mungo ¹⁵⁸, D. Munoz Perez ¹⁶⁶,
F.J. Munoz Sanchez ¹⁰³, M. Murin ¹⁰³, W.J. Murray ¹⁷⁰,¹³⁷, M. Muškinja ⁹⁵, C. Mwewa ³⁰,
A.G. Myagkov ³⁸a, A.J. Myers ⁸, G. Myers ¹⁰⁸, M. Myska ¹³⁵, B.P. Nachman ¹⁸a,
O. Nackenhorst ⁵⁰, K. Nagai ¹²⁹, K. Nagano ⁸⁵, J.L. Nagle ³⁰,af, E. Nagy ¹⁰⁴, A.M. Nairz ³⁷,
Y. Nakahama ⁸⁵, K. Nakamura ⁸⁵, K. Nakkalil ⁵, H. Nanjo ¹²⁷, E.A. Narayanan ¹¹⁵,
I. Naryshkin ³⁸, L. Nasella ⁷²a,⁷²b, M. Naseri ³⁵, S. Nasri ¹¹⁹b, C. Nass ²⁵, G. Navarro ²³a,
J. Navarro-Gonzalez ¹⁶⁶, R. Nayak ¹⁵⁴, A. Nayaz ¹⁹, P.Y. Nechaeva ³⁸, S. Nechaeva ²⁴b,²⁴a,
F. Nechansky ⁴⁹, L. Nedic ¹²⁹, T.J. Neep ²¹, A. Negri ⁷⁴a,⁷⁴b, M. Negrini ²⁴b, C. Nellist ¹¹⁷,
C. Nelson ¹⁰⁶, K. Nelson ¹⁰⁸, S. Nemecek ¹³⁴, M. Nessi ³⁷,h, M.S. Neubauer ¹⁶⁵, F. Neuhaus ¹⁰²,
J. Neundorf ⁴⁹, P.R. Newman ²¹, C.W. Ng ¹³², Y.W.Y. Ng ⁴⁹, B. Ngair ¹¹⁹ª, H.D.N. Nguyen ¹¹⁰,
R.B. Nickerson ¹²⁹, R. Nicolaidou ¹³⁸, J. Nielsen ¹³⁹, M. Niemeyer ⁵⁶, J. Niermann ⁵⁶,
N. Nikiforou ³⁷, V. Nikolaenko ³⁸a, I. Nikolic-Audit ¹³⁰, K. Nikolopoulos ²¹, P. Nilsson ³⁰,
I. Ninca ⁴⁹, G. Ninio ¹⁵⁴, A. Nisati ⁷⁶a, N. Nishu ², R. Nisius ¹¹², J-E. Nitschke ⁵¹,
E.K. Nkadimeng ³⁴g, T. Nobe ¹⁵⁶, T. Nommensen ¹⁵⁰, M.B. Norfolk ¹⁴², B.J. Norman ³⁵,
M. Noury ³⁶a, J. Novak ⁹⁵, T. Novak ⁹⁵, L. Novotny ¹³⁵, R. Novotny ¹¹⁵, L. Nozka ¹²⁵,
K. Ntekas ¹⁶², N.M.J. Nunes De Moura Junior ⁸⁴b, J. Ocariz ¹³⁰, A. Ochi ⁸⁶, I. Ochoa ¹³³a,
S. Oerdeک ⁴⁹,u, J.T. Offermann ⁴⁰, A. Ogrodnik ¹³⁶, A. Oh ¹⁰³, C.C. Ohm ¹⁴⁷, H. Oide ⁸⁵,
R. Oishi ¹⁵⁶, M.L. Ojeda ⁴⁹, Y. Okumura ¹⁵⁶, L.F. Oleiro Seabra ¹³³a, I. Oleksiyuk ⁵⁷,
S.A. Olivares Pino ¹⁴⁰d, G. Oliveira Correa ¹³, D. Oliveira Damazio ³⁰, J.L. Oliver ¹⁶²,
Ö.O. Öncel ⁵⁵, A.P. O'Neill ²⁰, A. Onofre ¹³³a,¹³³e, P.U.E. Onyisi ¹¹, M.J. Oreglia ⁴⁰,
G.E. Orellana ⁹², D. Orestano ⁷⁸a,⁷⁸b, N. Orlando ¹³, R.S. Orr ¹⁵⁸, L.M. Osojnak ¹³¹,
R. Ospanov ⁶³a, G. Otero y Garzon ³¹, H. Otono ⁹⁰, P.S. Ott ⁶⁴a, G.J. Ottino ¹⁸a, M. Ouchrif ³⁶d,
F. Ould-Saada ¹²⁸, T. Ovsiannikova ¹⁴¹, M. Owen ⁶⁰, R.E. Owen ¹³⁷, V.E. Ozcan ²²a,
F. Ozturk ⁸⁸, N. Ozturk ⁸, S. Ozturk ⁸³, H.A. Pacey ¹²⁹, A. Pacheco Pages ¹³,
C. Padilla Aranda ¹³, G. Padovano ⁷⁶a,⁷⁶b, S. Pagan Griso ¹⁸a, G. Palacino ⁶⁹, A. Palazzo ⁷¹a,⁷¹b,
J. Pampel ²⁵, J. Pan ¹⁷⁵, T. Pan ⁶⁵a, D.K. Panchal ¹¹, C.E. Pandini ¹¹⁷, J.G. Panduro Vazquez ¹³⁷,
H.D. Pandya ¹, H. Pang ¹⁵, P. Pani ⁴⁹, G. Panizzo ⁷⁰a,⁷⁰c, L. Panwar ¹³⁰, L. Paolozzi ⁵⁷,
S. Parajuli ¹⁶⁵, A. Paramonov ⁶, C. Paraskevopoulos ⁵⁴, D. Paredes Hernandez ⁶⁵b,
A. Pareti ⁷⁴a,⁷⁴b, K.R. Park ⁴², T.H. Park ¹⁵⁸, M.A. Parker ³³, F. Parodi ⁵⁸b,⁵⁸a, E.W. Parrish ¹¹⁸,
V.A. Parrish ⁵³, J.A. Parsons ⁴², U. Parzefall ⁵⁵, B. Pascual Dias ¹¹⁰, L. Pascual Dominguez ¹⁰¹,
E. Pasqualucci ⁷⁶a, S. Passaggio ⁵⁸b, F. Pastore ⁹⁷, P. Patel ⁸⁸, U.M. Patel ⁵², J.R. Pater ¹⁰³,
T. Pauly ³⁷, C.I. Pazos ¹⁶¹, J. Pearkes ¹⁴⁶, M. Pedersen ¹²⁸, R. Pedro ¹³³a, S.V. Peleganchuk ³⁸,
O. Penc ³⁷, E.A. Pender ⁵³, S. Peng ¹⁵, G.D. Penn ¹⁷⁵, K.E. Penski ¹¹¹, M. Penzin ³⁸,
B.S. Peralva ⁸⁴d, A.P. Pereira Peixoto ¹⁴¹, L. Pereira Sanchez ¹⁴⁶, D.V. Perepelitsa ³⁰,af,
G. Perera ¹⁰⁵, E. Perez Codina ¹⁵⁹a, M. Perganti ¹⁰, H. Pernegger ³⁷, S. Perrella ⁷⁶a,⁷⁶b,
O. Perrin ⁴¹, K. Peters ⁴⁹, R.F.Y. Peters ¹⁰³, B.A. Petersen ³⁷, T.C. Petersen ⁴³, E. Petit ¹⁰⁴,
V. Petousis ¹³⁵, C. Petridou ¹⁵⁵,d, T. Petru ¹³⁶, A. Petrukhin ¹⁴⁴, M. Pettee ¹⁸a, A. Petukhov ³⁸,
K. Petukhova ³⁷, R. Pezoa ¹⁴⁰f, L. Pezzotti ³⁷, G. Pezzullo ¹⁷⁵, T.M. Pham ¹⁷³, T. Pham ¹⁰⁷,
P.W. Phillips ¹³⁷, G. Piacquadio ¹⁴⁸, E. Pianori ¹⁸a, F. Piazza ¹²⁶, R. Piegaia ³¹, D. Pietreanu ²⁸b,
A.D. Pilkington ¹⁰³, M. Pinamonti ⁷⁰a,⁷⁰c, J.L. Pinfold ², B.C. Pinheiro Pereira ¹³³a,
A.E. Pinto Pinoargote ¹³⁸,¹³⁸, L. Pintucci ⁷⁰a,⁷⁰c, K.M. Piper ¹⁴⁹, A. Pirttikoski ⁵⁷, D.A. Pizzi ³⁵,
L. Pizzimento ⁶⁵b, A. Pizzini ¹¹⁷, M.-A. Pleier ³⁰, V. Pleskot ¹³⁶, E. Plotnikova³⁹, G. Poddar ⁹⁶,
R. Poettgen ¹⁰⁰, L. Poggioli ¹³⁰, I. Pokharel ⁵⁶, S. Polacek ¹³⁶, G. Polesello ⁷⁴a,
A. Poley ¹⁴⁵,¹⁵⁹a, A. Polini ²⁴b, C.S. Pollard ¹⁷⁰, Z.B. Pollock ¹²², E. Pompa Pacchi ⁷⁶a,⁷⁶b,
N.I. Pond ⁹⁸, D. Ponomarenko ¹¹⁶, L. Pontecorvo ³⁷, S. Popa ²⁸a, G.A. Popeneciu ²⁸d,
A. Poreba ID37, D.M. Portillo Quintero ID159a, S. Pospisil ID135, M.A. Postill ID142, P. Postolache ID28c, K. Potamianos ID170, P.A. Potepa ID87a, I.N. Potrap ID39, C.J. Potter ID33, H. Potti ID150, J. Poveda ID166, M.E. Pozo Astigarraga ID37, A. Prades Ibanez ID77a,77b, J. Pretel ID168, D. Price ID103, M. Primavera ID71a, L. Primomo ID70a,70c, M.A. Principe Martin ID101, R. Privara ID125, T. Procter ID60, M.L. Proffitt ID141, N. Proklova ID131, K. Prokofiev ID65c, G. Proto ID112, J. Proudfoot ID6, M. Przybycien ID87a, W.W. Przygoda ID87b, A. Psallidas ID47, J.E. Puddefoot ID142, D. Pudzha ID55, D. Pyatiizbyantseva ID38, J. Qian ID108, D. Qichen ID103, Y. Qin ID13, T. Qiu ID53, A. Quadt ID56, M. Queitsch-Maitland ID103, G. Quetant ID57, R.P. Quinn ID167, G. Rabanal Bolanos ID62, D. Rafanoharana ID55, F. Raffaeli ID77a,77b, F. Ragusa ID72a,72b, J.L. Rainbolt ID40, J.A. Raine ID57, S. Rajagopalan ID30, E. Ramakoti ID38, L. Rambelli ID58b,58a, I.A. Ramirez-Berend ID35, K. Ran ID49,114c, D.S. Rankin ID131, N.P. Rapheeha ID34g, H. Rasheed ID28b, V. Raskina ID130, D.F. Rassloff ID64a, A. Rastogi ID18a, S. Rave ID102, S. Ravera ID58b,58a, B. Ravina ID56, I. Ravinovich ID172, M. Raymond ID37, A.L. Read ID128, N.P. Readioff ID142, D.M. Rebuzzi ID74a,74b, G. Redlinger ID30, A.S. Reed ID112, K. Reeves ID27, J.A. Reidelsturz ID174, D. Reikher ID126, A. Rej ID50, C. Rembser ID37, M. Renda ID28b, F. Renner ID49, A.G. Rennie ID162, A.L. Rescia ID49, S. Resconi ID72a, M. Ressegotti ID58b,58a, S. Rettie ID37, J.G. Reyes Rivera ID109, E. Reynolds ID18a, O.L. Rezanova ID38, P. Reznicek ID136, H. Riani ID36d, N. Ribaric ID93, E. Ricci ID79a,79b, R. Richter ID112, S. Richter ID48a,48b, E. Richter-Was ID87b, M. Ridel ID130, S. Ridouani ID36d, P. Rieck ID120, P. Riedler ID37, E.M. Riefel ID48a,48b, J.O. Rieger ID117, M. Rijssenbeek ID148, M. Rimoldi ID37, L. Rinaldi ID24b,24a, P. Rincke ID56,164, T.T. Rinn ID30, M.P. Rinnagel ID111, G. Ripellino ID164, I. Riu ID13, J.C. Rivera Vergara ID168, F. Rizatdinova ID124, E. Rizvi ID96, B.R. Roberts ID18a, S.S. Roberts ID139, S.H. Robertson ID106,x, D. Robinson ID33, M. Robles Manzano ID102, A. Robson ID60, A. Rocchi ID77a,77b, C. Roda ID75a,75b, S. Rodriguez Bosca ID37, Y. Rodriguez Garcia ID23a, A. Rodriguez Rodriguez ID55, A.M. Rodríguez Vera ID118, S. Roe ID37, J.T. Roemer ID37, A.R. Roepe-Gier ID139, O. Röhne ID128, R.A. Rojas ID105, C.P.A. Roland ID130, J. Roloff ID30, A. Romaniouk ID38, E. Romano ID74a,74b, M. Romano ID24b, A.C. Romero Hernandez ID165, N. Rompotis ID94, L. Roos ID130, S. Rosati ID76a, B.J. Rosser ID40, E. Rossi ID129, E. Rossi ID73a,73b, L.P. Rossi ID62, L. Rossini ID55, R. Rosten ID122, M. Rotaru ID28b, B. Rottler ID55, C. Rougier ID91, D. Rousseau ID67, D. Rousso ID49, A. Roy ID165, S. Roy-Garand ID158, A. Rozanov ID104, Z.M.A. Rozario ID60, Y. Rozen ID153, A. Rubio Jimenez ID166, A.J. Ruby ID94, V.H. Ruelas Rivera ID19, T.A. Ruggeri ID1, A. Ruggiero ID129, A. Ruiz-Martinez ID166, A. Rummler ID37, Z. Rurikova ID55, N.A. Rusakovich ID39, H.L. Russell ID168, G. Russo ID76a,76b, J.P. Rutherford ID7, S. Rutherford Colmenares ID33, M. Rybar ID136, E.B. Rye ID128, A. Ryzhov ID45, J.A. Sabater Iglesias ID57, H.F-W. Sadrozinski ID139, F. Safai Tehrani ID76a, B. Safarzadeh Samani ID137, S. Saha ID1, M. Sahinsoy ID83, A. Saibel ID166, M. Saimpert ID138, M. Saito ID156, T. Saito ID156, A. Sala ID72a,72b, D. Salamani ID37, A. Salnikov ID146, J. Salt ID166, A. Salvador Salas ID154, D. Salvatore ID44b,44a, F. Salvatore ID149, A. Salzburger ID37, D. Sammel ID55, E. Sampson ID93, D. Sampsonidis ID155,d, D. Sampsonidou ID126, J. Sánchez ID166, V. Sanchez Sebastian ID166, H. Sandaker ID128, C.O. Sander ID49, J.A. Sandesara ID105, M. Sandhoff ID174, C. Sandoval ID23b, L. Sanfilippo ID64a, D.P.C. Sankey ID137, T. Sano ID89, A. Sansoni ID54, L. Santi ID37,76b, C. Santoni ID41, H. Santos ID133a,133b, A. Santra ID172, E. Sanzani ID24b,24a, K.A. Saoucha ID163, J.G. Saraiva ID133a,133d, J. Sardain ID7, O. Sasaki ID85, K. Sato ID160, C. Sauer ID64b, E. Sauvan ID4, P. Savard ID158,ad, R. Sawada ID156, C. Sawyer ID137, L. Sawyer ID99, C. Sbarra ID24b, A. Sbrizzi ID24b,24a, T. Scanlon ID98, J. Schaarschmidt ID141, U. Schäfer ID102, A.C. Schaffer ID67,45, D. Schaile ID111, R.D. Schamberger ID148, C. Scharf ID19, M.M. Schefer ID20, V.A. Schegelsky ID38, D. Scheirich ID136, M. Schernau ID162, C. Scheulen ID56, C. Schiavi ID58b,58a, M. Schioppa ID44b,44a, B. Schlag ID146,m, K.E. Schleicher ID55, S. Schlenker ID37, J. Schmeing ID174, M.A. Schmidt ID174, K. Schmieden ID102, C. Schmitt ID102, N. Schmitt ID102, S. Schmitt ID49, L. Schoeffel ID138, A. Schoening ID64b, P.G. Scholer ID35, E. Schopf ID129, M. Schott ID25, J. Schovancova ID37, S. Schramm ID57, T. Schroer ID57, H-C. Schultz-Coulon ID64a,
M. Schumacher (155), B.A. Schumm (139), Ph. Schune (138), A.J. Schuy (141), H.R. Schwartz (139), A. Schwartzman (146), T.A. Schwarz (108), Ph. Schwemling (138), R. Schwienhorst (109), F.G. Sciacca (20), A. Sciandra (30), G. Sciolla (27), F. Scuri (75a), C.D. Sebastiani (94), K. Sedlaczek (118), S.C. Seidel (115), A. Seiden (139), B.D. Seidlitz (42), C. Seitz (49), J.M. Seixas (84b), G. Sekhniaidze (73a), L. Selem (61), N. Semprini-Cesari (24b,24a), D. Sengupta (57), V. Senthilkumar (166), L. Serin (67), M. Sessa (77a,77b), H. Severini (123), F. Sforza (58b,58a), A. Sfyrla (57), Q. Sha (14), E. Shabalina (56), A.H. Shah (33), R. Shaheen (147), J.D. Shahinian (131), D. Shaked Renous (172), L.Y. Shan (14), M. Shapiro (18a), A. Sharma (37), A.S. Sharma (167), P. Sharma (81), P.B. Shatalov (38), K. Shaw (149), S.M. Shaw (103), Q. Shen (63c), D.J. Sheppard (145), P. Sherwood (98), L. Shi (98), X. Shi (14), S. Shimizu (85), C.O. Shimmin (175), J.D. Shinner (97), I.P.J. Shipsey (129), S. Shirabe (90), M. Shiyakova (39,v), M.J. Shochet (40), D.R. Shope (128), B. Shrestha (123), S. Shrestha (122,ag), M.J. Shroff (168), P. Sicho (134), A.M. Sickles (165), E. Sideras Haddad (34g), A.C. Sidley (117), A. Sidoti (24b), F. Siegert (51), Dj. Sijacki (16), F. Sili (92), J.M. Silva (53), I. Silva Ferreira (84b), M.V. Silva Oliveira (30), S.B. Silverstein (48a), S. Simion (67), R. Simoniello (37), E.L. Simpson (103), H. Simpson (149), L.R. Simpson (108), N.D. Simpson (100), S. Simsek (83), S. Sindhu (56), P. Sinervo (158), S. Singh (158), S. Sinha (49), S. Sinha (103), M. Sioli (24b,24a), I. Siral (37), E. Sitnikova (49), J. Sjölin (48a,48b), A. Skaf (56), E. Skorda (21), P. Skubic (123), M. Slawinska (88), V. Smakhtin (172), B.H. Smart (137), S.Yu. Smirnov (38), Y. Smirnov (38), L.N. Smirnova (38a), O. Smirnova (100), A.C. Smith (42), D.R. Smith (162), E.A. Smith (40), H.A. Smith (129), J.L. Smith (103), R. Smith (146), M. Smizanska (93), K. Smolek (135), A.A. Snesarev (38), S.R. Snider (158), H.L. Snoek (117), S. Snyder (30), R. Sobie (168,x), A. Soffer (154), C.A. Solans Sanchez (37), E.Yu. Soldatov (38), U. Soldevila (166), A.A. Solodkov (38), S. Solomon (27), A. Soloshenko (39), K. Solovieva (55), O.V. Solovyanov (41), P. Sommer (51), A. Sonay (13), W.Y. Song (159b), A. Sopczak (135), A.L. Sopio (98), F. Sopkova (29b), J.D. Sorenson (115), I.R. Sotarriva Alvarez (157), V. Sothilingam (64a), O.J. Soto Sandoval (140c,140b), S. Sottocornola (69), R. Soualah (163), Z. Soumaimi (36e), D. South (49), N. Soybelman (172), S. Spagnolo (71a,71b), M. Spalla (112), D. Sperlich (55), G. Spigo (37), B. Spisso (73a,73b), D.P. Spiteri (60), M. Spousta (136), E.J. Staats (35), R. Stamen (64a), A. Stampekis (21), M. Standke (25), E. Stanecka (88), W. Stanek-Maslouska (49), M.V. Stange (51), B. Stanislaus (18a), M.M. Stanitzki (49), B. Stapf (49), E.A. Starchenko (38), G.H. Stark (139), J. Stark (91), P. Staroba (134), P. Starovoitov (64a), S. Stärz (106), R. Staszewski (88), G. Stavropoulos (47), P. Steinberg (30), B. Stelzer (145,159a), H.J. Stelzer (132), O. Stelzer-Chilton (159a), H. Stenzel (59), T.J. Stevenson (149), G.A. Stewart (37), J.R. Stewart (124), M.C. Stockton (37), G. Stoicea (28b), M. Stolarski (133a), S. Stonjek (112), A. Straessner (51), J. Strandberg (147), S. Strandberg (48a,48b), M. Stratmann (174), M. Strauss (123), T. Strebler (104), P. Strizenec (29b), R. Ströhmer (169), D.M. Strom (126), R. Stroynowski (45), A. Strubig (48a,48b), S.A. Stucci (30), B. Stugu (17), J. Stupak (123), N.A. Styles (49), D. Su (146), S. Su (63a), W. Su (63d), X. Su (63a), D. Suchy (29a), K. Sugizaki (156), V.V. Sulin (38), M.J. Sullivan (94), D.M.S. Sultan (129), L. Sultanaliyeva (38), S. Sultansoy (3b), T. Sumida (89), S. Sun (173), O. Sunneborn Gudnadottir (164), N. Sur (104), M.R. Sutton (149), H. Suzuki (160), M. Svatos (134), M. Swiatlowski (159a), T. Swirski (169), I. Sykora (29a), M. Sykora (136), T. Sykora (136), D. Ta (102), K. Tackmann (49,u), A. Taffard (162), R. Tafirout (159a), J.S. Tafoya Vargas (67), Y. Takubo (85), M. Talby (104), A.A. Talyshev (38), K.C. Tam (65b), N.M. Tamir (154), A. Tanaka (156), J. Tanaka (156), R. Tanaka (67), M. Tanasini (148), Z. Tao (167), S. Tapia Araya (140f), S. Tapprogge (102), A. Tarek Abouelfadl Mohamed (109), S. Tarem (153), K. Tariq (14), G. Tarna (28b), G.F. Tartarelli (72a), M.J. Tartarin (91), P. Tas (136), M. Tasevsky (134), E. Tassi (44b,44a), A.C. Tate (165), G. Tateno (156), Y. Tayalati (36e,w), G.N. Taylor (107), W. Taylor (159b), R. Teixeira De Lima (146), P. Teixeira-Dias (97), J.J. Teoh (158), K. Terashi (156), J. Terron (101), S. Terzo (13), M. Testa (54), R.J. Teuscher (158,x), A. Thaler (80), O. Theiner (57), N. Themistokleous (53), T. Theveneaux-Pelzer (104), O. Thielmann (174),
D.W. Thomas<sup>97</sup>, J.P. Thomas<sup>21</sup>, E.A. Thompson<sup>18a</sup>, P.D. Thompson<sup>21</sup>, E. Thomson<sup>131</sup>, R.E. Thornberry<sup>45</sup>, C. Tian<sup>63a</sup>, Y. Tian<sup>56</sup>, V. Tikhomirov<sup>38a</sup>, Yu.A. Tikhonov<sup>38</sup>, S. Timoshenko<sup>38</sup>, D. Timoshyn<sup>136</sup>, E.X.L. Ting<sup>1</sup>, P. Tipton<sup>175</sup>, A. Tishelman-Charny<sup>30</sup>, S.H. Tlou<sup>34g</sup>, K. Todome<sup>157</sup>, S. Todorova-Nova<sup>136</sup>, S. Todt<sup>51</sup>, L. Toffolin<sup>70a,70c</sup>, M. Togawa<sup>85</sup>, J. Tojo<sup>90</sup>, S. Tokár<sup>29a</sup>, K. Tokushuku<sup>85</sup>, O. Toldaiev<sup>69</sup>, M. Tomoto<sup>85,113</sup>, L. Tompkins<sup>146m</sup>, K.W. Topolnicki<sup>87b</sup>, E. Torrence<sup>126</sup>, H. Torres<sup>91</sup>, E. Torró Pastor<sup>166</sup>, M. Toscani<sup>31</sup>, C. Tosciri<sup>40</sup>, M. Tost<sup>11</sup>, D.R. Tovey<sup>142</sup>, I.S. Trandafir<sup>28b</sup>, T. Trefzger<sup>169</sup>, A. Tricoli<sup>30</sup>, I.M. Trigger<sup>159a</sup>, S. Trincaz-Duvoid<sup>130</sup>, D.A. Trischuk<sup>27</sup>, B. Trocmé<sup>61</sup>, A. Tropina<sup>39</sup>, L. Truong<sup>34c</sup>, M. Trzebinski<sup>88</sup>, A. Trzupek<sup>88</sup>, F. Tsai<sup>148</sup>, M. Tsai<sup>108</sup>, A. Tsiamis<sup>155,d</sup>, P.V. Tsiareshka<sup>38</sup>, S. Tsigaridas<sup>159a</sup>, A. Tsirigotis<sup>155,s</sup>, V. Tsiskaridze<sup>158</sup>, E.G. Tskhadadze<sup>152a</sup>, M. Tsopoulou<sup>155</sup>, Y. Tsujikawa<sup>89</sup>, I.I. Tsukerman<sup>38</sup>, V. Tsulaia<sup>18a</sup>, S. Tsuno<sup>85</sup>, K. Tsurı<sup>121</sup>, D. Tsybychev<sup>148</sup>, Y. Tu<sup>65b</sup>, A. Tudorache<sup>28b</sup>, V. Tudorache<sup>28b</sup>, A.N. Tuna<sup>62</sup>, S. Turchikhin<sup>58b,58a</sup>, I. Turk Cakir<sup>3a</sup>, R. Turra<sup>72a</sup>, T. Turtuvshin<sup>39</sup>, P.M. Tuts<sup>42</sup>, S. Tzamarias<sup>155,d</sup>, E. Tzovara<sup>102</sup>, F. Ukegawa<sup>160</sup>, P.A. Ulloa Poblete<sup>140c,140b</sup>, E.N. Umaka<sup>30</sup>, G. Unal<sup>37</sup>, A. Undrus<sup>30</sup>, G. Unel<sup>162</sup>, J. Urban<sup>29b</sup>, P. Urrejola<sup>140a</sup>, G. Usai<sup>8</sup>, R. Ushioda<sup>157</sup>, M. Usman<sup>110</sup>, Z. Uysal<sup>83</sup>, V. Vacek<sup>135</sup>, B. Vachon<sup>106</sup>, T. Vafeiadis<sup>37</sup>, A. Vaitkus<sup>98</sup>, C. Valderanis<sup>111</sup>, E. Valdes Santurio<sup>48a,48b</sup>, M. Valente<sup>159a</sup>, S. Valentinetti<sup>24b,24a</sup>, A. Valero<sup>166</sup>, E. Valiente Moreno<sup>166</sup>, A. Vallier<sup>91</sup>, J.A. Valls Ferrer<sup>166</sup>, D.R. Van Arneman<sup>117</sup>, T.R. Van Daalen<sup>141</sup>, A. Van Der Graaf<sup>50</sup>, P. Van Gemmeren<sup>6</sup>, M. Van Rijnbach<sup>37</sup>, S. Van Stroud<sup>98</sup>, I. Van Vulpen<sup>117</sup>, P. Vana<sup>136</sup>, M. Vanadia<sup>77a,77b</sup>, W. Vandelli<sup>37</sup>, E.R. Vandewall<sup>124</sup>, D. Vannicola<sup>154</sup>, L. Vannoli<sup>54</sup>, R. Vari<sup>76a</sup>, E.W. Varnes<sup>7</sup>, C. Varni<sup>18b</sup>, T. Varol<sup>151</sup>, D. Varouchas<sup>67</sup>, L. Varriale<sup>166</sup>, K.E. Varvell<sup>150</sup>, M.E. Vasile<sup>28b</sup>, L. Vaslin<sup>85</sup>, G.A. Vasquez<sup>168</sup>, A. Vasyukov<sup>39</sup>, L.M. Vaughan<sup>124</sup>, R. Vavricka<sup>102</sup>, T. Vazquez Schroeder<sup>37</sup>, J. Veatch<sup>32</sup>, V. Vecchio<sup>103</sup>, M.J. Veen<sup>105</sup>, I. Veliscek<sup>30</sup>, L.M. Veloce<sup>158</sup>, F. Veloso<sup>133a,133c</sup>, S. Veneziano<sup>76a</sup>, A. Ventura<sup>71a,71b</sup>, S. Ventura Gonzalez<sup>138</sup>, A. Verbytskyi<sup>112</sup>, M. Verducci<sup>75a,75b</sup>, C. Vergis<sup>96</sup>, M. Verissimo De Araujo<sup>84b</sup>, W. Verkerke<sup>117</sup>, J.C. Vermeulen<sup>117</sup>, C. Vernieri<sup>146</sup>, M. Vessella<sup>105</sup>, M.C. Vetterli<sup>145,ad</sup>, A. Vgenopoulos<sup>102</sup>, N. Viaux Maira<sup>140f</sup>, T. Vickey<sup>142</sup>, O.E. Vickey Boeriu<sup>142</sup>, G.H.A. Viehhauser<sup>129</sup>, L. Vigani<sup>64b</sup>, M. Vigl<sup>112</sup>, M. Villa<sup>24b,24a</sup>, M. Villaplana Perez<sup>166</sup>, E.M. Villhauer<sup>53</sup>, E. Vilucchi<sup>54</sup>, M.G. Vincter<sup>35</sup>, A. Visibile<sup>117</sup>, C. Vittori<sup>37</sup>, I. Vivarelli<sup>24b,24a</sup>, E. Voevodina<sup>112</sup>, F. Vogel<sup>111</sup>, J.C. Voigt<sup>51</sup>, P. Vokac<sup>135</sup>, Yu. Volkotrub<sup>87b</sup>, J. Von Ahnen<sup>49</sup>, E. Von Toerne<sup>25</sup>, B. Vormwald<sup>37</sup>, V. Vorobel<sup>136</sup>, K. Vorobev<sup>38</sup>, M. Vos<sup>166</sup>, K. Voss<sup>144</sup>, M. Vozak<sup>117</sup>, L. Vozdecky<sup>123</sup>, N. Vranjes<sup>16</sup>, M. Vranjes Milosavljevic<sup>16</sup>, M. Vreeswijk<sup>117</sup>, N.K. Vu<sup>63d,63c</sup>, R. Vuillermet<sup>37</sup>, O. Vujinovic<sup>102</sup>, I. Vukotic<sup>40</sup>, S. Wada<sup>160</sup>, C. Wagner<sup>105</sup>, J.M. Wagner<sup>18a</sup>, W. Wagner<sup>174</sup>, S. Wahdan<sup>174</sup>, H. Wahlberg<sup>92</sup>, J. Walder<sup>137</sup>, R. Walker<sup>111</sup>, W. Walkowiak<sup>144</sup>, A. Wall<sup>131</sup>, E.J. Wallin<sup>100</sup>, T. Wamorkar<sup>6</sup>, A.Z. Wang<sup>139</sup>, C. Wang<sup>102</sup>, C. Wang<sup>11</sup>, H. Wang<sup>18a</sup>, J. Wang<sup>65c</sup>, P. Wang<sup>98</sup>, R. Wang<sup>62</sup>, R. Wang<sup>6</sup>, S.M. Wang<sup>151</sup>, S. Wang<sup>63b</sup>, S. Wang<sup>14</sup>, T. Wang<sup>63a</sup>, W.T. Wang<sup>81</sup>, W. Wang<sup>14</sup>, X. Wang<sup>114a</sup>, X. Wang<sup>165</sup>, X. Wang<sup>63c</sup>, Y. Wang<sup>63d</sup>, Y. Wang<sup>114a</sup>, Y. Wang<sup>63a</sup>, Z. Wang<sup>108</sup>, Z. Wang<sup>63d,52,63c</sup>, Z. Wang<sup>108</sup>, A. Warburton<sup>106</sup>, R.J. Ward<sup>21</sup>, N. Warrack<sup>60</sup>, S. Waterhouse<sup>97</sup>, A.T. Watson<sup>21</sup>, H. Watson<sup>60</sup>, M.F. Watson<sup>21</sup>, E. Watton<sup>60,137</sup>, G. Watts<sup>141</sup>, B.M. Waugh<sup>98</sup>, J.M. Webb<sup>55</sup>, C. Weber<sup>30</sup>, H.A. Weber<sup>19</sup>, M.S. Weber<sup>20</sup>, S.M. Weber<sup>64a</sup>, C. Wei<sup>63a</sup>, Y. Wei<sup>55</sup>, A.R. Weidberg<sup>129</sup>, E.J. Weik<sup>120</sup>, J. Weingarten<sup>50</sup>, C. Weiser<sup>55</sup>, C.J. Wells<sup>49</sup>, T. Wenaus<sup>30</sup>, B. Wendland<sup>50</sup>, T. Wengler<sup>37</sup>, N.S. Wenke<sup>112</sup>, N. Wermes<sup>25</sup>, M. Wessels<sup>64a</sup>, A.M. Wharton<sup>93</sup>, A.S. White<sup>62</sup>, A. White<sup>8</sup>, M.J. White<sup>1</sup>, D. Whiteson<sup>162</sup>, L. Wickremasinghe<sup>127</sup>, W. Wiedenmann<sup>173</sup>, M. Wielers<sup>137</sup>, C. Wiglesworth<sup>43</sup>, D.J. Wilbern<sup>123</sup>, H.G. Wilkens<sup>37</sup>, J.J.H. Wilkinson<sup>33</sup>, D.M. Williams<sup>42</sup>, H.H. Williams<sup>131</sup>, S. Williams<sup>33</sup>, S. Willocq<sup>105</sup>,
B.J. Wilson (103), P.J. Windischhofer (40), F.I. Winkel (31), F. Winklmeier (126), B.T. Winter (55), J.K. Winter (103), M. Wittgen (146), M. Wobisch (99), T. Wojtkowski (61), Z. Wolffs (117), J. Wollrath (162), M.W. Wolter (88), H. Wolters (133a,133c), M.C. Wong (139), E.L. Woodward (42), S.D. Worm (49), B.K. Wosiek (88), K.W. Woźniak (88), S. Wozniewski (56), K. Wraight (60), C. Wu (21), M. Wu (114b), M. Wu (116), S.L. Wu (173), X. Wu (57), Y. Wu (63a), Z. Wu (4), J. Wuerzinger (112,ab), T.R. Wyatt (103), B.M. Wynne (53), S. Xella (43), L. Xia (114a), M. Xia (15), M. Xie (63a), S. Xin (14,114c), A. Xiong (126), J. Xiong (18a), D. Xu (14), H. Xu (63a), L. Xu (63a), R. Xu (131), T. Xu (108), Y. Xu (15), Z. Xu (53), Z. Xu (114a), B. Yabsley (150), S. Yacoob (34a), Y. Yamaguchi (85), E. Yamashita (156), H. Yamauchi (160), T. Yamazaki (18a), Y. Yamazaki (86), J. Yan (63c), S. Yan (60), Z. Yan (105), H.J. Yang (63c,63d), H.T. Yang (63a), S. Yang (63a), T. Yang (65c), X. Yang (37), X. Yang (14), Y. Yang (45), Y. Yang (63a), Z. Yang (63a), W-M. Yao (18a), H. Ye (114a), H. Ye (56), J. Ye (14), S. Ye (30), X. Ye (63a), Y. Yeh (98), I. Yeletskikh (39), B. Yeo (18b), M.R. Yexley (98), T.P. Yildirim (129), P. Yin (42), K. Yorita (171), S. Younas (28b), C.J.S. Young (37), C. Young (146), C. Yu (14,114c), Y. Yu (63a), J. Yuan (14,114c), M. Yuan (108), R. Yuan (63d,63c), L. Yue (98), M. Zaazoua (63a), B. Zabinski (88), E. Zaid (53), Z.K. Zak (88), T. Zakareishvili (166), S. Zambito (57), J.A. Zamora Saa (140d,140b), J. Zang (156), D. Zanzi (55), O. Zaplatilek (135), C. Zeitnitz (174), H. Zeng (14), J.C. Zeng (165), D.T. Zenger Jr (27), O. Zenin (38), T. Ženiš (29a), S. Zenz (96), S. Zerradi (36a), D. Zerwas (67), M. Zhai (14,114c), D.F. Zhang (142), J. Zhang (63b), J. Zhang (6), K. Zhang (14,114c), L. Zhang (63a), L. Zhang (114a), P. Zhang (14,114c), R. Zhang (173), S. Zhang (108), S. Zhang (91), T. Zhang (156), X. Zhang (63c), X. Zhang (63b), Y. Zhang (63c), Y. Zhang (98), Y. Zhang (114a), Z. Zhang (18a), Z. Zhang (63b), Z. Zhang (67), H. Zhao (141), T. Zhao (63b), Y. Zhao (139), Z. Zhao (63a), Z. Zhao (63a), A. Zhemchugov (39), J. Zheng (114a), K. Zheng (165), X. Zheng (63a), Z. Zheng (146), D. Zhong (165), B. Zhou (108), H. Zhou (7), N. Zhou (63c), Y. Zhou (15), Y. Zhou (114a), Y. Zhou (7), C.G. Zhu (63b), J. Zhu (108), X. Zhu (63d), Y. Zhu (63c), Y. Zhu (63a), X. Zhuang (14), K. Zhukov (69), N.I. Zimine (39), J. Zinsser (64b), M. Ziolkowski (144), L. Živković (16), A. Zoccoli (24b,24a), K. Zoch (62), T.G. Zorbas (142), O. Zormpa (47), W. Zou (42), L. Zwalinski (37).

¹Department of Physics, University of Adelaide, Adelaide; Australia.

²Department of Physics, University of Alberta, Edmonton AB; Canada.

³(a)Department of Physics, Ankara University, Ankara; (b)Division of Physics, TOBB University of Economics and Technology, Ankara; Türkiye.

⁴LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France.

⁵APC, Université Paris Cité, CNRS/IN2P3, Paris; France.

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

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

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

⁹Physics Department, National and Kapodistrian University of Athens, Athens; Greece.

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

¹¹Department of Physics, University of Texas at Austin, Austin TX; United States of America.

¹²Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan.

¹³Institut de Física d'Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain.

¹⁴Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; China.

¹⁵Physics Department, Tsinghua University, Beijing; China.

¹⁶Institute of Physics, University of Belgrade, Belgrade; Serbia.

¹⁷Department for Physics and Technology, University of Bergen, Bergen; Norway.

¹⁸(a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA; (b)University of California,
Berkeley CA; United States of America.

<sup>19</sup>Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany.

<sup>20</sup>Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern; Switzerland.

<sup>21</sup>School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom.

<sup>22(a)</sup>Department of Physics, Bogazici University, Istanbul; <sup>(b)</sup>Department of Physics Engineering, Gaziantep University, Gaziantep; <sup>(c)</sup>Department of Physics, Istanbul University, Istanbul; Türkiye.

<sup>23(a)</sup>Facultad de Ciencias y Centro de Investigaciones, Universidad Antonio Nariño, Bogotá; <sup>(b)</sup>Departamento de Física, Universidad Nacional de Colombia, Bogotá; Colombia.

<sup>24(a)</sup>Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna; <sup>(b)</sup>INFN Sezione di Bologna; Italy.

<sup>25</sup>Physikalisches Institut, Universität Bonn, Bonn; Germany.

<sup>26</sup>Department of Physics, Boston University, Boston MA; United States of America.

<sup>27</sup>Department of Physics, Brandeis University, Waltham MA; United States of America.

<sup>28(a)</sup>Transilvania University of Brasov, Brasov; <sup>(b)</sup>Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; <sup>(c)</sup>Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; <sup>(d)</sup>National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca; <sup>(e)</sup>National University of Science and Technology Politechnica, Bucharest; <sup>(f)</sup>West University in Timisoara, Timisoara; <sup>(g)</sup>Faculty of Physics, University of Bucharest, Bucharest; Romania.

<sup>29(a)</sup>Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; <sup>(b)</sup>Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic.

<sup>30</sup>Physics Department, Brookhaven National Laboratory, Upton NY; United States of America.

<sup>31</sup>Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, y CONICET, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires; Argentina.

<sup>32</sup>California State University, CA; United States of America.

<sup>33</sup>Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom.

<sup>34(a)</sup>Department of Physics, University of Cape Town, Cape Town; <sup>(b)</sup>iThemba Labs, Western Cape; <sup>(c)</sup>Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg; <sup>(d)</sup>National Institute of Physics, University of the Philippines Diliman (Philippines); <sup>(e)</sup>University of South Africa, Department of Physics, Pretoria; <sup>(f)</sup>University of Zululand, KwaDlangezwa; <sup>(g)</sup>School of Physics, University of the Witwatersrand, Johannesburg; South Africa.

<sup>35</sup>Department of Physics, Carleton University, Ottawa ON; Canada.

<sup>36(a)</sup>Faculté des Sciences Ain Chock, Université Hassan II de Casablanca; <sup>(b)</sup>Faculté des Sciences, Université Ibn-Tofail, Kénitra; <sup>(c)</sup>Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech; <sup>(d)</sup>LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda; <sup>(e)</sup>Faculté des sciences, Université Mohammed V, Rabat; <sup>(f)</sup>Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco.

<sup>37</sup>CERN, Geneva; Switzerland.

<sup>38</sup>Affiliated with an institute covered by a cooperation agreement with CERN.

<sup>39</sup>Affiliated with an international laboratory covered by a cooperation agreement with CERN.

<sup>40</sup>Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America.

<sup>41</sup>LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France.

<sup>42</sup>Nevis Laboratory, Columbia University, Irvington NY; United States of America.

<sup>43</sup>Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark.

<sup>44(a)</sup>Dipartimento di Fisica, Università della Calabria, Rende; <sup>(b)</sup>INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy.
<sup>45</sup>Physics Department, Southern Methodist University, Dallas TX; United States of America.

<sup>46</sup>Physics Department, University of Texas at Dallas, Richardson TX; United States of America.

<sup>47</sup>National Centre for Scientific Research "Demokritos", Agia Paraskevi; Greece.

<sup>48</sup>(<sup>a</sup>)Department of Physics, Stockholm University;<sup>(b)</sup>Oskar Klein Centre, Stockholm; Sweden.

<sup>49</sup>Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany.

<sup>50</sup>Fakultät Physik, Technische Universität Dortmund, Dortmund; Germany.

<sup>51</sup>Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany.

<sup>52</sup>Department of Physics, Duke University, Durham NC; United States of America.

<sup>53</sup>SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom.

<sup>54</sup>INFN e Laboratori Nazionali di Frascati, Frascati; Italy.

<sup>55</sup>Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany.

<sup>56</sup>II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany.

<sup>57</sup>Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland.

<sup>58</sup>(<sup>a</sup>)Dipartimento di Fisica, Università di Genova, Genova;<sup>(b)</sup>INFN Sezione di Genova; Italy.

<sup>59</sup>II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany.

<sup>60</sup>SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom.

<sup>61</sup>LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France.

<sup>62</sup>Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America.

<sup>63</sup>(<sup>a</sup>)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei;<sup>(b)</sup>Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao;<sup>(c)</sup>School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC, Shanghai;<sup>(d)</sup>Tsung-Dao Lee Institute, Shanghai;<sup>(e)</sup>School of Physics and Microelectronics, Zhengzhou University; China.

<sup>64</sup>(<sup>a</sup>)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg;<sup>(b)</sup>Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany.

<sup>65</sup>(<sup>a</sup>)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;<sup>(b)</sup>Department of Physics, University of Hong Kong, Hong Kong;<sup>(c)</sup>Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China.

<sup>66</sup>Department of Physics, National Tsing Hua University, Hsinchu; Taiwan.

<sup>67</sup>IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France.

<sup>68</sup>Centro Nacional de Microelectrónica (IMB-CNM-CSIC), Barcelona; Spain.

<sup>69</sup>Department of Physics, Indiana University, Bloomington IN; United States of America.

<sup>70</sup>(<sup>a</sup>)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;<sup>(b)</sup>ICTP, Trieste;<sup>(c)</sup>Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy.

<sup>71</sup>(<sup>a</sup>)INFN Sezione di Lecce;<sup>(b)</sup>Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy.

<sup>72</sup>(<sup>a</sup>)INFN Sezione di Milano;<sup>(b)</sup>Dipartimento di Fisica, Università di Milano, Milano; Italy.

<sup>73</sup>(<sup>a</sup>)INFN Sezione di Napoli;<sup>(b)</sup>Dipartimento di Fisica, Università di Napoli, Napoli; Italy.

<sup>74</sup>(<sup>a</sup>)INFN Sezione di Pavia;<sup>(b)</sup>Dipartimento di Fisica, Università di Pavia, Pavia; Italy.

<sup>75</sup>(<sup>a</sup>)INFN Sezione di Pisa;<sup>(b)</sup>Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy.

<sup>76</sup>(<sup>a</sup>)INFN Sezione di Roma;<sup>(b)</sup>Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy.

<sup>77</sup>(<sup>a</sup>)INFN Sezione di Roma Tor Vergata;<sup>(b)</sup>Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy.

<sup>78</sup>(<sup>a</sup>)INFN Sezione di Roma Tre;<sup>(b)</sup>Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy.

<sup>79</sup>(<sup>a</sup>)INFN-TIFPA;<sup>(b)</sup>Università degli Studi di Trento, Trento; Italy.
<sup>80</sup>Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria.

<sup>81</sup>University of Iowa, Iowa City IA; United States of America.

<sup>82</sup>Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America.

<sup>83</sup>Istinye University, Sariyer, Istanbul; Türkiye.

<sup>84</sup>(<sup>a</sup>)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora;<sup>b</sup>Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro;<sup>c</sup>Instituto de Física, Universidade de São Paulo, São Paulo;<sup>d</sup>Rio de Janeiro State University, Rio de Janeiro;<sup>e</sup>Federal University of Bahia, Bahia; Brazil.

<sup>85</sup>KEK, High Energy Accelerator Research Organization, Tsukuba; Japan.

<sup>86</sup>Graduate School of Science, Kobe University, Kobe; Japan.

<sup>87</sup>(<sup>a</sup>)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow;<sup>b</sup>Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland.

<sup>88</sup>Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland.

<sup>89</sup>Faculty of Science, Kyoto University, Kyoto; Japan.

<sup>90</sup>Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan.

<sup>91</sup>L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse; France.

<sup>92</sup>Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina.

<sup>93</sup>Physics Department, Lancaster University, Lancaster; United Kingdom.

<sup>94</sup>Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom.

<sup>95</sup>Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia.

<sup>96</sup>School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom.

<sup>97</sup>Department of Physics, Royal Holloway University of London, Egham; United Kingdom.

<sup>98</sup>Department of Physics and Astronomy, University College London, London; United Kingdom.

<sup>99</sup>Louisiana Tech University, Ruston LA; United States of America.

<sup>100</sup>Fysiska institutionen, Lunds universitet, Lund; Sweden.

<sup>101</sup>Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain.

<sup>102</sup>Institut für Physik, Universität Mainz, Mainz; Germany.

<sup>103</sup>School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom.

<sup>104</sup>CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France.

<sup>105</sup>Department of Physics, University of Massachusetts, Amherst MA; United States of America.

<sup>106</sup>Department of Physics, McGill University, Montreal QC; Canada.

<sup>107</sup>School of Physics, University of Melbourne, Victoria; Australia.

<sup>108</sup>Department of Physics, University of Michigan, Ann Arbor MI; United States of America.

<sup>109</sup>Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America.

<sup>110</sup>Group of Particle Physics, University of Montreal, Montreal QC; Canada.

<sup>111</sup>Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany.

<sup>112</sup>Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany.

<sup>113</sup>Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan.

<sup>114</sup>(<sup>a</sup>)Department of Physics, Nanjing University, Nanjing;<sup>b</sup>School of Science, Shenzhen Campus of Sun Yat-sen University;<sup>c</sup>University of Chinese Academy of Science (UCAS), Beijing; China.

<sup>115</sup>Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America.

<sup>116</sup>Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands.
117Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands.

118Department of Physics, Northern Illinois University, DeKalb IL; United States of America.

119(a)New York University Abu Dhabi, Abu Dhabi; (b)United Arab Emirates University, Al Ain; United Arab Emirates.

120Department of Physics, New York University, New York NY; United States of America.

121Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan.

122Ohio State University, Columbus OH; United States of America.

123Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America.

124Department of Physics, Oklahoma State University, Stillwater OK; United States of America.

125Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic.

126Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America.

127Graduate School of Science, Osaka University, Osaka; Japan.

128Department of Physics, University of Oslo, Oslo; Norway.

129Department of Physics, Oxford University, Oxford; United Kingdom.

130LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France.

131Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America.

132Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America.

133(a)Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa; (b)Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa; (c)Departamento de Física, Universidade de Coimbra, Coimbra; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa; (e)Departamento de Física, Universidade do Minho, Braga; (f)Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada (Spain); (g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal.

134Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic.

135Czech Technical University in Prague, Prague; Czech Republic.

136Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic.

137Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom.

138IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France.

139Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America.

140(a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; (b)Millennium Institute for Subatomic physics at high energy frontier (SAPHIR), Santiago; (c)Instituto de Investigación Multidisciplinario en Ciencia y Tecnología, y Departamento de Física, Universidad de La Serena; (d)Universidad Andres Bello, Department of Physics, Santiago; (e)Instituto de Alta Investigación, Universidad de Tarapacá, Arica; (f)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile.

141Department of Physics, University of Washington, Seattle WA; United States of America.

142Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom.

143Department of Physics, Shinshu University, Nagano; Japan.

144Department Physik, Universität Siegen, Siegen; Germany.

145Department of Physics, Simon Fraser University, Burnaby BC; Canada.

146SLAC National Accelerator Laboratory, Stanford CA; United States of America.

147Department of Physics, Royal Institute of Technology, Stockholm; Sweden.

148Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America.
America.

<sup>149</sup>Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom.

<sup>150</sup>School of Physics, University of Sydney, Sydney; Australia.

<sup>151</sup>Institute of Physics, Academia Sinica, Taipei; Taiwan.

<sup>152</sup>(<sup>a</sup>)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (<sup>b</sup>)High Energy Physics Institute, Tbilisi State University, Tbilisi; (<sup>c</sup>)University of Georgia, Tbilisi; Georgia.

<sup>153</sup>Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel.

<sup>154</sup>Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel.

<sup>155</sup>Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece.

<sup>156</sup>International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan.

<sup>157</sup>Department of Physics, Tokyo Institute of Technology, Tokyo; Japan.

<sup>158</sup>Department of Physics, University of Toronto, Toronto ON; Canada.

<sup>159</sup>(<sup>a</sup>)TRIUMF, Vancouver BC; (<sup>b</sup>)Department of Physics and Astronomy, York University, Toronto ON; Canada.

<sup>160</sup>Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan.

<sup>161</sup>Department of Physics and Astronomy, Tufts University, Medford MA; United States of America.

<sup>162</sup>Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America.

<sup>163</sup>University of Sharjah, Sharjah; United Arab Emirates.

<sup>164</sup>Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden.

<sup>165</sup>Department of Physics, University of Illinois, Urbana IL; United States of America.

<sup>166</sup>Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia; Spain.

<sup>167</sup>Department of Physics, University of British Columbia, Vancouver BC; Canada.

<sup>168</sup>Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada.

<sup>169</sup>Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany.

<sup>170</sup>Department of Physics, University of Warwick, Coventry; United Kingdom.

<sup>171</sup>Waseda University, Tokyo; Japan.

<sup>172</sup>Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel.

<sup>173</sup>Department of Physics, University of Wisconsin, Madison WI; United States of America.

<sup>174</sup>Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany.

<sup>175</sup>Department of Physics, Yale University, New Haven CT; United States of America.

<sup><sup>a</sup></sup>Also Affiliated with an institute covered by a cooperation agreement with CERN.

<sup><sup>b</sup></sup>Also at An-Najah National University, Nablus; Palestine.

<sup><sup>c</sup></sup>Also at Borough of Manhattan Community College, City University of New York, New York NY; United States of America.

<sup><sup>d</sup></sup>Also at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki; Greece.

<sup><sup>e</sup></sup>Also at Centro Studi e Ricerche Enrico Fermi; Italy.

<sup><sup>f</sup></sup>Also at CERN, Geneva; Switzerland.

<sup><sup>g</sup></sup>Also at CMD-AC UNEC Research Center, Azerbaijan State University of Economics (UNEC); Azerbaijan.

<sup><sup>h</sup></sup>Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland.

<sup><sup>i</sup></sup>Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain.

<sup><sup>j</sup></sup>Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece.
k Also at Department of Physics, California State University, Sacramento; United States of America.

l Also at Department of Physics, King's College London, London; United Kingdom.

m Also at Department of Physics, Stanford University, Stanford CA; United States of America.

n Also at Department of Physics, Stellenbosch University; South Africa.

o Also at Department of Physics, University of Fribourg, Fribourg; Switzerland.

p Also at Department of Physics, University of Thessaly; Greece.

q Also at Department of Physics, Westmont College, Santa Barbara; United States of America.

r Also at Faculty of Physics, Sofia University, 'St. Kliment Ohridski', Sofia; Bulgaria.

s Also at Hellenic Open University, Patras; Greece.

t Also at Institutio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain.

u Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany.

v Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia; Bulgaria.

w Also at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco.

x Also at Institute of Particle Physics (IPP); Canada.

y Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan.

z Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia.

aa Also at National Institute of Physics, University of the Philippines Diliman (Philippines); Philippines.

ab Also at Technical University of Munich, Munich; Germany.

ac Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China.

ad Also at TRIUMF, Vancouver BC; Canada.

ae Also at Università di Napoli Parthenope, Napoli; Italy.

af Also at University of Colorado Boulder, Department of Physics, Colorado; United States of America.

ag Also at Washington College, Chestertown, MD; United States of America.

ah Also at Yeditepe University, Physics Department, Istanbul; Türkiye.

* Deceased