Search for long-lived particles produced in pp collisions at $\sqrt{s}$=13 TeV
that decay into displaced hadronic jets in the ATLAS muon spectrometer

M. Aaboud et al. *

(ATLAS Collaboration)

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

(Received 21 November 2018; published 15 March 2019)

A search for the decay of neutral, weakly interacting, long-lived particles using data collected by the ATLAS detector at the LHC is presented. The analysis in this paper uses 36.1 fb⁻¹ of proton-proton collision data at √s = 13 TeV recorded in 2015–2016. The search employs techniques for reconstructing vertices of long-lived particles decaying into jets in the muon spectrometer exploiting a two-vertex strategy and a novel technique that requires only one vertex in association with additional activity in the detector that improves the sensitivity for longer lifetimes. The observed numbers of events are consistent with the expected background and limits for several benchmark signals are determined.

DOI: 10.1103/PhysRevD.99.052005

# I. INTRODUCTION

The discovery of the Higgs boson at the LHC completed the Standard Model (SM) of elementary particles and focused attention on the many central features of our universe that the SM does not address: dark matter, neutrino mass, matter-antimatter asymmetry (baryogenesis), and the hierarchy problem (naturalness). Many beyond the Standard Model (BSM) theoretical constructs proposed in the past few years that address these phenomena predict the existence of long-lived particles (LLPs) with macroscopic decay lengths that are limited only by big bang nucleosynthesis to about $c\tau \lesssim 10^7-10^8$ m, where $\tau$ is the proper lifetime of the LLP [1]. Examples include supersymmetric (SUSY) models such as mini-split SUSY [2,3], gauge-mediated SUSY breaking [4], R-parity-violating SUSY [5,6] and stealth SUSY [7,8]; models addressing the hierarchy problem such as neutral naturalness [9–12] and hidden valleys [13,14]; models addressing dark matter [15–19], and the matter-antimatter asymmetry of the universe [20–22]; and models that generate neutrino masses [23,24]. Many of these theoretical models result in neutral LLPs, which may be produced in the proton-proton collisions of the LHC and decay back into SM particles far from the interaction point (IP).

Searches for LLPs decaying into final states containing jets were carried out at the Tevatron (√s = 1.96 TeV) by both the CDF [25] and D0 [26] Collaborations, at the LHC

by the ATLAS and LHCb Collaborations in proton-proton collisions at $\sqrt{s}$ = 7 TeV [27,28], by the ATLAS, CMS and LHCb Collaborations at $\sqrt{s}$ = 8 TeV [29–34] and more recently by the CMS Collaboration at $\sqrt{s}$ = 13 TeV [35]. To date, no search has observed evidence of BSM, neutral LLPs.

This paper describes a search for neutral LLPs produced in proton-proton interactions at $\sqrt{s}$ = 13 TeV, using 36.1 fb⁻¹ of data recorded by the ATLAS detector at the LHC during 2015 and 2016. Decays of LLPs can result in secondary decay vertices (displaced vertices) that are highly displaced from the IP. The present paper focuses on LLP decays reconstructed in the muon spectrometer. Three different analysis strategies are considered, with each strategy targeting a specific event topology. Two single-vertex strategies are based on methodologies presented in Ref. [36], and the third strategy is an inclusive search for two displaced vertices in the muon spectrometer.

This work significantly extends the mean proper lifetime ($c\tau$) range of the ATLAS search for a light scalar boson decaying into long-lived neutral particles beyond that at $\sqrt{s}$ = 8 TeV in 20.3 fb⁻¹ of 2012 proton-proton collision data [29], which covered the $c\tau$ region 1–100 m. Additionally, it extends the range of excluded proper lifetimes beyond that of a recent ATLAS analysis [32] that searches for displaced decays in the hadronic calorimeter and uses the same scalar boson model and mass points.

The paper first describes the ATLAS detector in Sec. II, followed by the event selection strategy in Sec. III, the benchmark models in Sec. IV, and the data and simulation samples in Sec. V. The specialized trigger and reconstruction algorithms are discussed in Sec. VI, followed by a description of the baseline selection applied to all events in Sec. VII. Sections VIII and IX outline the three search topologies.

<footnote>*Full author list given at the end of the article.</footnote>

<footnote>Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP³.</footnote>
Systematic uncertainties are summarized in Sec. X and results for all three topologies are presented in Sec. XI. The summary and conclusions are given in Sec. XII.

## II. ATLAS DETECTOR

The ATLAS detector [37], which has nearly $4\pi$ steradian coverage, is a multipurpose detector consisting of an inner tracking detector (ID) surrounded by a superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer (MS) based on three large air-core toroidal superconducting magnets, each with eight coils. The ID covers the range $0.03 \text{ m} < r < 1.1 \text{ m}$ and $|z| < 3.5 \text{ m}$.<fnref n="1" /> It consists of a silicon pixel detector, a silicon microstrip detector, and a straw-tube transition-radiation tracker. Together, the three systems provide precision tracking of charged particles for $|\eta| < 2.5$.

The calorimeter system covers the pseudorapidity range $|\eta| < 4.9$. It consists of a high-granularity lead/liquid-argon electromagnetic calorimeter (ECal) surrounded by a hadronic calorimeter (HCal). Within the region $|\eta| < 3.2$, the ECal comprises liquid-argon (LAr) barrel and end cap electromagnetic calorimeters with lead absorbers. An additional thin LAr presampler covering $|\eta| < 1.8$ is used to correct for energy loss in material upstream of the calorimeters. The ECal extends from 1.5 m to 2.0 m in $r$ in the barrel and from 3.6 m to 4.25 m in $|z|$ in the end caps. The HCal is a steel/scintillator-tile calorimeter that is segmented into three barrel structures within $|\eta| < 1.7$, and two copper/LAr hadronic calorimeters in the end cap ($1.5 < |\eta| < 3.2$). The HCal covers the region from 2.25 m to 4.25 m in $r$ in the barrel (although the HCal active material extends only up to 3.9 m) and from 4.3 m to 6.05 m in $|z|$ in the end caps. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimized for electromagnetic and hadronic measurements, respectively. Together the ECal and HCal have a thickness of 9.7 interaction lengths at $\eta = 0$.

The MS comprises three stations of separate trigger and tracking chambers that measure the deflection of muons in a magnetic field generated by the air-core toroid magnets. The barrel chamber system is subdivided into 16 sectors: 8 large sectors (between the magnet coils) and 8 small sectors (inside the magnet coils). Three stations of resistive plate chambers (RPC) and thin gap chambers (TGC) are used for triggering in the MS barrel and end caps, respectively.

The first two RPC stations, which are radially separated by 0.5 m, start at a radius of either 7 m (large sectors) or 8 m (small sectors). The third station is located at a radius of either 9 m (large sectors) or 10 m (small sectors). In the end caps, the first TGC station is located at $|z| = 13 \text{ m}$. The other two stations start at $|z| = 14 \text{ m}$ and $|z| = 14.5 \text{ m}$, respectively. The muon trigger system covers the range $|\eta| < 2.4$. The muon tracking chamber system covers the region $|\eta| < 2.7$ with three layers of monitored drift tubes (MDT), complemented by cathode strip chambers (CSC) in the forward region. The MDT chambers consist of two multilayers separated by a distance ranging from 6.5 mm to 317 mm. Each multilayer consists of three or four layers of drift tubes. The individual drift tubes are 30 mm in diameter and have a length of 2–5 m depending on the location of the chamber in the spectrometer. In each multilayer the charged-particle track segment can be reconstructed by finding the line that is tangent to the drift circles. These segments are local measurements of the position and direction of the charged particle. Because the tubes are 2–5 m in length with a direction along $\phi$, the MDT measurement provides only a very coarse $\phi$ position of the track hit. In order to reconstruct the $\phi$ position and direction, the MDT measurements are combined with the $\phi$ coordinate measurements from the trigger chambers.

The ATLAS trigger and data acquisition system [38] consists of a hardware-based first-level trigger (L1) followed by a software-based high-level trigger (HLT) that reduces the rate of recorded events for offline storage to 1 kHz.

The implementation of the L1 muon trigger logic is similar for both the RPC and TGC systems. Each of the three planes of the RPC system and the two outermost planes of the TGC system consist of a doublet of independent detector layers. The first TGC plane contains three detector layers. A low-$p_T$ (<10 GeV) muon region-of-interest (RoI) is generated by requiring a coincidence of hits in at least three of the four layers of the two inner RPC planes for the barrel. In the end caps, the trigger requires hits in the two outer TGC planes. A high-$p_T$ muon RoI requires additional hits in at least one of the two layers of the outer RPC plane for the barrel, while for the end caps, hits in two of the three layers of the innermost TGC layer are required. The muon RoIs have a spatial extent of $0.2 \times 0.2 \text{ in } \Delta\eta \times \Delta\phi$ in the MS barrel and $0.1 \times 0.1 \text{ in } \Delta\eta \times \Delta\phi$ in the MS end caps. Only the two highest-$p_T$ RoIs per MS sector are used by the HLT.

The L1 calorimeter trigger is based on information from the calorimeter elements within projective regions, called trigger towers. The trigger towers have a size of approximately 0.1 in $\Delta\eta$ and $\Delta\phi$ in the central part of the calorimeter, $|\eta| < 2.5$, and are larger and less uniform in the more forward region.

## III. ANALYSIS STRATEGY

The analysis presented in this paper searches for events with two displaced vertices in the MS, or one displaced

<footnote n="1">¹ATLAS uses a right-handed coordinate system with its origin at the nominal IP in the center of the detector and the z axis along the beam pipe. The x axis points from the IP to the center of the LHC ring, and the y axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, where φ is the azimuthal angle around the z axis. The pseudorapidity is defined in terms of the polar angle θ as η = -ln tan(θ/2). Angular distance is measured in units of ΔR ≡ $\sqrt{(\Delta\eta)^2 + (\Delta\phi)^2}$.</footnote>
<table><caption>TABLE I. Topologies considered in this paper, corresponding basic event selection and benchmark models.</caption><thead><tr><th>Strategy</th><th>Basic event selection</th><th>Benchmarks</th></tr></thead><tbody><tr><td>2MSVx</td><td>at least 2 MS vertices</td><td>scalar portal, Higgs portal baryogenesis, stealth SUSY</td></tr><tr><td>1MSVx+Jets</td><td>exactly 1 MS vertex at least 2 jets<br>with E<sub>T</sub> &gt; 150 GeV</td><td>stealth SUSY</td></tr><tr><td>1MSVx + E<sub>T</sub><sup>miss</sup></td><td>exactly 1 MS vertex E<sub>T</sub><sup>miss</sup> &gt; 30 GeV</td><td>scalar portal with m<sub>Φ</sub> = 125 GeV,<br>Higgs portal baryogenesis</td></tr></tbody></table>

vertex in the MS in association with additional activity in the detector. Three separate strategies are studied, defined by the number of MS vertices and additional selection criteria. The benchmark models that motivate these strategies are discussed in detail in Sec. IV.

Candidate events are selected by the Muon RoI Cluster trigger [39] that requires a cluster of three (four) muon RoIs in the barrel (end caps). No jet or track isolation requirements are applied at trigger level. Displaced vertices are then reconstructed using a dedicated MS vertex reconstruction algorithm [40]. For each strategy considered in this paper additional selection criteria, optimized by comparing signal to background events, are used to maximize the analysis sensitivity.

The simplest strategy requires at least two MS vertices (2MSVx), and it is inclusive of any other activity in the event. The other two strategies require exactly one MS vertex, with additional requirements on associated objects (1MSVx+AO). The first requires exactly one MS vertex and two prompt jets (1MSVx+Jets), targeting models where prompt jets are produced together with the LLP, as expected in the stealth SUSY scenarios. In these cases, the two prompt jets can also contribute to signal event selection. The second requires a small amount of missing transverse momentum (denoted by $E_T^{\text{miss}}$) in addition to the single displaced vertex (1MSVx + $E_T^{\text{miss}}$) and targets models that do not predict significant activity in addition to LLPs, such as from decays of “SM-like” Higgs bosons into long-lived neutral scalar particle pairs. In fact, since the tracks originating from vertices in the MS are not considered in the $E_T^{\text{miss}}$ computation, for these models the $E_T^{\text{miss}}$ in signal events is sensitive to the Higgs boson $p_T$, which is typically of the order of tens of GeV. For signal vertices, if the second LLP has decayed in the ID or calorimeter the $E_T^{\text{miss}}$ vector tends to be aligned with the direction of the displaced vertex, measured from the origin of the detector coordinate system. However, if the decay of the second LLP occur beyond the MS there is no missing energy. Therefore, the angle in the transverse plane between the vertex and the $E_T^{\text{miss}}$ direction can contribute to the signal event selection. The three analysis strategies are summarized in Table I, together with the theoretical benchmark models used in this paper.

The main source of background to LLPs decaying into hadronic jets in the MS is from hadronic or electromagnetic

showers not contained in the calorimeter volume (punch-through jets) resulting in tracks reconstructed in the MS. Multijet events that contain vertices in the MS would have ID tracks and jets that point towards the displaced MS vertex as well as inwards to the IP. To reduce the acceptance of fake vertices from multijet events, vertices are required to be isolated from ID tracks and calorimeter jets. Additional background, referred to in this paper as noncollision background, can be generated by electronic noise in the MDT and RPC/TGC chambers, by cosmic-ray muons, by multijet events with mismeasured jets and by machine-induced background [41]. This last contribution, usually referred to as beam-induced background, is composed of particles produced in the hadronic and electromagnetic showers caused by beam protons interacting with collimators or residual gas molecules inside the vacuum pipe.

To avoid unintended biasing of the results, the signal regions of the 2MSVx and 1MSVx+AO strategies were blinded during the analysis development.

## IV. DESCRIPTION OF BENCHMARK MODELS

Although the event selections outlined in Sec. III are sensitive to a large variety of models, this paper interprets the results in terms of three different benchmark models. The first, shown in Fig. 1(a), is a scalar portal model [14], where a SM-like Higgs or lower/higher-mass boson ($\Phi$) decays into two long-lived scalars ($\mathcal{S}$). Figure 1(b) shows the second model, Higgs portal baryogenesis [22], in which a SM-like Higgs boson ($\mathcal{H}$) decays into long-lived Majorana fermions $\chi$ that decay into fermions, violating baryon and/or lepton number conservation. The last model, shown in Fig. 1(c), is a stealth SUSY model [7,8] where the long-lived singlino ($\tilde{S}$) is produced by a gluino ($\tilde{g}$) in association with a prompt gluon-jet ($\mathcal{G}$). The singlino decay produces two gluons and a light gravitino.

The decay channels, the relative masses and lifetimes generated for each model, as well as details about the Monte Carlo (MC) event generation are described in Sec. V.

### A. Scalar portal

A theoretically popular way of introducing long-lived, neutral particles to the SM is through a hidden sector that weakly couples to the SM. For example, scalar (Higgs) portals [13,14,42], where the Higgs boson weakly mixes
<figure><img src="image_2.png" /><figcaption>FIG. 1. Diagrams of the benchmark models studied in this paper: (a) scalar portal model, (b) Higgs portal baryogenesis model, and (c) stealth SUSY model. The LLPs in these processes are represented by double lines and labeled (a) $s$, (b) $\chi$, and (c) $\tilde{S}$. In the stealth SUSY model, $\tilde{G}$ is the gravitino and $S$ is the singlet. The final-state SM fermions are labeled as $f$, and the gluons as $g$.</figcaption></figure>

with a hidden-sector scalar, can result in pair production of hidden-sector scalars or pseudoscalars that carry no SM quantum numbers. The branching fraction limit for SM Higgs boson decays into undetected particles is currently at the 25% level [43] (assuming SM-like Higgs boson production and width), potentially allowing sizable branching fractions for decays into non-SM particles.

Moreover, models of neutral naturalness [44] are generic extensions of hidden-valley portal models where the scalar masses can be very low, typically 5–15 GeV. To date, no LHC analysis has explored this model.

The mechanism for LLP production in scalar decays is shown in Fig. 1(a). Here, a scalar boson $\Phi$ decays with some effective coupling into a pair of long-lived scalars, $s$. The scalars $s$ subsequently decay into SM particles. Since this model assumes that the couplings of the scalar to SM particles are determined by a Yukawa coupling, each long-lived scalar decays mainly into heavy fermions, $b\bar{b}$, $c\bar{c}$, and $\tau^+\tau^-$. The branching fractions of these decays depend on the mass of the scalar, $m_s$, but for $m_s \gtrsim 25$ GeV they are almost constant and equal to 85%, 5%, and 8%.

The branching fraction for $\Phi$ decaying into a pair of hidden-sector particles is not constrained in these models. It is therefore interesting to focus both on Higgs boson decays into LLPs, where $\Phi$ is a SM-like Higgs boson, and on other $\Phi$ mass regions previously unexplored for decays into LLPs.

## B. Higgs portal baryogenesis

The origin of the cosmic asymmetric abundance of baryons remains one of the most prominent questions that demand physics beyond the SM. Several baryogenesis mechanisms have been proposed, but electroweak baryogenesis is one of the few with signatures that could be explored at the LHC energies. In addition to better testability, baryogenesis based on new weak-scale particles is also theoretically appealing since it can naturally connect new physics addressing the weak-scale hierarchy problem with the dynamics responsible for generating the baryon asymmetry. A few examples of low-scale ($\lesssim$ TeV) baryogenesis models that generate the baryon asymmetry via the

decays of weak-scale states have been shown to have direct testability at colliders [45–47].

In the baryogenesis model considered for this paper [22], the lowest-dimension operator coupling a singlet $\chi$ to the SM is the Higgs portal. The simplest realization of this interaction is with a scalar, $\Phi$, that mixes with the SM Higgs boson [48]. If $\Phi$ has a Yukawa coupling to a pair of $\chi$, this leads to the Higgs portal production of $\chi$ via exchange of a single SM-like Higgs boson after mixing, $pp \to h \to \chi\chi$, as shown in Fig. 1(b). Since LHC experiments have established the existence of a SM-like Higgs boson with a mass of 125 GeV, while the other possibilities are more model-dependent, the model used here assumes the minimal spectrum where the $\Phi$ scalar is heavy and decouples, and focuses on the production channel via the SM Higgs portal.

For the production of the $\chi$ through the Higgs portal, two different regimes can be identified.

(i) $m_\chi < m_h/2$: in this region the dominant production mechanism is through an on-shell Higgs boson. The $\chi$ production at 13 TeV is expected to be copious, $O(10 \text{ pb})$, and the constraints set by the current LHC searches are correspondingly strong. There are also indirect limits on the non-SM decay branching fraction of the Higgs boson based on global fits [49,50]. Despite all these strong constraints, the on-shell region is still very interesting due to the sizable branching fractions allowed for the Higgs decays into BSM particles [49,50].

(ii) $m_\chi > m_h/2$: in this region the Higgs boson is off-shell and the signal rate falls rapidly with increasing $m_\chi$, even for large mixing. The cross section expected for a $\chi$ mass of 100 GeV is about 7 fb.

The decay modes of the $\chi$ must violate baryon and/or lepton number conservation, which generates the baryonic asymmetry. The lowest-dimensional interactions of this type allow $\chi$ to decay into three SM fermions. The decay channels used in this paper, $\chi \to \tau^+\tau^-\nu_\ell$, $cbs$, $\ell^+\bar{c}b$, $\nu b\bar{b}$, are examples of three types of couplings inspired by R-parity-violating SM fermion trilinear operators that can couple to $\chi$ [22]. The charge conjugates of these decay channels are also considered. Decays into final states as
cbb or `css` are not possible since the R-parity-violating operator containing two down-type quarks with the same flavor is not allowed. Mixed cases such as `cbd` or `cds` are possible, but they are not considered in this paper since their kinematics is similar to the `cbs` channel and give similar results.

### C. Stealth SUSY

Stealth SUSY models [7,8] are a class of R-parity-conserving SUSY models that do not have large $E_p^{miss}$ signatures. While this can be accomplished in many different ways, this search explores a model that involves adding a hidden-sector (stealth) singlet superfield $S$ at the electro-weak scale, which has a superpartner singlino $\tilde{S}$. By weakly coupling the hidden sector to the minimal supersymmetric Standard Model [51], the mass-splitting between $S$ and $\tilde{S}$ ($\delta M$) is small, assuming low-scale SUSY breaking. High-scale SUSY breaking also can be consistent with small mass splitting and stealth SUSY, although this requires a more complex model and is not considered in this search [8].

The SUSY decay chain ends with the singlino decaying into a singlet plus a low-mass gravitino $\tilde{G}$, where the gravitino carries off very little energy and the singlet promptly decays into two gluons. The effective decay processes are $\tilde{g} \to \tilde{S}g$ (prompt), $\tilde{S} \to \tilde{S}\tilde{G}$ (not prompt), and $S \to gg$ (prompt), where the gravitino is treated as massless. This scenario results in one prompt gluon and two displaced gluons per gluino decay. Since R-parity is assumed to be conserved, each event necessarily produces two gluinos, resulting in two displaced vertices. A representative diagram of this process is shown in Fig. 1(c). The simplified stealth SUSY model considered in this paper assumes that all squarks are decoupled.

The decay width (and, consequently, the lifetime) of the singlino is determined by both the $\delta M$ and the SUSY-breaking scale $\sqrt{F}: \Gamma_{\tilde{S}-S\tilde{G}} \approx m_{\tilde{S}}(\delta M)^4/\pi F^2$ [7]. The SUSY-breaking scale $\sqrt{F}$ is not a fixed parameter, and thus the singlino has the possibility of traveling an appreciable distance through the detector, leading to a significantly displaced vertex.

### V. DATA AND SIMULATION SAMPLES

The analysis presented in this paper uses $\sqrt{s} = 13$ TeV $pp$ collision data recorded by the ATLAS detector with stable LHC beams during the 2015 and 2016 data-taking periods. After data quality requirements, the total integrated luminosity is 3.2 fb$^{-1}$ and 32.9 fb$^{-1}$ for 2015 and 2016, respectively.

Zero-bias data are used to estimate the expected background for the 2MSVx strategy and potential contamination by noncollision background in the 1MSVx+AO strategies. These data are acquired with a special trigger which fires on the bunch crossing that occurs one LHC revolution after a low-threshold calorimeter-based trigger

and therefore have a negligible signal contamination. The zero-bias trigger runs throughout ATLAS data taking, so these data are acquired with the same beam conditions present in normal physics data and can be used to study the expected background. Due to the very high output event rate, the zero-bias trigger is prescaled and only a fraction of the total events are recorded. For this reason, the integrated luminosity acquired is much lower than the total collected during 2015 and 2016 data taking and corresponds to 1.1 $\mu$b$^{-1}$ and 12 $\mu$b$^{-1}$ for the two periods, respectively.

Monte Carlo simulation samples were produced for all models considered in this paper. The masses, summarized in Table II, were chosen to span the accessible parameter space. For the stealth SUSY model, the singlino and singlet masses were set to 100 and 90 GeV, respectively. These values were recommended by the authors of the model as a good representative choice [7]. The small mass-splitting between the singlino and singlet ensures that the gravitino carries off very little momentum. The mean proper lifetime of each sample is tuned to obtain a mean lab-frame decay length of 5 m. This choice maximizes the distribution of decays throughout the ATLAS detector volume. The mean proper lifetime used for the generation of the samples is within a range of 0.17–5.55 m, depending on the sample. For each MC sample, 400 000 events are produced.

Since the analysis is sensitive to a wide range of mean proper lifetimes, and the generation of many samples to cover a broad lifetime range would be extremely CPU-time consuming, a toy MC strategy was adopted to extrapolate the number of expected events to the range of mean proper lifetimes between 0 and 1000 m. For each LLP in the MC sample a random decay position sampled from an

<table><caption>TABLE II. Mass parameters for the simulated scalar portal, Higgs portal baryogenesis, and stealth SUSY models.</caption><thead><tr><th>Model</th><th><i>m</i><sub>φ</sub> [GeV]</th><th><i>m</i><sub>s</sub> [GeV]</th></tr></thead><tbody><tr><td rowspan="6">Scalar portal</td><td>100</td><td>8, 25</td></tr><tr><td>125</td><td>5, 8, 15, 25, 40</td></tr><tr><td>200</td><td>8, 25, 50</td></tr><tr><td>400</td><td>50, 100</td></tr><tr><td>600</td><td>50, 150</td></tr><tr><td>1000</td><td>50, 150, 400</td></tr><tr><td rowspan="4">Higgs portal<br>baryogenesis</td><td><i>m</i><sub>χ</sub> [GeV]</td><td>χ decay channel</td></tr><tr><td>10</td><td>τ<sup>+</sup>τ<sup>-</sup>ν<sub>ℓ</sub>, cbs, ℓ<sup>+</sup>c<span style="text-decoration: overline">b</span>, νb<span style="text-decoration: overline">b</span></td></tr><tr><td>30</td><td></td></tr><tr><td>50</td><td></td></tr><tr><td></td><td></td></tr><tr><td rowspan="6">Stealth SUSY</td><td><i>m</i><sub>˜g</sub> [GeV]</td><td><i>m</i><sub>˜s</sub>, <i>m</i><sub>S</sub> [GeV]</td></tr><tr><td>250</td><td>100, 90</td></tr><tr><td>500</td><td></td></tr><tr><td>800</td><td></td></tr><tr><td>1200</td><td></td></tr><tr><td>1500</td><td></td></tr><tr><td></td><td></td></tr><tr><td></td><td></td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>FIG. 2. Efficiency for the muon RoI cluster trigger as a function of the decay position of the LLP for four simulated benchmark samples in the (a) MS barrel and (b) MS end caps. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second layer of RPC chambers, TGC 1 represents the first layer of TGC chambers and L/S indicates whether they are in large or small sectors.</figcaption></figure>

The relative differences between the efficiencies of the benchmark samples are a result of the different masses of the LLPs, which in turn affect their momenta and consequently the opening angles of the decay products. The trigger efficiency is higher when the LLP decays close to the end of the hadronic calorimeter (barrel: $r \sim 4$ m; end caps: $z \sim 6$ m) and it decreases substantially as the decay occurs closer to the middle station of the muon spectrometer (barrel: $r \sim 7$ m; end caps: $z \sim 13$ m). For decays occurring close to the middle station, the charged hadrons and photons (and their EM showers) are not spatially separated and they are overlapping when they traverse the middle stations.

Scale factors were used in order to correct for mismodeling of the L1 muon trigger response in MC simulation and they were calculated by comparing the distributions of the average number of muon RoI clusters within a $\Delta R$ cone of 0.4 around the axis of a punch-through jet in multijet MC and data events. In fact, a high-energy jet has a high probability of punching through into the MS and creating a cluster of muon RoIs that can mimic the behavior of signal events. High-energy jets were selected using a jet trigger with a $E_T$ threshold of 400 GeV. The scale factor is $1.13 \pm 0.01$ for the barrel and $1.04 \pm 0.02$ for the end caps, and it does not depend on the $\eta$ or the $p_T$ of the jet.

### C. Reconstruction of MS vertices

A dedicated algorithm [40], capable of reconstructing low-momentum tracks in a busy environment, was used to reconstruct the displaced MS vertices used in this search. The algorithm takes advantage of the spatial separation between the two multilayers inside a single MDT chamber. Single-multilayer straight-line segments that contain three or more MDT hits were reconstructed using a minimum $\chi^2$ fit. Segments from multilayer-1 were then matched with those from multilayer-2. The paired set of single-multilayer

segments and corresponding track parameters is called a *tracklet*. These tracklets are used to reconstruct the positions of MS vertices. This algorithm was previously used for both the 7 TeV [27] and 8 TeV [71] searches for displaced decays. Detectable decay vertices were located in the region between the outer edge of the HCal and the middle station of muon chambers. Due to the different detector technology (no spatially separated multilayers), the CSC chambers were not used for the MS vertex reconstruction.

#### 1. Reconstructed objects for vertex isolation

In order to ensure sufficient signal acceptance and background rejection, a set of *vertex isolation criteria* for ID tracks and calorimeter jets was established in order to assist in determining whether or not a vertex is consistent with a displaced hadronic decay.

For track isolation, two separate criteria were used: one for high-$p_T$ tracks which considers tracks with $p_T > 5$ GeV, and one for large multiplicities of low-$p_T$ tracks which used the $p_T$ vector sum of all tracks associated with the PV with $p_T > 400$ MeV in a $\Delta R$ cone of 0.2 around the MS vertex axis.<fnref n="2" /> The two different isolations stem from the fact that some jets have most of their energy in a single hadron, while others can consist of multiple low-$p_T$ tracks.

For the 2MSVx and 1MSVx+Jets strategies, all the jets considered for isolation must meet *jet quality criteria*. Jets must satisfy $E_T > 30$ GeV and $\log_{10}(E_{HAD}/E_{EM}) < 0.5$. The value $\log_{10}(E_{HAD}/E_{EM})$ quantifies the fraction of energy of the jet that is deposited in the HCal ($E_{HAD}$) with respect to the energy deposited in the ECal ($E_{EM}$). This requirement ensures that vertices originating from LLPs that decay near the outer edge of the hadronic calorimeter and

<footnote n="2">²The MS vertex axis is defined with respect to the detector coordinate system.</footnote>
also have significant MS activity were not rejected. In addition, in order to reduce the probability that a signal vertex fails to meet the isolation criteria due to pileup jets that do not have sufficient energy to create an MS vertex, jets with $20 < E_T < 60$ GeV were required to be matched to the PV using a jet vertex tagger (JVT) discriminant [72]. Standard jet quality criteria [73] were not enforced because jets that do not fulfill these requirements can also produce a background MS vertex.

For the 1MSVx + $E_T^{\text{miss}}$ strategy a looser selection on the jets used for isolation was applied. The reason for this is that most of the background that enters the signal region of this strategy is generated by events where a jet satisfying the jet quality criteria is almost back-to-back with an MS vertex created by another jet that does not fulfill the jet quality criteria, but has enough energy to punch through into the muon spectrometer. Studies performed in data showed that there could be standard or pileup jets with a measured energy down to 15 GeV that can create a vertex in the MS. These jets would not be used to compute the isolation since they do not pass the $E_T$ selection require-ment present in jet quality criteria and the associated vertex would be incorrectly considered as a signal candidate. For these reasons, all jets above 15 GeV were considered in the isolation computation for the 1MSVx + $E_T^{\text{miss}}$ strategy. The looser selection on the jets used for isolation has negligible impact on the signal efficiency, according to simulation; in all the samples considered for the results presented in this paper there are no events rejected because of a jet below 20 GeV entering the isolation.

The same type of jet events could also affect the 2MSVx and 1MSVx+Jets strategies. For the former the jet quality criteria have no impact on the background estimation, while for the latter the effect is negligible because the additional selection on the energy of the two prompt jets strongly reduces this background contribution.

Vertex isolation criteria were optimized separately for each analysis strategy described in Sec. III, and they are described in detail in Secs. VIII and IX.

## VII. BASELINE EVENT SELECTION

A common baseline selection was applied to the events considered in the three strategies described in Table I. Events were required to pass the muon RoI cluster trigger and contain a PV with at least two tracks with $p_T > 400$ MeV. The vertex with the largest sum of the squares of the transverse momenta of all tracks associated with the vertex was chosen as the PV. This PV selection has no impact on the signal efficiency. In simulation, the selected PV corresponds to the signal interaction in about 95%-99% of the cases, depending on the sample; even though the LLPs are invisible in the ID, the resonance (scalar, Higgs boson) is produced with a significant $p_T$.

An MS vertex due to a displaced decay typically has many more hits than an MS vertex from background;

consequently a minimum number of MDT ($n_{\text{MDT}}$) and RPC/TGC ($n_{\text{RPC}}/n_{\text{TGC}}$) hits was required. The number of MDT hits was counted in the MDT chambers that have their center within $\Delta\phi = 0.6$ and $\Delta\eta = 0.6$ of the vertex ($\eta, \phi$) position. The number of RPC or TGC hits is the sum of hits that are within $\Delta R = 0.6$ of the vertex position. A requirement on the maximum number of MDT hits was also applied to remove background events caused by coherent noise bursts in the MDT chambers. In addition to reducing the background, the minimum required number of RPC/TGC hits helps to further reject these noisy events, because a noise burst in the MDT system is not expected to be coherent with one in the muon trigger system.

A displaced decay that occurs in the transition region between MS barrel and end caps results in hits in both regions. Vertex reconstruction was performed separately in the barrel and end caps, and only the barrel (end cap) hits were used in the barrel (end cap) vertex reconstruction algorithm. Therefore, any vertices reconstructed from either of the two algorithms have fewer hits, as they were reconstructed from a subset of the total hits. The result is a decrease in the reconstruction efficiency, and this also occasionally results in two vertices being reconstructed from a single LLP decay. Therefore, the MS vertices with pseudorapidity, $|\eta_{\text{vx}}|$, between 0.8 and 1.3 were not considered in the analysis. This has a negligible impact on the signal efficiency, since the average MS vertex efficiency in this region is less than 2%.

Background studies performed for the 1MSVx+AO strategies using data showed that in the transition region between the barrel and the end cap hadronic calorimeters, $0.7 < |\eta_{\text{vx}}| < 1.2$, the probability of having a jet that does not fulfill the minimal selection criteria for being consid-ered for isolation and that punches through into the MS is much higher than in other regions of the detector. This region overlaps the already excluded MS transition region, except for $0.7 < |\eta_{\text{vx}}| < 0.8$. The fraction of signal events removed was very small compared to the gain obtained by removing punch-through jet background that could affect the single-vertex analysis; therefore, vertices reconstructed in the MS region $0.7 < |\eta_{\text{vx}}| < 0.8$ were not considered either.

Table III summarizes the baseline criteria used to select “good” MS vertices. After this selection, the main back-ground contribution is from punch-through jets. This remains true after further selections are applied in both the 1MSVx+AO and 2MSVx strategies. The number of events passing the baseline selection reported in Table III is 389 743 and 1 209 324 in the barrel and end caps, respectively.

## VIII. TWO-MS-VERTEX SEARCH

The two-MS-vertex strategy is designed to be sensitive to models where the LLP is pair-produced and decays hadronically between the outer region of the HCal and the
<table><caption>TABLE III. Summary of the baseline criteria used for the analysis presented in this paper. All selection criteria are also applied to signal MC events when determining the number of expected signal events in the dataset.</caption><tr><td colspan="2">Event passes muon RoI cluster trigger.</td></tr><tr><td colspan="2">Event has a PV with at least two tracks with $p_T > 400$ MeV.</td></tr><tr><td colspan="2">Event has at least one MS vertex.</td></tr><tr><td colspan="2">MS vertex matched to triggering muon RoI cluster [$\Delta R$(vertex, cluster) &lt; 0.4].</td></tr><tr><td colspan="2">For 2MSVx strategy: in the case of 2 muon RoI clusters, the second vertex should be matched to the second cluster.</td></tr><tr><td colspan="2">$300 \le n_{\text{MDT}} < 3000$</td></tr><tr><th>Barrel</th><th>End caps</th></tr><tr><td>MS vertex with $|\eta_{\text{vx}}| < 0.7$<br>$n_{\text{RPC}} \ge 250$</td><td>MS vertex with $1.3 < |\eta_{\text{vx}}| < 2.5$<br>$n_{\text{TGC}} \ge 250$</td></tr></table>

middle station of the MS. Requiring two displaced vertices significantly reduces the expected background. In addition, background from punch-through jets was further reduced using the isolation criteria described in Sec. VI C 1.

Residual background can arise from collision or non-collision processes and cannot be accurately simulated. Thus, data-driven methods were used to estimate the expected background, which also avoids systematic uncertainties due to the use of simulated events.

### A. Event selection

In order to improve the rejection of background from punch-through jets, the isolation criteria using the reconstructed objects described in Sec. VI C 1 were optimized for all the benchmark samples considered in this paper by comparing signal with multijet simulated events. The isolation criteria used for the 2MSVx strategy are summarized in Table IV, where $\Delta R$ is defined as the angular distance between the direction of the tracks or jets and the vertex axis. An MS vertex with tracks and/or jets satisfying these criteria was not considered in the analysis.

At least two isolated MS vertices must be present in the events. One MS vertex must be matched to the trigger-level muon RoI cluster [$\Delta R$(cluster, vertex) < 0.4]. If there were two distinct clusters, each MS vertex must be matched to one cluster. To ensure that the two MS vertices and/or two muon RoI trigger clusters do not come from the same background activity, the two vertices were required to be

separated by at least $\Delta R = 1.0$, which has minimal impact on the overall signal acceptance.

### B. MS vertex efficiency

The efficiency for vertex reconstruction is defined as the fraction of simulated LLP decays in the MS fiducial volume which match a reconstructed vertex passing the baseline event selection and satisfying the vertex isolation criteria [40]. A reconstructed vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency was parameterized as a function of the transverse ($L_{xy}$) and longitudinal ($|L_z|$) LLP decay position in the barrel and end caps, respectively. Figure 3(a) shows the efficiency for reconstructing a vertex in the MS barrel for a selection of benchmark samples. Figure 3(b) shows the efficiency for reconstructing a vertex in the MS end caps.

The MS barrel vertex reconstruction efficiency is 30%–40% near the outer edge of the hadronic calorimeter ($r \approx 4$ m) and it decreases substantially as the decay occurs closer to the middle station ($r \approx 7$ m). The decrease occurs because the charged hadrons and photons are not spatially separated and overlap when they traverse the middle station. This results in a reduction of the efficiencies for tracklet reconstruction and, consequently, vertex reconstruction. The efficiency for reconstructing vertices in the MS end caps reaches 70% for higher-mass benchmark models. Because there is no magnetic field in the region in which end cap tracklets are reconstructed, the vertex reconstruction algorithm does not have the constraints on charge and momentum that are present in the barrel. Consequently, the vertex reconstruction in the end caps is more efficient for signal, but also less robust in rejecting background events. More details are provided in Ref. [40].

### C. Background estimation

To estimate the expected background for the 2MSVx strategy, which comes mainly from punch-through jets, it is necessary to quantify the frequency with which the MS vertex algorithm reconstructs isolated vertices for nonsignal events. This number can be calculated from data using events with one isolated MS vertex which pass either the muon RoI cluster trigger or a zero-bias trigger. The expected background with two isolated MS vertices is calculated as follows:

<table><caption>TABLE IV. Summary of the isolation criteria used to select signal events for the 2MSVx strategy in the barrel and end caps regions. $\Delta R$ is defined as the angular distance between the direction of the tracks or jets and the vertex axis. MS vertices satisfying these criteria were not considered in the analysis.</caption><thead><tr><th>Isolation requirements for 2MSVx strategy</th><th>Barrel</th><th>End caps</th></tr></thead><tbody><tr><td>High-$p_T$ track isolation, ($p_T > 5$ GeV)</td><td>$\Delta R < 0.3$</td><td>$\Delta R < 0.6$</td></tr><tr><td>Low-$p_T$ track isolation, ($\Sigma p_T (\Delta R < 0.2)$)</td><td>$\Sigma p_T < 10$ GeV</td><td>$\Sigma p_T < 10$ GeV</td></tr><tr><td>Jet isolation</td><td>$\Delta R < 0.3$</td><td>$\Delta R < 0.6$</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>FIG. 3. Efficiency to reconstruct an MS vertex for some of the stealth SUSY and baryogenesis benchmark samples, for vertices that pass the baseline event selection and satisfy the vertex isolation criteria (no trigger selection is applied). (a) Barrel MS vertex reconstruction efficiency as a function of the transverse decay position of the LLP. (b) End cap MS vertex reconstruction efficiency as a function of the longitudinal decay position of the LLP relative to the center of the detector. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second layer of MDT chambers and L/S indicate whether they are in large or small sectors.</figcaption></figure>

$$N_{2Vx} = N^{1\text{cl}} \cdot P_{\text{noMStrig}}^{Vx} + N_{\text{1UMBcl}}^{2\text{cl}} \cdot P_{\text{Bcl}}^{Vx} + N_{\text{1UMEcl}}^{2\text{cl}} \cdot P_{\text{Ecl}}^{Vx}$$

The events selected by the muon RoI cluster trigger and containing only one MS vertex are separated into those containing only one cluster of muon RoIs ($N^{1\text{cl}}$), and those containing two muon RoI clusters, where only one cluster is matched to the reconstructed MS vertex and the other is unmatched in the barrel or end caps ($N_{\text{1UMBcl}}^{2\text{cl}}$, $N_{\text{1UMEcl}}^{2\text{cl}}$). The term $P_{\text{noMStrig}}^{Vx}$ is the probability of finding a vertex in events not selected by the muon RoI cluster trigger. This probability is determined from zero-bias events by dividing the number of good, isolated MS vertices not passing the muon RoI Cluster trigger by the total number of zero-bias events that satisfy standard event quality criteria. The terms $P_{\text{Bcl}}^{Vx}$ and $P_{\text{Ecl}}^{Vx}$ are the probabilities for finding an MS vertex given a muon RoI cluster in the barrel and end caps, respectively. Since zero events are observed with two trigger clusters and one vertex, the contribution of the $N^{2\text{cl}}$ terms is negligible.

Therefore, the number of two-MS-vertex events can be calculated as $N_{2Vx} = N^{1\text{cl}} \cdot P_{\text{noMStrig}}^{Vx}$.

Six good isolated MS vertices were found in 35 673 956 zero-bias triggered events, while 159 816 events with one isolated muon RoI cluster matched to a vertex were selected in the 2015 and 2016 datasets. The zero-bias sample has no overlap with the muon RoI cluster triggered events and contains zero events with more than one MS vertex. Contamination from signal events would result in over-estimation of the probabilities and resulting background rates. The probability $P_{\text{noMStrig}}^{Vx}$ is thus estimated to be $6/35673956 = (1.7 \pm 0.7) \times 10^{-7}$, where the uncertainty is statistical only. Therefore, the expected number of background events with one trigger cluster and two vertices

is evaluated as $(159816 \pm 400) \cdot (1.7 \pm 0.7) \times 10^{-7} = 0.027 \pm 0.011$, where the uncertainty is statistical only.

# IX. SINGLE-MS-VERTEX SEARCH

For models with two LLPs, the probability of having both LLPs decay inside the detector decreases for mean lab-frame decay lengths greater than ~5 m. Thus, extending sensitivity to shorter and longer proper lifetimes for a given model also requires a strategy of using only one reconstructed displaced decay [36]. In the regime of long lifetimes the single-vertex analysis in the MS has unique sensitivity compared to other displaced searches, although it is affected by higher levels of background. For a search with only one displaced object, a background determination method similar to the two-vertex search does not work since the ensemble of events with one isolated vertex, used to estimate the background, already contains the signal region for the 1MSVx+AO strategies. Instead, nonisolated vertices are used in a data-driven method to estimate the expected number of isolated fake vertices.

The following sections describe the event selection and background estimation for the 1MSVx+Jets and 1MSVx + $E_T^{\text{miss}}$ strategies. The events considered in these searches must satisfy the baseline selection criteria summarized in Sec. VII.

## A. Event selection

Two separate signal selections are used for the two topologies that are considered in the single-MS-vertex search.

### 1. 1MSVx+Jets strategy

The main criterion that is used to distinguish a signal MS vertex from background is its degree of isolation as
<figure><img src="image_5.png" /><figcaption>FIG. 4. Distributions of the isolation variable used to select signal events for the 1MSVx+Jet strategy, for the (a) barrel and (b) end caps. The black points are data collected in 2015 and 2016, while the dashed and solid lines show the distributions for four of the MC stealth SUSY signal samples. The black vertical lines show the selection cuts that define the signal region. The events in the plots satisfy the baseline selection criteria described in Sec. VII. Distributions are normalized to unity.</figcaption></figure>

described in Sec. VIII A. To characterize the degree of isolation with a single value, the variable $\Delta R_{min} = \min(\Delta R(\text{vertex}, \text{closest jet}), \Delta R(\text{vertex}, \text{closest track}))$ was defined. Figures 4(a) and 4(b) show the distributions of the isolation variable used for 1MSVx+Jets events for data and some of the MC benchmark samples for barrel and end cap vertices, respectively.

Another signal selection variable is the sum of the number of MDT hits and trigger hits (RPC and TGC in the barrel and end caps, respectively) in a cone around an MS vertex, since a signal event is expected to leave more hits than a background one. Figures 5(a) and 5(b) present the distributions of the number of MS hits variable used for the 1MSVx+Jets strategy for data collected during 2015 and 2016 and for some of the MC benchmark samples for barrel and end cap vertices, respectively.

Moreover, the two prompt jets produced by the gluino decays can be used to improve the signal selection for the stealth SUSY analysis. The second-highest (subleading) jet $E_T$ is generally above 150 GeV, though applying this requirement results in some loss of signal efficiency in the lowest-mass gluino sample ($m_{\tilde{\chi}'} = 250$ GeV). For events with a barrel MS vertex the $E_T$ of the leading and subleading jets was required to be above 150 GeV, while for events with an end cap MS vertex, a tighter requirement of 250 GeV was chosen due to the higher levels of background. Since the isolation variable depends on the $\Delta R$ between a jet and the vertex, jets chosen for this selection must have $\Delta R(\text{jet}, \text{vertex}) > 0.7$. This prevents the selection of a sample containing punch-through jets that leave a vertex in the MS. In signal events, this requirement has minimal effect, since jets and vertices originate from

<figure><img src="image_6.png" /><figcaption>FIG. 5. Distributions of the number of MS hits associated with the displaced vertex, used to select signal events for the 1MSVx+Jet strategy, for the (a) barrel and (b) end caps. The black points are data collected in 2015 and 2016, while the dashed and solid lines show the distributions for four of the MC stealth SUSY signal samples. The black vertical lines show the selection cuts that define the signal region. The events in the plots satisfy the baseline selection criteria described in Sec. VII. Distributions are normalized to unity.</figcaption></figure>
<table><caption>TABLE V. Summary of the signal selection for the 1MSVx+Jets strategy. An MS vertex satisfying these criteria is selected.</caption><thead><tr><th colspan="2">Event passes baseline selection</th></tr><tr><th>Barrel</th><th>End caps</th></tr></thead><tbody><tr><td>n<sub>MDT</sub> + n<sub>RPC</sub> > 2000</td><td>n<sub>MDT</sub> + n<sub>TGC</sub> > 2500</td></tr><tr><td>ΔR<sub>min</sub> > 0.3</td><td>ΔR<sub>min</sub> > 0.4</td></tr><tr><td>Two jets with E<sub>T</sub> > 150 GeV, ΔR(jet, Vx) > 0.7</td><td>Two jets with E<sub>T</sub> > 250 GeV, ΔR(jet, Vx) > 0.7</td></tr></tbody></table>

different particles and thus tend to be well separated. In data events, the main background is from multijet production, and thus if a jet is near a vertex it is generally well within $\Delta R = 0.7$. The selection on the two prompt jets described above is used to define two regions: one signal-dominated, and one background-dominated used to validate the data-driven background estimation.

The signal selection for the 1MSVx+Jets strategy was optimized by examining the signal acceptance and background rejection using data from the background-dominated region defined by the $E_T$ values of the two prompt jets. The minimum values required for isolation $\Delta R_{min}$ are 0.3 and 0.4 for the barrel and end caps, respectively; a minimum of 2000 MDT + RPC hits in the barrel and 2500 MDT + TGC hits in the end caps is required. Table V summarizes the signal selection for the 1MSVx+Jets strategy.

## 2. 1MSVx + $E_T^{miss}$ strategy

The one-vertex searches for the scalar portal model with $m_\Phi = 125$ GeV and the Higgs portal baryogenesis model are particularly challenging due to the absence of any distinctive associated objects produced with the LLPs, such as the two prompt jets in the stealth SUSY model.

All the events used for the 1MSVx + $E_T^{miss}$ strategy are required to have a $E_T^{miss} > 30$ GeV, and in the end caps, only vertices with at least five tracklets are considered, which further reduces the higher level of background present in this region.

The same isolation variable, $\Delta R_{min}$, defined for the 1MSVx+Jets strategy, is also used to select signal events for the 1MSVx + $E_T^{miss}$ strategy, although for the latter the isolation criteria are computed while placing a looser selection on the jets, as described in Sec. VI C 1. Figures 6(a) and 6(b) show the distributions of the isolation variable used for the 1MSVx + $E_T^{miss}$ strategy for data and some of the MC benchmark samples for barrel and end cap vertices, respectively.

The angle in the transverse plane between the $E_T^{miss}$ vector and the direction of the displaced vertex measured from the origin of the detector coordinate system, $|\Delta\phi(E_T^{miss}, \text{MSVx})|$, was also used to distinguish signal from background because for signal vertices the $E_T^{miss}$ vector tends to be aligned with the direction of the displaced vertex. Figures 7(a) and 7(b) report the distributions of the $|\Delta\phi(E_T^{miss}, \text{MSVx})|$ variable, used to select signal events for the 1MSVx + $E_T^{miss}$ strategy, for data and some of the MC benchmark samples for barrel and end cap vertices, respectively.

The number of MDT hits and trigger hits (RPC and TGC hits in the barrel and end caps, respectively) in a $\Delta R$ cone around the MS vertex was used to define one signal-dominated region and one background-dominated region that was used to validate the data-driven background estimation. The selection requirements that define the two regions were optimized in order to ensure a sufficient signal acceptance and background rejection. For events with a barrel MS vertex the number of hits ($n_{MDT} + n_{RPC}$) is required to be greater than 1200, while for events with an

<figure><img src="image_7.png" /><figcaption>FIG. 6. Distributions of the isolation variable used to select signal events for the 1MSVx + $E_T^{miss}$ strategy, for the (a) barrel and (b) end caps. The black points are data collected in 2015 and 2016, while the solid lines show the distributions for four MC signal samples. The black vertical lines show the selection cuts that define the signal region. The events in the plots satisfy the baseline selection criteria described in Sec. VII and have $E_T^{miss} > 30$ GeV. Distributions are normalized to unity.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>FIG. 7. Distributions of the angle in the transverse plane between the $E_T^{\text{miss}}$ and the displaced vertex $|\Delta\phi(E_T^{\text{miss}}, \text{MSVx})|$ used to select signal events for the $1\text{MSVx} + E_T^{\text{miss}}$ strategy, for the (a) barrel and (b) end caps. The black points are data collected in 2015 and 2016, while the dashed and solid lines show the distributions for four MC signal samples. The black vertical lines show the selection cuts that define the signal region. The events in the plots satisfy the baseline selection criteria described in Sec. VII and have $E_T^{\text{miss}} > 30$ GeV. Distributions are normalized to unity.</figcaption></figure>

end cap MS vertex, the number of hits ($n_{\text{MDT}} + n_{\text{TGC}}$) must exceed 1500.

The signal region selection for the $1\text{MSVx} + E_T^{\text{miss}}$ strategy was optimized using signal acceptance versus background rejection estimated from data in the background-dominated region. The selection requires values of at least 0.8 for isolation $\Delta R_{\text{min}}$ and 1.2 for $|\Delta\phi(E_T^{\text{miss}}, \text{MSVx})|$, for both the barrel and end caps. Due to the higher levels of background, the selection requirement imposed on the isolation is stricter than the ones used for the $1\text{MSVx}+\text{Jets}$ strategy. Table VI summarizes the signal selection for the $1\text{MSVx} + E_T^{\text{miss}}$ strategy.

### 3. MS vertex efficiency

The efficiency for vertex reconstruction is defined as the fraction of simulated LLP decays in the MS fiducial volume which match a reconstructed vertex satisfying the signal selection criteria. A reconstructed vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. Figures 8(a) and 8(b) show the efficiency for reconstructing a vertex for a selection of benchmark samples in the MS barrel and

end caps, respectively. Vertices selected for the stealth SUSY and baryogenesis benchmark samples must satisfy the signal selection criteria described in Secs. IX A 1 and IX A 2, respectively (no trigger selection is applied). The behavior of the MS vertex reconstruction efficiency as a function of the LLP proper lifetime is similar to that shown in Fig. 3, although the efficiency values are different due to the different event selection. The MS barrel vertex reconstruction efficiency is 15%–25% near the outer edge of the hadronic calorimeter ($r \approx 4$ m) and it substantially decreases as the decay occurs closer to the middle station ($r \approx 7$ m). The efficiency for reconstructing vertices in the MS end caps reaches 40% for baryogenesis and stealth SUSY high-mass gluino benchmark models. The lower efficiency for the stealth SUSY sample with $m_{\tilde{g}} = 500$ GeV is due to the selection requirement on the $E_T$ values of the two prompt jets, which is not optimal for the lower masses.

### B. Background estimation

The ABCD method developed for the $1\text{MSVx}+\text{AO}$ strategies uses two, uncorrelated vertex-based variables to create a two-dimensional plane that is split into four parts: region A is where most signal events are located, and three control regions (B, C, and D) that contain mostly background. The number of background events in A can be predicted from the population of the other three regions: $N_A = N_B \times N_C/N_D$, assuming negligible leakage of signal into regions B, C, and D. This calculation is performed in two separate regions: one background-dominated validation region (VR) and one signal region (SR). Two different ABCD planes were defined for the $1\text{MSVx}+\text{Jets}$ and $1\text{MSVx} + E_T^{\text{miss}}$ strategies. Figures 9(a) and 9(b) show the distribution of barrel vertices for data in the ABCD

<table><caption>TABLE VI. Summary of the signal selection for the $1\text{MSVx} + E_T^{\text{miss}}$ strategy. An MS vertex satisfying these criteria is selected.</caption><thead><tr><th colspan="2">Event passes baseline selection</th></tr><tr><th>$E_T^{\text{miss}} > 30$ GeV</th><th>$|\Delta\phi(E_T^{\text{miss}}, \text{MSVx})| < 1.2$</th></tr><tr><th>$\Delta R_{\text{min}} > 0.8$</th><th></th></tr><tr><th>Barrel</th><th>End caps</th></tr></thead><tbody><tr><td>$n_{\text{MDT}} + n_{\text{RPC}} > 1200$</td><td>$n_{\text{MDT}} + n_{\text{TGC}} > 1500$<br>$n_{\text{tracklets}} \ge 5$</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>FIG. 8. Efficiency to reconstruct an MS vertex for some of the stealth SUSY and baryogenesis benchmark samples, for vertices that satisfy the signal selection criteria for the 1MSVx+AO strategies (no trigger selection is applied). (a): Barrel MS vertex reconstruction efficiency as a function of the transverse decay position of the LLP. (b): End cap MS vertex reconstruction efficiency as a function of the longitudinal decay position of the LLP. The vertical lines show the relevant detector boundaries.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>FIG. 9. Distribution of barrel vertices in the ABCD plane for the SR and the definition of the four subregions for the (a) 1MSVx+Jets strategy and (b) $1\text{MSVx} + E_T^{\text{miss}}$ strategy.</figcaption></figure>

plane and the definition of the four subregions for the 1MSVx+Jets and $1\text{MSVx} + E_T^{\text{miss}}$ strategies, respectively.

The ABCD method relies on there being only one source of background, or multiple sources that have identical distributions in the ABCD plane. In general, noncollision background, which does not originate from the *pp* interaction point, will have a different distribution in the ABCD plane. To determine the noncollision background contamination, data collected in LHC empty bunch crossings throughout the 2016 data-taking period were used to estimate the number of noncollision background vertices in coincidence with events otherwise satisfying the single-vertex selection criteria. The empty bunch crossing trigger was not available in 2015, but the noncollision background's relative contribution is expected to be the same. The fraction of expected noncollision background vertices passing the final signal selection is negligible for the

$1\text{MSVx}+\text{Jets}$ strategy while for the $1\text{MSVx} + E_T^{\text{miss}}$ strategy it corresponds to 0.8% (0.6%) of the total number of background events expected in the SR in the barrel (end caps). Noncollision background events are equally distributed in the ABCD plane and they are taken into account as a systematic uncertainty.

Signal contamination in the VR, which can bias the ABCD method validation, was tested and found to be negligible for both the $1\text{MSVx}+\text{Jets}$ and $1\text{MSVx} + E_T^{\text{miss}}$ strategies.

## 1. ABCD plane for $1\text{MSVx}+\text{Jets}$ strategy

For stealth SUSY-like events the ABCD plane for background estimation is constructed with the isolation $\Delta R_{\text{min}}$ variable, and the sum of the numbers of MDT and trigger hits associated with the MS vertex, described in
<table><caption>TABLE VII. Summary of the definition of the VR and SR used for the ABCD method for the 1MSVx+Jets strategy.</caption><thead><tr><th>Region</th><th>Criteria</th></tr></thead><tbody><tr><td>Barrel</td><td>VR: 50 &lt; E<sub>T,subleading</sub> &lt; 150 GeV, E<sub>T,leading</sub> &gt; 150 GeV<br>SR: E<sub>T,leading</sub> &gt; 150 GeV, E<sub>T,subleading</sub> &gt; 150 GeV</td></tr><tr><td>End caps</td><td>VR: 100 &lt; E<sub>T,subleading</sub> &lt; 250 GeV, E<sub>T,leading</sub> &gt; 250 GeV<br>SR: E<sub>T,leading</sub> &gt; 250 GeV, E<sub>T,subleading</sub> &gt; 250 GeV</td></tr></tbody></table>

<table><caption>TABLE VIII. Event counts in each of the four regions of the 1MSVx+Jets ABCD plane and expected number in region A obtained using 2015 and 2016 data from the VR. Both the statistical and systematic errors of the background expectation are reported.</caption><thead><tr><th>VR</th><th>A</th><th>Expected background</th><th>B</th><th>C</th><th>D</th></tr></thead><tbody><tr><td>Barrel</td><td>46</td><td>45 ± 5(stat) ± 9(syst)</td><td>7, 748</td><td>90</td><td>15 620</td></tr><tr><td>End caps</td><td>11</td><td>15 ± 3(stat) ± 12(syst)</td><td>3, 335</td><td>20</td><td>4, 365</td></tr></tbody></table>

Sec. IX A 1. These two variables form the x axis and y axis of the ABCD plane, respectively. The SR and the VR are built using the $E_T$ values of the leading and subleading jets and their definition is summarized in Table VII.

Signal contamination in regions B, C, and D in the SR of the 1MSVx+Jets ABCD plane was found to be negligible. Signal contamination in the VR is negligible for benchmark samples with $m_{\tilde{g}} > 500$ GeV, and for $m_{\tilde{g}} = 500$ GeV in the barrel region. However, the end caps region for $m_{\tilde{g}} = 500$ GeV and both barrel and end caps regions for $m_{\tilde{g}} = 250$ GeV have non-negligible signal contamination in the VR and thus are not included in the 1MSVx+Jets strategy. Both the VR and SR show very low linear correlation between the two variables: -0.01 (-0.03) for the VR, and -0.01 (-0.05) for the SR in the barrel (end caps).

Table VIII summarizes the observed and expected numbers of events in the four regions of the ABCD plane constructed using events from the VR. The number of observed events in region A is 46 and 11 in the barrel and end caps, respectively. These are in agreement with the

$45 \pm 5(stat) \pm 9(syst)$ and $15 \pm 3(stat) \pm 12(syst)$ events predicted by the ABCD method in the barrel and end caps, respectively. The systematic uncertainty associated with the background estimation reported above is described in detail in Sec. X B.

## 2. ABCD plane for $1\text{MSVx} + E_T^{\text{miss}}$ strategy

For the $1\text{MSVx} + E_T^{\text{miss}}$ strategy the two variables used to define the ABCD plane are the isolation $\Delta R_{\text{min}}$ and the angle in the transverse plane between the $E_T^{\text{miss}}$ vector and the displaced vertex $|\Delta\phi(E_T^{\text{miss}}, \text{MSVx})|$, described in Sec. IX A 2. The SR and VR are defined using the sum of the numbers of MDT and trigger hits in a cone around the MS vertex and their definition is summarized in Table IX. Signal contamination in the VR is negligible.

Both the VR and SR show very low linear correlation between the two variables: 0.03 (0.01) for the VR, and 0.02 (-0.01) for the SR in the barrel (end caps).

Table X summarizes the observed and expected numbers of events in the four regions of the ABCD plane constructed using events from the VR. The number of observed events in region A is 334 and 1,107 for the barrel and end caps, respectively. These are in agreement with the $319 \pm 29(stat) \pm 38(syst)$ and $1,153 \pm 46(stat) \pm 69(syst)$ events predicted by the ABCD method in the barrel and end caps, respectively. The systematic uncertainty associated with the background estimation reported above is described in detail in Sec. X B.

For the $1\text{MSVx} + E_T^{\text{miss}}$ strategy the signal contamination in regions B, C, and D of the SR ABCD plane is not

<table><caption>TABLE IX. Summary of the definition of the VR and SR used for the ABCD method for the $1\text{MSVx} + E_T^{\text{miss}}$ strategy.</caption><thead><tr><th>Region</th><th>Criteria</th></tr></thead><tbody><tr><td>Barrel</td><td>VR: n<sub>MDT</sub> + n<sub>RPC</sub> &lt; 1200<br>SR: n<sub>MDT</sub> + n<sub>RPC</sub> &gt; 1200</td></tr><tr><td>End caps</td><td>VR: n<sub>MDT</sub> + n<sub>TGC</sub> &lt; 1500<br>SR: n<sub>MDT</sub> + n<sub>TGC</sub> &gt; 1500</td></tr></tbody></table>

<table><caption>TABLE X. Event counts in each of the four regions of the $1\text{MSVx} + E_T^{\text{miss}}$ ABCD plane and expected number in region A obtained using 2015 and 2016 data from the VR. Both the statistical and systematic errors of the background expectation are reported.</caption><thead><tr><th>VR</th><th>A</th><th>Expected background</th><th>B</th><th>C</th><th>D</th></tr></thead><tbody><tr><td>Barrel</td><td>334</td><td>319 ± 29(stat) ± 38(syst)</td><td>119</td><td>67 980</td><td>25 380</td></tr><tr><td>End caps</td><td>1,107</td><td>1,153 ± 46(stat) ± 69(syst)</td><td>639</td><td>56 970</td><td>31 570</td></tr></tbody></table>
negligible. The traditional method, which estimates the background in region A by taking the ratio of events in the adjacent regions, breaks down in the presence of signal in the control regions. An ABCD-likelihood method addresses this issue because it estimates the background in region A by fitting simultaneously background and expected signal events in the four regions of the ABCD plane. A likelihood function is formed from the product of four Poisson functions, one for each region A, B, C and D, describing signal and background expectations. The likelihood takes the form

$$ \mathcal{L}(n_A, n_B, n_C, n_D | s, b, \tau_B, \tau_C) = \prod_{i=A,B,C,D} \frac{e^{-N_i} N_i^{n_i}}{n_i!}, \quad (1) $$

where $n_A, n_B, n_C$ and $n_D$ are the four observables that denote the number of events observed in each region in data. The $N_i$ are linear combinations of the signal and background expectation in each region, defined as follows:

$$ \begin{aligned} N_A &= s + b, \\ N_B &= s\epsilon_B + b\tau_B, \\ N_C &= s\epsilon_C + b\tau_C, \\ N_D &= s\epsilon_D + b\tau_B\tau_C, \end{aligned} \quad (2) $$

where $s$ is the signal yield, $b$ the estimated background in region A, $\epsilon_i$ the signal contamination derived from MC simulation, and $\tau_B$ and $\tau_C$ are the coefficients that relate the number of background events in region A to the other regions. The $s, b$ and $\tau_i$ values are allowed to float in the simultaneous fit to the four data regions.

# X. SYSTEMATIC UNCERTAINTIES

In this section, experimental and theoretical systematic uncertainties associated with the signal predictions and background estimation are described.

## A. Uncertainties in the signal predictions

The signal efficiency systematic uncertainties are dominated by the modeling of the signal physics processes, pileup and detector response and the extrapolation of the expected number of signal events as a function of the LLP proper lifetime.

One of the sources of systematic uncertainty associated with the muon RoI cluster trigger stems from the trigger scale factors, and it was estimated by moving them up and down by their statistical uncertainty. The trigger efficiency values obtained with these modified MC samples were compared with the efficiency of the nominal sample, and the difference was taken as the systematic uncertainty. A similar strategy was also adopted to estimate the trigger systematic uncertainty associated with the modeling of the minimum-bias interactions used to emulate pileup and the

systematic uncertainty due to the PDF used to generate signal MC events. For the latter, the PDF uncertainty was obtained by considering the envelope of the uncertainty of the PDF set. The total systematic uncertainty of the signal efficiency for passing the muon RoI cluster trigger was obtained by summing in quadrature the contributions described above and varies from 1% to 12%, depending on the sample and detector region. The total systematic uncertainty of the signal efficiency for reconstructing an MS vertex was obtained by summing in quadrature the contributions coming from pileup and PDF uncertainties (evaluated with the similar procedure described above) and varies from 0.07% to 5.5%, depending on the sample and detector region. The systematic uncertainty associated with the prompt jets used for the 1MSVx+Jets strategy originates from jet energy scale, PDF and pileup uncertainties, and was evaluated with the similar procedure described earlier. The overall systematic uncertainty of the two-jet efficiency was determined by adding each component in quadrature and varies from 0.3% to 9.8%, depending on the sample and detector region. Systematic uncertainties due to the $E_T^{\text{miss}}$ computation for the 1MSVx + $E_T^{\text{miss}}$ strategy are negligible. By comparing the average number of muon segments in a cone around punch-through jets in data and MC simulation, systematic uncertainties associated with the mismodeling of the MS vertex reconstruction in signal events were found to be negligible.

For each of the scalar boson samples, excluding $m_s = 100$ GeV, two proper lifetime points were fully simulated: one nominal sample and a secondary sample with longer proper lifetime, as described in Sec. V. The secondary sample is used to validate the extrapolation procedure, and a systematic uncertainty is assigned to each sample due to the nonclosure of the extrapolation procedure. This is calculated by determining the fraction of events passing all analysis cuts in each MC sample generated with 9 m lab-frame decay length and comparing them to the expected global efficiencies obtained with the extrapolation procedure. The systematic uncertainty varies from 2% to 37%, depending on the sample. Stealth SUSY benchmark samples have events with kinematic behavior similar to that of events in the high-mass scalar boson samples and they are thus assigned the average systematic uncertainty of those samples (11%). The kinematics of events in the baryogenesis samples is very close to the $\Phi(125) \to ss$ kinematics and a comparable systematic uncertainty related to the extrapolation procedure is assumed. For that reason, for all the baryogenesis benchmark samples the average systematic uncertainty of the five $\Phi(125) \to ss$ samples (32%) is used.

The uncertainty in the combined 2015 + 2016 integrated luminosity is 2.1%. It is derived, following a methodology similar to that detailed in Ref. [74], using the LUCID-2 detector for the baseline luminosity measurements [75], from calibration of the luminosity scale using x-y beam-separation scans.
<figure><img src="image_11.png" /><figcaption>FIG. 10. ABCD plane for the 1MSVx + $E_T^{\text{miss}}$ strategy in the barrel and definition of the regions excluded from the estimation of the background's systematic uncertainty.</figcaption></figure>

## B. Uncertainties in the background prediction

The systematic uncertainty associated with the background estimation of the ABCD method was evaluated using data events passing the validation region selection. Events falling in each of the two bands of the ABCD plane that surround region A, and that are shown in Fig. 10 for the 1MSVx + $E_T^{\text{miss}}$ strategy in the barrel, were excluded and the expected background in the signal region was reevaluated. The definition of region A was not modified in this procedure. The relative variation with respect to the observed events in region A was then evaluated. The size of the two bands is defined by the resolution of the two variables used to define the ABCD plane. The maximum value of the two results obtained by separately removing bands with widths of $1\sigma$ and $2\sigma$ was taken as the systematic uncertainty associated with the background estimation. Since the numbers of events in regions A and B are small, a bootstrap method [76,77] was used to determine the statistical uncertainty of the background estimate.

For the 1MSVx+Jets strategy the size of the two removed bands corresponds to 0.1 and 0.2 for the isolation and 100 and 200 for the number of MS hits. In the end cap validation region the uncertainty in the relative difference obtained from the bootstrap method is much higher than the statistical uncertainty of the background estimate in region A, while in the barrel the two are comparable. The final systematic uncertainty of the background estimate was taken to be the maximum of the statistical uncertainty from

the bootstrap method and the statistical uncertainty from the background prediction in region A, and corresponds to 20% in the barrel and 81% in the end caps.

For the 1MSVx + $E_T^{\text{miss}}$ strategy the size of the two removed bands corresponds to 0.1 and 0.2 for the isolation variable and 0.2 and 0.4 for $|\Delta\phi(E_T^{\text{miss}}, \text{MSVx})|$. The relative difference in the validation region is lower than the statistical uncertainty on the relative difference obtained from the bootstrap method in both the barrel and end caps. The final systematic uncertainty of the background estimate is taken to be the maximum of the statistical uncertainty from the bootstrap method and the statistical uncertainty of the background prediction in region A, corresponding to 12% and 6% for barrel and end caps, respectively. The small contribution from noncollision background was taken into account as a systematic uncertainty of the background estimate, as discussed in Sec. IX B.

## XI. RESULTS

For the 2MSVx strategy, $0.027 \pm 0.011$ background events are expected. After unblinding, no events passing the full signal selection were found.

For the 1MSVx+AO strategies, the number of observed events in the four regions of the ABCD plane and the background prediction in region A for events passing the SR selection are summarized in Table XI. No significant excess above the predicted number of background events is found.

Upper limits on the production cross section times branching fraction were derived using the CLs prescription [78], implemented with the ROOSTAT [79] and HISTFACTORY [80] packages using a profile likelihood function [81]. For the 2MSVx and 1MSVx+Jets strategies the likelihood includes a Poisson probability term describing the total number of observed events. For the 1MSVx + $E_T^{\text{miss}}$ strategy the likelihood described in Sec. IX B 2 was used. For scalar boson benchmark samples with $m_\Phi \neq 125$ GeV, upper limits were set on $\sigma \times B$, where B represents the branching fraction for $\Phi \to ss$ assuming 100% branching fraction into fermion pairs. For scalar boson benchmark samples with $m_\Phi = 125$ GeV, upper limits were set on $\sigma/\sigma_{\text{SM}} \times B$, where $\sigma_{\text{SM}}$ is the SM Higgs boson production cross section, 48.58 pb [82]. For the stealth SUSY benchmarks, upper limits were set on $\sigma/\sigma_{\text{SUSY}} \times B$, where $\sigma_{\text{SUSY}}$ is the SUSY production cross

<table><caption>TABLE XI. Event counts in each of the four regions of the ABCD plane and expected number in region A for the SR, using the 2015 and 2016 datasets. Both the statistical and systematic errors of the background expectation are reported.</caption><thead><tr><th>Strategy</th><th>Region</th><th>A</th><th>Expected background</th><th>B</th><th>C</th><th>D</th></tr></thead><tbody><tr><td rowspan="2">1MSVx + Jets</td><td>Barrel</td><td>14</td><td>15 ± 3(stat) ± 3(syst)</td><td>2,057</td><td>25</td><td>3,414</td></tr><tr><td>End caps</td><td>4</td><td>11 ± 3(stat) ± 9(syst)</td><td>560</td><td>15</td><td>761</td></tr><tr><td rowspan="2">1MSVx + <i>E</i><sub>T</sub><sup>miss</sup></td><td>Barrel</td><td>224</td><td>243 ± 38(stat) ± 29(syst)</td><td>42</td><td>132 000</td><td>22 800</td></tr><tr><td>End caps</td><td>489</td><td>497 ± 51(stat) ± 30(syst)</td><td>94</td><td>165 800</td><td>31 390</td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>FIG. 11. Observed limits for (a) stealth SUSY and (b) $\Phi(125) \to ss$ benchmark samples obtained from the combination of 2MSVx and 1MSVx+AO strategies.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>FIG. 12. Observed limits for scalar boson benchmark samples with $m_\Phi = 100$ GeV and $m_\Phi > 125$ GeV obtained from the 2MSVx strategy. The different plots show the results obtained for different $\Phi$ mass points.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>FIG. 13. Observed limits obtained for all the baryogenesis benchmark samples. Limits for the on-shell $h \to \chi\chi\chi$ production in (a), (b) and (c) are obtained from the combination of 2MSVx and 1MSVx + $E_T^{\text{miss}}$ strategies, while for the off-shell sample ($m_\chi = 100$ GeV) the limits shown are obtained from the 2MSVx strategy only. Limits for the $\chi \to \tau\tau\nu$ channel shown in (d) are obtained from the 2MSVx strategy only. For reference, the black solid and dashed lines show respectively the 100% and 10% $\sigma \times B$ assuming the SM Higgs boson total production cross section, while the red dash-dotted line reports the 100% $\sigma \times B$ for the off-shell $h \to \chi\chi\chi$ production with $m_\chi = 100$ GeV, which is equal to 7 fb.</figcaption></figure>

section for $pp \to \tilde{g}\tilde{g}$ [83] and B represents the branching fraction for $\tilde{g} \to \tilde{S}g$, assuming that both $\tilde{S} \to \tilde{S}\tilde{G}$ and $S \to gg$ have 100% branching fraction.

Figures 11–13 show the observed limits for all the MC benchmark samples considered in this paper. The limits were obtained from the combination of 2MSVx and 1MSVx+AO strategies, performing a simultaneous fit of the 2MSVx and 1MSVx+AO likelihood functions, except for the scalar boson samples with $m_\Phi = 100$ GeV and $m_\Phi > 125$ GeV, stealth SUSY with $m_{\tilde{g}} = 250$ GeV (both barrel and end caps regions) and $m_{\tilde{g}} = 500$ GeV (end caps region), baryogenesis with $m_\chi = 100$ GeV and baryogenesis $\chi \to \tau\tau\nu$ benchmark samples. In these cases, the ABCD method developed for the analysis reported in this paper was found to be not optimal due to a large contamination by signal events in the VR or small signal-background separation for one of the variables of the ABCD plane. For those samples, the 2MSVx strategy provides strong limits and only those results are presented in this paper.

Table XII summarizes the lifetime ranges excluded by the analysis presented in this paper for branching fractions of 10% and 1% for the scalar boson with $m_\Phi = 125$ GeV decaying into two long-lived scalars. The results are substantially improved compared to the Run 1 analysis, where for 25 and 40 GeV long-lived scalar masses the $c\tau$

<table><caption>TABLE XII. Ranges of mean proper lifetime excluded at 95% C.L. for scalar boson benchmark models with $m_\Phi = 125$ GeV, assuming production cross sections equal to 10% or 1% of the SM Higgs boson production cross section [82] for the combination of 2MSVx and 1MSVx + $E_T^{\text{miss}}$ strategies.</caption><thead><tr><th rowspan="2">Φ(125) → ss<br>m<sub>s</sub> [GeV]</th><th colspan="2">Excluded cτ range [m]</th></tr><tr><th>10%</th><th>1%</th></tr></thead><tbody><tr><td>5</td><td>0.04–10.8</td><td>0.1–1.6</td></tr><tr><td>8</td><td>0.07–15</td><td>0.14–3.8</td></tr><tr><td>15</td><td>0.1–58</td><td>0.22–10.8</td></tr><tr><td>25</td><td>0.2–149</td><td>0.4–25</td></tr><tr><td>40</td><td>0.3–221</td><td>0.7–39</td></tr></tbody></table>
ranges excluded for 1% branching fraction were respec-
tively 1.10–5.35 m and 2.82–7.45 m, while for lower long-
lived scalar masses the Run 1 analysis did not have
sensitivity at this level.

## XII. SUMMARY

This paper presents the results of a search for long-lived
neutral particles using 36.1 fb⁻¹ of proton-proton collisions
at √s = 13 TeV recorded at the LHC by the ATLAS
detector in 2015 and 2016. Three separate strategies have
been considered: two displaced vertices in the muon
spectrometer (MS), one displaced vertex in the MS with
two additional prompt jets, and one displaced vertex in the
MS with E_tmiss > 30 GeV. The observed number of events
are consistent with the expected background and exclusion
limits on the LLP production cross section as a function
of its proper lifetime were computed for the theoretical
benchmark models.

The results reported here are interpreted in terms of a
scalar portal model similar to hidden-valley models where a
boson can decay into two long-lived scalars, a stealth
SUSY model where each of the two long-lived singlino are
produced from the gluino in association with a prompt jet,
and a Higgs-boson-mediated baryogenesis model where
the long-lived χ can decay into jets or leptons that violate
the baryon and/or lepton number conservation.

The two-vertex search was performed with the same
strategy as adopted in Run 1 and benefits from very low
background, but at large (cτ) its sensitivity scales as
1/(cτ)², where τ is the proper lifetime of the LLP. The
increased statistics and the analysis enhancements have
improved the cross-section sensitivity for some of the
Φ → ss decays by about an order of magnitude compared
to the ATLAS Run 1 analysis, and extended the sensitivity
for the stealth SUSY model to higher gluino masses that
could not be reached with the Run 1 search.

The one-vertex search extends the sensitivity, which scales
as 1/(cτ) at large (cτ), to much longer lifetimes. For the low-
mass samples [Φ(125) → ss and Higgs portal baryogenesis]
the sensitivity at shorter lifetimes is weaker than that attained
with the two-MS-vertex search, while for the stealth SUSY
model in the high-mass regime (m<sub>˜g</sub> > 500 GeV) the con-
tribution of the one-MS-vertex search is dominant in the
whole spectrum of proper lifetimes.

ACKNOWLEDGMENTS

We thank CERN for the very successful operation of the
LHC, as well as the support staff from our institutions
without whom ATLAS could not be operated efficiently.
We acknowledge the support of ANPCyT, Argentina;
Yerevan Physics Institute, Armenia; ARC, Australia;

BMFWF and FWF, Austria; Azerbaijan National
Academy of Sciences, Azerbaijan; State Science and
Technology Committee, Belarus; CNPq and FAPESP,
Brazil; NSERC, NRC and CFI, Canada; CERN;
CONICYT, Chile; CAS, MOST and NSFC, China;
COLCIENCIAS, Colombia; MSMT CR, MPO CR and
VSC CR, Czech Republic; DNRF and Danish Natural
Science Research Council, Denmark; Institut national de
physique nucléaire et de physique des particules, Centre
National de la Recherche Scientifique, Institut de recherche
sur les lois fondamentales de l'Univers, Direction des
Sciences de la Matière, Commissariat à l'Énergie
Atomique, France; Shota Rustaveli National Science
Foundation of Georgia, Georgia; BMBF, HGF, and
MPG, Germany; General Secretariat for Research and
Technology, Greece; RGC, Hong Kong SAR, China; ISF
and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS,
Japan; CNRST, Morocco; NWO, Netherlands; RCN,
Norway; MNiSW and NCN, Poland; FCT, Portugal;
Ministry of National Education, Institute of Atomic
Physics, Romania; Ministry of Education and Science of
the Russian Federation of Russia and NRC KI, Russian
Federation; Joint Institute for Nuclear Research; Ministry
of Education, Science and Technological Development,
Serbia; Ministry of Education, Science, Research and
Sport, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF,
South Africa; MINECO, Spain; Swedish Research Council
and Wallenberg Foundation, Sweden; Secretariat for
Education and Research, SNSF and Cantons of Bern
and Geneva, Switzerland; MOST, Taiwan; TAEK,
Turkey; STFC, United Kingdom; DOE and NSF, United
States of America. In addition, individual groups and
members have received support from BCKDF,
CANARIE, CRC and Compute Canada, Canada; COST,
ERC, ERDF, Horizon 2020, and Marie Skłodowska-Curie
Actions, European Union; Investissements d'Avenir Labex
and Idex, ANR, France; DFG and AvH Foundation,
Germany; Herakleitos, Thales and Aristeia programmes
co-financed by EU-ESF and the Greek NSRF, Greece;
BSF-NSF and GIF, Israel; CERCA Programme Generalitat
de Catalunya, Spain; The Royal Society and Leverhulme
Trust, United Kingdom. The crucial computing support
from all WLCG partners is acknowledged gratefully, in
particular from CERN, the ATLAS Tier-1 facilities at
TRIUMF (Canada), NDGF (Denmark, Norway,
Sweden), CC-IN2P3 (France), KIT/GridKA (Germany),
INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain),
ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2
facilities worldwide and large non-WLCG resource pro-
viders. Major contributors of computing resources are
listed in Ref. [84].
[1] A. Fradette and M. Pospelov, BBN for the LHC: Constraints on lifetimes of the Higgs portal scalars, Phys. Rev. D **96**, 075033 (2017).

[2] A. Arvanitaki, N. Craig, S. Dimopoulos, and G. Villadoro, Mini-split, J. High Energy Phys. 02 (2013) 126.

[3] N. Arkani-Hamed, A. Gupta, D. E. Kaplan, N. Weiner, and T. Zorawski, Simply unnatural supersymmetry, arXiv:1212.6971.

[4] G. F. Giudice and R. Rattazzi, Theories with gauge mediated supersymmetry breaking, Phys. Rep. **322**, 419 (1999).

[5] R. Barbier et al., R-parity violating supersymmetry, Phys. Rep. **420**, 1 (2005).

[6] C. Csaki, E. Kuflik, and T. Volansky, Dynamical R-Parity Violation, Phys. Rev. Lett. **112**, 131801 (2014).

[7] J. Fan, M. Reece, and J. T. Ruderman, Stealth supersymmetry, J. High Energy Phys. 11 (2011) 012.

[8] J. Fan, M. Reece, and J. T. Ruderman, A stealth supersymmetry sampler, J. High Energy Phys. 07 (2012) 196.

[9] Z. Chacko, D. Curtin, and C. B. Verhaaren, A quirky probe of neutral naturalness, Phys. Rev. D **94**, 011504 (2016).

[10] G. Burdman, Z. Chacko, H.-S. Goh, and R. Harnik, Folded supersymmetry and the LEP paradox, J. High Energy Phys. 02 (2007) 009.

[11] H. Cai, H.-C. Cheng, and J. Terning, A quirky little Higgs model, J. High Energy Phys. 05 (2009) 045.

[12] Z. Chacko, H.-S. Goh, and R. Harnik, Natural Electroweak Breaking from a Mirror Symmetry, Phys. Rev. Lett. **96**, 231802 (2006).

[13] M. J. Strassler and K. M. Zurek, Echoes of a hidden valley at hadron colliders, Phys. Lett. B **651**, 374 (2007).

[14] M. J. Strassler and K. M. Zurek, Discovering the Higgs through highly-displaced vertices, Phys. Lett. B **661**, 263 (2008).

[15] M. Baumgart, C. Cheung, J. T. Ruderman, L.-T. Wang, and I. Yavin, Non-Abelian dark sectors and their collider signatures, J. High Energy Phys. 04 (2009) 014.

[16] D. E. Kaplan, M. A. Luty, and K. M. Zurek, Asymmetric dark matter, Phys. Rev. D **79**, 115016 (2009).

[17] Y. F. Chan, M. Low, D. E. Morrissey, and A. P. Spray, LHC signatures of a minimal supersymmetric hidden valley, J. High Energy Phys. 05 (2012) 155.

[18] K. R. Dienes and B. Thomas, Dynamical dark matter: I. Theoretical overview, Phys. Rev. D **85**, 083523 (2012).

[19] K. R. Dienes, S. Su, and B. Thomas, Distinguishing dynamical dark matter at the LHC, Phys. Rev. D **86**, 054008 (2012).

[20] Y. Cui and R. Sundrum, Baryogenesis for weakly interacting massive particles, Phys. Rev. D **87**, 116013 (2013).

[21] Y. Cui, Natural baryogenesis from unnatural supersymmetry, J. High Energy Phys. 12 (2013) 067.

[22] Y. Cui and B. Shuve, Probing baryogenesis with displaced vertices at the LHC, J. High Energy Phys. 02 (2015) 049.

[23] J. C. Helo, M. Hirsch, and S. Kovalenko, Heavy neutrino searches at the LHC with displaced vertices, Phys. Rev. D **89**, 073005 (2014); Erratum, Phys. Rev. D **93**, 099902(E) (2016).

[24] B. Batell, M. Pospelov, and B. Shuve, Shedding light on neutrino masses with dark forces, J. High Energy Phys. 08 (2016) 052.

[25] CDF Collaboration, Search for heavy metastable particles decaying to jet pairs in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV, Phys. Rev. D **85**, 012007 (2012).

[26] D0 Collaboration, Search for Resonant Pair Production of Neutral Long-Lived Particles Decaying to $b\bar{b}$ in $p\bar{p}$ Collisions at $\sqrt{s} = 1.96$ TeV, Phys. Rev. Lett. **103**, 071801 (2009).

[27] ATLAS Collaboration, Search for a Light Higgs Boson Decaying to Long-Lived Weakly Interacting Particles in Proton-Proton Collisions at $\sqrt{s} = 7$ TeV with the ATLAS Detector, Phys. Rev. Lett. **108**, 251801 (2012).

[28] LHCb Collaboration, Search for long-lived particles decaying to jet pairs, Eur. Phys. J. C **75**, 152 (2015).

[29] ATLAS Collaboration, Search for long-lived, weakly interacting particles that decay to displaced hadronic jets in proton-proton collisions at $\sqrt{s} = 8$ TeV with the ATLAS detector, Phys. Rev. D **92**, 012010 (2015).

[30] ATLAS Collaboration, Search for massive, long-lived particles using multitrack displaced vertices or displaced lepton pairs in $pp$ collisions at $\sqrt{s} = 8$ TeV with the ATLAS detector, Phys. Rev. D **92**, 072004 (2015).

[31] CMS Collaboration, Search for long-lived neutral particles decaying to quark-antiquark pairs in proton-proton collisions at $\sqrt{s} = 8$ TeV, Phys. Rev. D **91**, 012007 (2015).

[32] ATLAS Collaboration, Search for pair-produced long-lived neutral particles decaying in the ATLAS hadronic calorimeter in $pp$ collisions at $\sqrt{s} = 8$ TeV, Phys. Lett. B **743**, 15 (2015).

[33] LHCb Collaboration, Updated search for long-lived particles decaying to jet pairs, Eur. Phys. J. C **77**, 812 (2017).

[34] LHCb Collaboration, Search for massive long-lived particles decaying semileptonically in the LHCb detector, Eur. Phys. J. C **77**, 224 (2017).

[35] CMS Collaboration, Search for new long-lived particles at $\sqrt{s} = 13$ TeV, Phys. Lett. B **780**, 432 (2018).

[36] A. Coccaro, D. Curtin, H. J. Lubatti, H. Russell, and J. Shelton, Data-driven model-independent searches for long-lived particles at the LHC, Phys. Rev. D **94**, 113003 (2016).

[37] ATLAS Collaboration, The ATLAS experiment at the CERN large hadron collider, J. Instrum. **3**, S08003 (2008).

[38] ATLAS Collaboration, Performance of the ATLAS trigger system in 2015, Eur. Phys. J. C **77**, 317 (2017).

[39] ATLAS Collaboration, Triggers for displaced decays of long-lived neutral particles in the ATLAS detector, J. Instrum. **8**, P07015 (2013).

[40] ATLAS Collaboration, Standalone vertex finding in the ATLAS muon spectrometer, J. Instrum. **9**, P02001 (2014).

[41] R. Bruce et al., Sources of machine-induced background in the ATLAS and CMS detectors at the CERN large hadron collider, Nucl. Instrum. Methods Phys. Res., Sect. A **729**, 825 (2013).

[42] M. J. Strassler, Possible effects of a hidden valley on supersymmetric phenomenology, arXiv:hep-ph/0607160.

[43] ATLAS and CMS Collaborations, Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC $pp$ collision data at $\sqrt{s} = 7$ and 8 TeV, J. High Energy Phys. 08 (2016) 045.

[44] D. Curtin and C. B. Verhaaren, Discovering uncolored naturalness in exotic Higgs decays, J. High Energy Phys. 12 (2015) 072.

[45] S. Blanchet, Z. Chacko, S. S. Granor, and R. N. Mohapatra, Probing resonant leptogenesis at the LHC, Phys. Rev. D **82**, 076008 (2010).
[46] C. S. Fong, M. Gonzalez-Garcia, E. Nardi, and E. Peinado, New ways to TeV scale leptogenesis, J. High Energy Phys. 08 (2013) 104.

[47] H. An and Y. Zhang, Direct detection of baryogenesis mechanism from squark decays at LHC, Phys. Rev. D **89**, 071902 (2014).

[48] D. Curtin *et al.*, Exotic decays of the 125 GeV Higgs boson, Phys. Rev. D **90**, 075004 (2014).

[49] C. Englert, T. Plehn, D. Zerwas, and P. M. Zerwas, Explor- ing the Higgs portal, Phys. Lett. B **703**, 298 (2011).

[50] C. Englert, T. Plehn, M. Rauch, D. Zerwas, and P. M. Zerwas, LHC: Standard Higgs and hidden Higgs, Phys. Lett. B **707**, 512 (2012).

[51] C. Csáki, The minimal supersymmetric standard model (MSSM), Mod. Phys. Lett. A **11**, 599 (1996).

[52] J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H.-S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, The automated computation of tree-level and next-to- leading order differential cross sections, and their matching to parton shower simulations, J. High Energy Phys. 07 (2014) 079.

[53] T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An introduction to PYTHIA 8.2, Comput. Phys. Commun. **191**, 159 (2015).

[54] ATLAS Collaboration, ATLAS Pythia 8 tunes to 7 TeV data, CERN Report No. ATL-PHYS-PUB-2014-021, 2014, https://cds.cern.ch/record/1966419.

[55] R. D. Ball *et al.*, Parton distributions with LHC data, Nucl. Phys. **B867**, 244 (2013).

[56] D. J. Lange, The EvtGen particle decay simulation package, Nucl. Instrum. Methods Phys. Res., Sect. A **462**, 152 (2001).

[57] ATLAS Collaboration, The ATLAS simulation infrastruc- ture, Eur. Phys. J. C **70**, 823 (2010).

[58] S. Agostinelli *et al.*, Geant4—A simulation toolkit, Nucl. Instrum. Methods Phys. Res., Sect. A **506**, 250 (2003).

[59] ATLAS Collaboration, Summary of ATLAS Pythia 8 tunes, CERN Report No. ATL-PHYS-PUB-2012-003, 2012, https://cds.cern.ch/record/1474107.

[60] A. Martin, W. Stirling, R. Thorne, and G. Watt, Parton distributions for the LHC, Eur. Phys. J. C **63**, 189 (2009).

[61] M. Cacciari, G. Salam, and G. Soyez, The anti-$k_t$ jet clustering algorithm, J. High Energy Phys. 04 (2008) 063.

[62] M. Cacciari, G. Salam, and G. Soyez, FastJet user manual, Eur. Phys. J. C **72**, 1896 (2012).

[63] ATLAS Collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1, Eur. Phys. J. C **77**, 490 (2017).

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

[65] ATLAS Collaboration, Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector, Phys. Rev. D **96**, 072002 (2017).

[66] ATLAS Collaboration, $E_T^{miss}$ performance in the ATLAS detector using 2015–2016 LHC pp collisions, CERN Report No. ATLAS-CONF-2018-023, 2018, https://cds.cern.ch/record/2625233.

[67] ATLAS Collaboration, Electron and photon energy calibra- tion with the ATLAS detector using data collected in 2015 at $\sqrt{s} = 13$ TeV, CERN Report No. ATL-PHYS-PUB-2016- 015, 2016, https://cds.cern.ch/record/2203514.

[68] ATLAS Collaboration, Muon reconstruction performance of the ATLAS detector in proton-proton collision data at $\sqrt{s} = 13$ TeV, Eur. Phys. J. C **76**, 292 (2016).

[69] ATLAS Collaboration, Electron identification measure- ments in ATLAS using $\sqrt{s} = 13$ TeV data with 50 ns bunch spacing, CERN Report No. ATL-PHYS-PUB-2015- 041, 2015, https://cds.cern.ch/record/2048202.

[70] ATLAS Collaboration, Photon identification in 2015 AT- LAS data, CERN Report No. ATL-PHYS-PUB-2016-014, 2016, https://cds.cern.ch/record/2203125.

[71] ATLAS Collaboration, Search for long-lived, weakly inter- acting particles that decay to displaced hadronic jets in proton-proton collisions at $\sqrt{s} = 8$ TeV with the ATLAS detector, Phys. Rev. D **92**, 012010 (2015).

[72] ATLAS Collaboration, Performance of pile-up mitigation techniques for jets in pp collisions at $\sqrt{s} = 8$ TeV using the ATLAS detector, Eur. Phys. J. C **76**, 581 (2016).

[73] ATLAS Collaboration, Selection of jets produced in 13 TeV proton-proton collisions with the ATLAS detector, CERN Report No. ATLAS-CONF-2015-029, 2015, https://cds.cern.ch/record/2037702.

[74] ATLAS Collaboration, Luminosity determination in pp collisions at $\sqrt{s} = 8$ TeV using the ATLAS detector at the LHC, Eur. Phys. J. C **76**, 653 (2016).

[75] G. Avoni *et al.*, The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS, J. Instrum. **13**, P07017 (2018).

[76] B. Efron, Bootstrap methods: Another look at the jackknife, Ann. Stat. **7**, 1 (1979).

[77] B. Efron and R. Tibshirani, *An Introduction to the Bootstrap* (Chapman & Hall/CRC, London, 1993).

[78] A. L. Read, Presentation of search results: The CLS tech- nique, J. Phys. G **28**, 2693 (2002).

[79] L. Moneta *et al.*, The RooStats Project, Proc. Sci., ACAT2010 (2010) 057 [arXiv:1009.1003].

[80] K. Cranmer, G. Lewis, L. Moneta, A. Shibata, and W. Verkerke, HistFactory: A tool for creating statistical models for use with RooFit and RooStats, CERN Report No. CERN-OPEN-2012-016(2012), http://cdsweb.cern.ch/ record/1456844.

[81] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Asymp- totic formulae for likelihood-based tests of new physics, Eur. Phys. J. C **71**, 1554 (2011); Erratum, Eur. Phys. J. C **73**, 2501(E) (2013).

[82] LHC Higgs Cross Section Working Group, Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector, CERN Report No. CERN-2017-002-M, DOI: 10.23731/CYRM-2017-002.

[83] C. Borschensky, M. Krämer, A. Kulesza, M. Mangano, S. Padhi, T. Plehn, and X. Portell, Squark and gluino production cross sections in pp collisions at $\sqrt{s} = 13, 14, 33$ and 100 TeV, Eur. Phys. J. C **74**, 3174 (2014).

[84] ATLAS Collaboration, ATLAS computing acknowledge- ments, CERN Report No. ATL-GEN-PUB-2016-002, https://cds.cern.ch/record/2202407.
M. Aaboud,³⁴d G. Aad,⁹⁹ B. Abbott,¹²⁵ O. Abdinov,¹³a B. Abeloos,¹²⁹ D. K. Abhayasinghe,⁹¹ S. H. Abidi,¹⁶⁴

O. S. AbouZeid,³⁹ N. L. Abraham,¹⁵³ H. Abramowicz,¹⁵⁸ H. Abreu,¹⁵⁷ Y. Abulaiti,⁶ B. S. Acharya,⁶⁴a,⁶⁴b,⁸ S. Adachi,¹⁶⁰

L. Adamczyk,⁸¹a J. Adelman,¹¹⁹ M. Adersberger,¹¹² A. Adiguzel,¹²c,¹²c T. Adye,¹⁴¹ A. A. Affolder,¹⁴³ Y. Afik,¹⁵⁷

C. Agheorghiesei,²⁷c J. A. Aguilar-Saavedra,¹³⁷f,¹³⁷a,d F. Ahmadov,⁷⁷,e G. Aielli,⁷¹a,⁷¹b S. Akatsuka,⁸³ T. P. A. Åkesson,⁹⁴

E. Akilli,⁵² A. V. Akimov,¹⁰⁸ G. L. Alberghi,²³b,²³a J. Albert,¹⁷³ P. Albicocco,⁴⁹ M. J. Alconada Verzini,⁸⁶

S. Alderweireldt,¹¹⁷ M. Aleksa,³⁵ I. N. Aleksandrov,⁷⁷ C. Alexa,²⁷b T. Alexopoulos,¹⁰ M. Alhroob,¹²⁵ B. Ali,¹³⁹

G. Alimonti,⁶⁶a J. Alison,³⁶ S. P. Alkire,¹⁴⁵ C. Allaire,¹²⁹ B. M. M. Allbrooke,¹⁵³ B. W. Allen,¹²⁸ P. P. Allport,²¹

A. Aloisio,⁶⁷a,⁶⁷b A. Alonso,³⁹ F. Alonso,⁸⁶ C. Alpigiani,¹⁴⁵ A. A. Alshehri,⁵⁵ M. I. Alstaty,⁹⁹ B. Alvarez Gonzalez,³⁵

D. Álvarez Piqueras,¹⁷¹ M. G. Alviggi,⁶⁷a,⁶⁷b B. T. Amadio,¹⁸ Y. Amaral Coutinho,⁷⁸b L. Ambroz,¹³² C. Amelung,²⁶

D. Amidei,¹⁰³ S. P. Amor Dos Santos,¹³⁷a,¹³⁷c S. Amoroso,⁴⁴ C. S. Amrouche,⁵² C. Anastopoulos,¹⁴⁶ L. S. Ancu,⁵²

N. Andari,¹⁴² T. Andeen,¹¹ C. F. Anders,⁵⁹b J. K. Anders,²⁰ K. J. Anderson,³⁶ A. Andreazza,⁶⁶a,⁶⁶b V. Andrei,⁵⁹a

C. R. Anelli,¹⁷³ S. Angelidakis,³⁷ I. Angelozzi,¹¹⁸ A. Angerami,³⁸ A. V. Anisenkov,¹²⁰b,¹²⁰a A. Annovi,⁶⁹a C. Antel,⁵⁹a

M. T. Anthony,¹⁴⁶ M. Antonelli,⁴⁹ D. J. A. Antrim,¹⁶⁸ F. Anulli,⁷⁰a M. Aoki,⁷⁹ J. A. Aparisi Pozo,¹⁷¹ L. Aperio Bella,³⁵

G. Arabidze,¹⁰⁴ J. P. Araque,¹³⁷a V. Araujo Ferraz,⁷⁸b R. Araujo Pereira,⁷⁸b A. T. H. Arce,⁴⁷ R. E. Ardell,⁹¹ F. A. Arduh,⁸⁶

J-F. Arguin,¹⁰⁷ S. Argyropoulos,⁷⁵ A. J. Armbruster,³⁵ L. J. Armitage,⁹⁰ A. Armstrong,¹⁶⁸ O. Arnaez,¹⁶⁴ H. Arnold,¹¹⁸

M. Arratia,³¹ O. Arslan,²⁴ A. Artamonov,¹⁰⁹a, G. Artoni,¹³² S. Artz,⁹⁷ S. Asai,¹⁶⁰ N. Asbah,⁵⁷ E. M. Asimakopoulou,¹⁶⁹

L. Asquith,¹⁵³ K. Assamagan,²⁹ R. Astalos,²⁸a R. J. Atkin,³²a M. Atkinson,¹⁷⁰ N. B. Atlay,¹⁴⁸ K. Augsten,¹³⁹ G. Avolio,³⁵

R. Avramidou,⁵⁸a M. K. Ayoub,¹⁵a G. Azuelos,¹⁰⁷f A. E. Baas,⁵⁹a M. J. Baca,²¹ H. Bachacou,¹⁴² K. Bachas,⁶⁵a,⁶⁵b

M. Backes,¹³² P. Bagnaia,⁷⁰a,⁷⁰b M. Bahmani,⁸² H. Bahrasemani,¹⁴⁹ A. J. Bailey,¹⁷¹ J. T. Baines,¹⁴¹ M. Bajic,³⁹ C. Bakalis,¹⁰

O. K. Baker,¹⁸⁰ P. J. Bakker,¹¹⁸ D. Bakshi Gupta,⁹³ S. Balaji,¹⁵⁴ E. M. Baldin,¹²⁰b,¹²⁰a P. Balek,¹⁷⁷ F. Balli,¹⁴²

W. K. Balunas,¹³⁴ J. Balz,⁹⁷ E. Banas,⁸² A. Bandyopadhyay,²⁴ S. Banerjee,¹⁷⁸g A. A. E. Bannoura,¹⁷⁹ L. Barak,¹⁵⁸

W. M. Barbe,³⁷ E. L. Barberio,¹⁰² D. Barberis,⁵³b,⁵³a M. Barbero,⁹⁹ T. Barillari,¹¹³ M-S. Barisits,³⁵ J. Barkeloo,¹²⁸

T. Barklow,¹⁵⁰ R. Barnea,¹⁵⁷ S. L. Barnes,⁵⁸c B. M. Barnett,¹⁴¹ R. M. Barnett,¹⁸ Z. Barnovska-Blenessy,⁵⁸a A. Baroncelli,⁷²a

G. Barone,²⁶ A. J. Barr,¹³² L. Barranco Navarro,¹⁷¹ F. Barreiro,⁹⁶ J. Barreiro Guimarães da Costa,¹⁵a R. Bartoldus,¹⁵⁰

A. E. Barton,⁸⁷ P. Bartos,²⁸a A. Basalaev,¹³⁵ A. Bassalat,¹²⁹ R. L. Bates,⁵⁵ S. J. Batista,¹⁶⁴ S. Batlamous,³⁴e J. R. Batley,³¹

M. Battaglia,¹⁴³ M. Bauce,⁷⁰a,⁷⁰b F. Bauer,¹⁴² K. T. Bauer,¹⁶⁸ H. S. Bawa,¹⁵⁰h J. B. Beacham,¹²³ T. Beau,¹³³

P. H. Beauchemin,¹⁶⁷ P. Bechtle,²⁴ H. C. Beck,⁵¹ H. P. Beck,²⁰i K. Becker,⁵⁰ M. Becker,⁹⁷ C. Becot,⁴⁴ A. Beddall,¹²d

A. J. Beddall,¹²a V. A. Bednyakov,⁷⁷ M. Bedognetti,¹¹⁸ C. P. Bee,¹⁵² T. A. Beermann,³⁵ M. Begalli,⁷⁸b M. Begel,²⁹

A. Behera,¹⁵² J. K. Behr,⁴⁴ A. S. Bell,⁹² G. Bella,¹⁵⁸ L. Bellagamba,²³b A. Bellerive,³³ M. Bellomo,¹⁵⁷ P. Bellos,⁹

K. Belotskiy,¹¹⁰ N. L. Belyaev,¹¹⁰ O. Benary,¹⁵⁸a D. Benchekroun,³⁴a M. Bender,¹¹² N. Benekos,¹⁰ Y. Benhammou,¹⁵⁸

E. Benhar Noccioli,¹⁸⁰ J. Benitez,⁷⁵ D. P. Benjamin,⁴⁷ M. Benoit,⁵² J. R. Bensinger,²⁶ S. Bentvelsen,¹¹⁸ L. Beresford,¹³²

M. Beretta,⁴⁹ D. Berge,⁴⁴ E. Bergeaas Kuutmann,¹⁶⁹ N. Berger,⁵ L. J. Bergsten,²⁶ J. Beringer,¹⁸ S. Berlendis,⁷

N. R. Bernard,¹⁰⁰ G. Bernardi,¹³³ C. Bernius,¹⁵⁰ F. U. Bernlochner,²⁴ T. Berry,⁹¹ P. Berta,⁹⁷ C. Bertella,¹⁵a G. Bertoli,⁴³a,⁴³b

I. A. Bertram,⁸⁷ G. J. Besjes,³⁹ O. Bessidskaia Bylund,¹⁷⁹ M. Bessner,⁴⁴ N. Besson,¹⁴² A. Bethani,⁹⁸ S. Bethke,¹¹³ A. Betti,²⁴

A. J. Bevan,⁹⁰ J. Beyer,¹¹³ R. M. Bianchi,¹³⁶ O. Biebel,¹¹² D. Biedermann,¹⁹ R. Bielski,³⁵ K. Bierwagen,⁹⁷

N. V. Biesuz,⁶⁹a,⁶⁹b M. Biglietti,⁷²a T. R. V. Billoud,¹⁰⁷ M. Bindi,⁵¹ A. Bingul,¹²d C. Bini,⁷⁰a,⁷⁰b S. Biondi,²³b,²³a M. Birman,¹⁷⁷

T. Bisanz,⁵¹ J. P. Biswal,¹⁵⁸ C. Bittich,⁴⁶ D. M. Bjergaard,⁴⁷ J. E. Black,¹⁵⁰ K. M. Black,²⁵ T. Blazek,²⁸a I. Bloch,⁴⁴

C. Blocker,²⁶ A. Blue,⁵⁵ U. Blumenschein,⁹⁰ Dr. Blunier,¹⁴⁴a G. J. Bobbink,¹¹⁸ V. S. Bobrovnikov,¹²⁰b,¹²⁰a S. S. Bocchetta,⁹⁴

A. Bocci,⁴⁷ D. Boerner,¹⁷⁹ D. Bogavac,¹¹² A. G. Bogdanchikov,¹²⁰b,¹²⁰a C. Bohm,⁴³a V. Boisvert,⁹¹ P. Bokan,¹⁶⁹ T. Bold,⁸¹a

A. S. Boldyrev,¹¹¹ A. E. Bolz,⁵⁹b M. Bomben,¹³³ M. Bona,⁹⁰ J. S. Bonilla,¹²⁸ M. Boonekamp,¹⁴² A. Borisov,¹²¹

G. Borissov,⁸⁷ J. Bortfeldt,³⁵ D. Bortoletto,¹³² V. Bortolotto,⁷¹a,⁷¹b D. Boscherini,²³b M. Bosman,¹⁴ J. D. Bossio Sola,³⁰

K. Bouaouda,³⁴a J. Boudreau,¹³⁶ E. V. Bouhova-Thacker,⁸⁷ D. Boumediene,³⁷ C. Bourdarios,¹²⁹ S. K. Boutle,⁵⁵

A. Boveia,¹²³ J. Boyd,³⁵ D. Boye,³²b I. R. Boyko,⁷⁷ A. J. Bozson,⁹¹ J. Bracinik,²¹ N. Brahimi,⁹⁹ A. Brandt,⁸ G. Brandt,¹⁷⁹

O. Brandt,⁵⁹a F. Braren,⁴⁴ U. Bratzler,¹⁶¹ B. Brau,¹⁰⁰ J. E. Brau,¹²⁸ W. D. Breaden Madden,⁵⁵ K. Brendlinger,⁴⁴ L. Brenner,⁴⁴

R. Brenner,¹⁶⁹ S. Bressler,¹⁷⁷ B. Brickwedde,⁹⁷ D. L. Briglin,²¹ D. Britton,⁵⁵ D. Britzger,⁵⁹b I. Brock,²⁴ R. Brock,¹⁰⁴

G. Brooijmans,³⁸ T. Brooks,⁹¹ W. K. Brooks,¹⁴⁴b E. Brost,¹¹⁹ J. H. Broughton,²¹ P. A. Bruckman de Renstrom,⁸²

D. Bruncko,²⁸b A. Bruni,²³b G. Bruni,²³b L. S. Bruni,¹¹⁸ S. Bruno,⁷¹a,⁷¹b B. H. Brunt,³¹ M. Bruschi,²³b N. Bruscino,¹³⁶

P. Bryant,³⁶ L. Bryngemark,⁴⁴ T. Buanes,¹⁷ Q. Buat,³⁵ P. Buchholz,¹⁴⁸ A. G. Buckley,⁵⁵ I. A. Budagov,⁷⁷ M. K. Bugge,¹³¹

F. Bührer,⁵⁰ O. Bulekov,¹¹⁰ D. Bullock,⁸ T. J. Burch,¹¹⁹ S. Burdin,⁸⁸ C. D. Burgard,¹¹⁸ A. M. Burger,⁵ B. Burghgrave,¹¹⁹
K. Burka,⁸² S. Burke,¹⁴¹ I. Burmeister,⁴⁵ J. T. P. Burr,¹³² V. Büscher,⁹⁷ E. Buschmann,⁵¹ P. Bussey,⁵⁵ J. M. Butler,²⁵
C. M. Buttar,⁵⁵ J. M. Butterworth,⁹² P. Butti,³⁵ W. Buttinger,³⁵ A. Buzatu,¹⁵⁵ A. R. Buzykaev,¹²⁰b,¹²⁰a G. Cabras,²³b,²³a
S. Cabrera Urbán,¹⁷¹ D. Caforio,¹³⁹ H. Cai,¹⁷⁰ V. M. M. Cairo,² O. Cakir,⁴ª N. Calace,⁵² P. Calafiura,¹⁸ A. Calandri,⁹⁹
G. Calderini,¹³³ P. Calfayan,⁶³ G. Callea,⁴⁰b,⁴⁰a L. P. Caloba,⁷⁸b S. Calvente Lopez,⁹⁶ D. Calvet,³⁷ S. Calvet,³⁷ T. P. Calvet,¹⁵²
M. Calvetti,⁶⁹a,⁶⁹b R. Camacho Toro,¹³³ S. Camarda,³⁵ P. Camarri,⁷¹a,⁷¹b D. Cameron,¹³¹ R. Caminal Armadans,¹⁰⁰
C. Camincher,³⁵ S. Campana,³⁵ M. Campanelli,⁹² A. Camplani,³⁹ A. Campoverde,¹⁴⁸ V. Canale,⁶⁷a,⁶⁷b M. Cano Bret,⁵⁸c
J. Cantero,¹²⁶ T. Cao,¹⁵⁸ Y. Cao,¹⁷⁰ M. D. M. Capeans Garrido,³⁵ I. Caprini,²⁷b M. Caprini,²⁷b M. Capua,⁴⁰b,⁴⁰a
R. M. Carbone,³⁸ R. Cardarelli,⁷¹a F. C. Cardillo,¹⁴⁶ I. Carli,¹⁴⁰ T. Carli,³⁵ G. Carlino,⁶⁷a B. T. Carlson,¹³⁶ L. Carminati,⁶⁶a,⁶⁶b
R. M. D. Carney,⁴³a,⁴³b S. Caron,¹¹⁷ E. Carquin,¹⁴⁴b S. Carrá,⁶⁶a,⁶⁶b G. D. Carrillo-Montoya,³⁵ D. Casadei,³²b M. P. Casado,¹⁴,j
A. F. Casha,¹⁶⁴ D. W. Casper,¹⁶⁸ R. Castelijn,¹¹⁸ F. L. Castillo,¹⁷¹ V. Castillo Gimenez,¹⁷¹ N. F. Castro,¹³⁷a,¹³⁷e
A. Catinaccio,³⁵ J. R. Catmore,¹³¹ A. Cattai,³⁵ J. Caudron,²⁴ V. Cavaliere,²⁹ E. Cavallaro,¹⁴ D. Cavalli,⁶⁶a
M. Cavalli-Sforza,¹⁴ V. Cavasinni,⁶⁹a,⁶⁹b E. Celebi,¹²b F. Ceradini,⁷²a,⁷²b L. Cerda Alberich,¹⁷¹ A. S. Cerqueira,⁷⁸a A. Cerri,¹⁵³
L. Cerrito,⁷¹a,⁷¹b F. Cerutti,¹⁸ A. Cervelli,²³b,²³a S. A. Cetin,¹²b A. Chafaq,³⁴a D. Chakraborty,¹¹⁹ S. K. Chan,⁵⁷ W. S. Chan,¹¹⁸
Y. L. Chan,⁶¹a J. D. Chapman,³¹ B. Chargeishvili,¹⁵⁶b D. G. Charlton,²¹ C. C. Chau,³³ C. A. Chavez Barajas,¹⁵³ S. Che,¹²³
A. Chegwidden,¹⁰⁴ S. Chekanov,⁶ S. V. Chekulaev,¹⁶⁵a G. A. Chelkov,⁷⁷k M. A. Chelstowska,³⁵ C. Chen,⁵⁸a C. H. Chen,⁷⁶
H. Chen,²⁹ J. Chen,⁵⁸a J. Chen,³⁸ S. Chen,¹³⁴ S. J. Chen,¹⁵c X. Chen,¹⁵b,¹ Y. Chen,⁸⁰ Y-H. Chen,⁴⁴ H. C. Cheng,¹⁰³
H. J. Cheng,¹⁵d A. Cheplakov,⁷⁷ E. Cheremushkina,¹²¹ R. Cherkaoui El Moursli,³⁴e E. Cheu,⁷ K. Cheung,⁶² L. Chevalier,¹⁴²
V. Chiarella,⁴⁹ G. Chiarelli,⁶⁹a G. Chiodini,⁶⁵a A. S. Chisholm,³⁵,²¹ A. Chitan,²⁷b I. Chiu,¹⁶⁰ Y. H. Chiu,¹⁷³ M. V. Chizhov,⁷⁷
K. Choi,⁶³ A. R. Chomont,¹²⁹ S. Chouridou,¹⁵⁹ Y. S. Chow,¹¹⁸ V. Christodoulou,⁹² M. C. Chu,⁶¹a J. Chudoba,¹³⁸
A. J. Chuinard,¹⁰¹ J. J. Chwastowski,⁸² L. Chytka,¹²⁷ D. Cinca,⁵⁴ V. Cindro,⁸⁹ I. A. Cioară,²⁴ A. Ciocio,¹⁸ F. Cirotto,⁶⁷a,⁶⁷b
Z. H. Citron,¹⁷⁷ M. Citterio,⁶⁶a A. Clark,⁵² M. R. Clark,³⁸ P. J. Clark,⁴⁸ C. Clement,⁴³a,⁴³b Y. Coadou,⁹⁹ M. Cobal,⁶⁴a,⁶⁴c
A. Coccaro,⁵³b,⁵³a J. Cochran,⁷⁶ H. Cohen,¹⁵⁸ A. E. C. Coimbra,¹⁷⁷ L. Colasurdo,¹¹⁷ B. Cole,³⁸ A. P. Colijn,¹¹⁸ J. Collot,⁵⁶
P. Conde Muiño,¹³⁷a,¹m E. Coniavitis,⁵⁰ S. H. Connell,³²b I. A. Connelly,⁹⁸ S. Constantinescu,²⁷b F. Conventi,⁶⁷a,¹n
A. M. Cooper-Sarkar,¹³² F. Cormier,¹⁷² K. J. R. Cormier,¹⁶⁴ L. D. Corpe,⁹² M. Corradi,⁷⁰a,⁷⁰b E. E. Corrigan,⁹⁴
F. Corriveau,¹⁰¹,⁰ A. Cortes-Gonzalez,³⁵ M. J. Costa,¹⁷¹ F. Costanza,⁵ D. Costanzo,¹⁴⁶ G. Cottin,³¹ G. Cowan,⁹¹ B. E. Cox,⁹⁸
J. Crane,⁹⁸ K. Cranmer,¹²² S. J. Crawley,⁵⁵ R. A. Creager,¹³⁴ G. Cree,³³ S. Crépé-Renaudin,⁵⁶ F. Crescioli,¹³³
M. Cristinziani,²⁴ V. Croft,¹²² G. Crosetti,⁴⁰b,⁴⁰a A. Cueto,⁹⁶ T. Cuhadar Donszelmann,¹⁴⁶ A. R. Cukierman,¹⁵⁰
S. Czekierda,⁸² P. Czodrowski,³⁵ M. J. Da Cunha Sargedas De Sousa,⁵⁸b C. Da Via,⁹⁸ W. Dabrowski,⁸¹a T. Dado,²⁸a,¹p
S. Dahbi,³⁴e T. Dai,¹⁰³ F. Dallaire,¹⁰⁷ C. Dallapiccola,¹⁰⁰ M. Dam,³⁹ G. D'amen,²³b,²³a J. Damp,⁹⁷ J. R. Dandoy,¹³⁴
M. F. Daneri,³⁰ N. P. Dang,¹⁷⁸g N. D. Dann,⁹⁸ M. Danninger,¹⁷² V. Dao,³⁵ G. Darbo,⁵³b S. Darmora,⁸ O. Dartsi,⁵
A. Dattagupta,¹²⁸ T. Daubney,⁴⁴ S. D'Auria,⁵⁵ W. Davey,²⁴ C. David,⁴⁴ T. Davidek,¹⁴⁰ D. R. Davis,⁴⁷ E. Dawe,¹⁰²
I. Dawson,¹⁴⁶ K. De,⁸ R. De Asmundis,⁶⁷a A. De Benedetti,¹²⁵ M. De Beurs,¹¹⁸ S. De Castro,²³b,²³a S. De Cecco,⁷⁰a,⁷⁰b
N. De Groot,¹¹⁷ P. de Jong,¹¹⁸ H. De la Torre,¹⁰⁴ F. De Lorenzi,⁷⁶ A. De Maria,⁵¹,q D. De Pedis,⁷⁰a A. De Salvo,⁷⁰a
U. De Sanctis,⁷¹a,⁷¹b M. De Santis,⁷¹a,⁷¹b A. De Santo,¹⁵³ K. De Vasconcelos Corga,⁹⁹ J. B. De Vivie De Regie,¹²⁹
C. Debenedetti,¹⁴³ D. V. Dedovich,⁷⁷ N. Dehghanian,³ M. Del Gaudio,⁴⁰b,⁴⁰a J. Del Peso,⁹⁶ Y. Delabat Diaz,⁴⁴ D. Delgove,¹²⁹
F. Deliot,¹⁴² C. M. Delitzsch,⁷ M. Della Pietra,⁶⁷a,⁶⁷b D. Della Volpe,⁵² A. Dell'Acqua,³⁵ L. Dell'Asta,²⁵ M. Delmastro,⁵
C. Delporte,¹²⁹ P. A. Delsart,⁵⁶ D. A. DeMarco,¹⁶⁴ S. Demers,¹⁸⁰ M. Demichev,⁷⁷ S. P. Denisov,¹²¹ D. Denysiuk,¹¹⁸
L. D'Eramo,¹³³ D. Derendarz,⁸² J. E. Derkaoui,³⁴d F. Derue,¹³³ P. Dervan,⁸⁸ K. Desch,²⁴ C. Deterre,⁴⁴ K. Dette,¹⁶⁴
M. R. Devesa,³⁰ P. O. Deviveiros,³⁵ A. Dewhurst,¹⁴¹ S. Dhaliwal,²⁶ F. A. Di Bello,⁵² A. Di Ciaccio,⁷¹a,⁷¹b L. Di Ciaccio,⁵
W. K. Di Clemente,¹³⁴ C. Di Donato,⁶⁷a,⁶⁷b A. Di Girolamo,³⁵ B. Di Micco,⁷²a,⁷²b R. Di Nardo,¹⁰⁰ K. F. Di Petrillo,⁵⁷
R. Di Sipio,¹⁶⁴ D. Di Valentino,³³ C. Diaconu,⁹⁹ M. Diamond,¹⁶⁴ F. A. Dias,³⁹ T. Dias Do Vale,¹³⁷a M. A. Diaz,¹⁴⁴a
J. Dickinson,¹⁸ E. B. Diehl,¹⁰³ J. Dietrich,¹⁹ S. Díez Cornell,⁴⁴ A. Dimitrievska,¹⁸ J. Dingfelder,²⁴ F. Dittus,³⁵ F. Djama,⁹⁹
T. Djobava,¹⁵⁶b J. I. Djuvsland,⁵⁹a M. A. B. Do Vale,⁷⁸c M. Dobre,²⁷b D. Dodsworth,²⁶ C. Doglioni,⁹⁴ J. Dolejsi,¹⁴⁰
Z. Dolezal,¹⁴⁰ M. Donadelli,⁷⁸d J. Donini,³⁷ A. D'onofrio,⁹⁰ M. D'Onofrio,⁸⁸ J. Dopke,¹⁴¹ A. Doria,⁶⁷a M. T. Dova,⁸⁶
A. T. Doyle,⁵⁵ E. Drechsler,⁵¹ E. Dreyer,¹⁴⁹ T. Dreyer,⁵¹ Y. Du,⁵⁸b F. Dubinin,¹⁰⁸ M. Dubovsky,²⁸a A. Dubreuil,⁵²
E. Duchovni,¹⁷⁷ G. Duckeck,¹¹² A. Ducourthial,¹³³ O. A. Ducu,¹⁰⁷,x D. Duda,¹¹³ A. Dudarev,³⁵ A. C. Dudder,⁹⁷
E. M. Duffield,¹⁸ L. Duflot,¹²⁹ M. Dührssen,³⁵ C. Dülsen,¹⁷⁹ M. Dumancic,¹⁷⁷ A. E. Dumitriu,²⁷b,⁸ A. K. Duncan,⁵⁵
M. Dunford,⁵⁹a A. Duperrin,⁹⁹ H. Duran Yildiz,⁴ª M. Düren,⁵⁴ A. Durglishvili,¹⁵⁶b D. Duschinger,⁴⁶ B. Dutta,⁴⁴
D. Duvnjak,¹ M. Dyndal,⁴⁴ S. Dysch,⁹⁸ B. S. Dziedzic,⁸² C. Eckardt,⁴⁴ K. M. Ecker,¹¹³ R. C. Edgar,¹⁰³ T. Eifert,³⁵
G. Eigen,¹⁷ K. Einsweiler,¹⁸ T. Ekelof,¹⁹ M. El Kacimi,³⁴c R. El Kosseifi,⁹⁹ V. Ellajosyula,⁹⁹ M. Ellert,¹⁶⁹ F. Ellinghaus,¹⁷⁹

A. A. Elliot,⁹⁰ N. Ellis,³⁵ J. Elmsheuser,²⁹ M. Elsing,³⁵ D. Emeliyanov,¹⁴¹ Y. Enari,¹⁶⁰ J. S. Ennis,¹⁷⁵ M. B. Epland,⁴⁷

J. Erdmann,⁴⁵ A. Ereditato,²⁰ S. Errede,¹⁷⁰ M. Escalier,¹²⁹ C. Escobar,¹⁷¹ O. Estrada Pastor,¹⁷¹ A. I. Tienvre,¹⁴² E. Etzion,¹⁵⁸

H. Evans,⁶³ A. Ezhilov,¹³⁵ M. Ezzi,³⁴e F. Fabbri,⁵⁵ L. Fabbri,²³b,²³a V. Fabiani,¹¹⁷ G. Facini,⁹²

R. M. Faisca Rodrigues Pereira,¹³⁷a R. M. Fakhrutdinov,¹²¹ S. Falciano,⁷⁰a P. J. Falke,⁵ S. Falke,⁵ J. Faltova,¹⁴⁰ Y. Fang,¹⁵a

M. Fanti,⁶⁶a,⁶⁶b A. Farbin,⁸ A. Farilla,⁷²a E. M. Farina,⁶⁸a,⁶⁸b T. Farooque,¹⁰⁴ S. Farrell,¹⁸ S. M. Farrington,¹⁷⁵ P. Farthouat,³⁵

F. Fassi,³⁴e P. Fassnacht,³⁵ D. Fassouliotis,⁹ M. Faucci Giannelli,⁴⁸ A. Favareto,⁵³b,⁵³a W. J. Fawcett,³¹ L. Fayard,¹²⁹

O. L. Fedin,¹³⁵,t W. Fedorko,¹⁷² M. Feickert,⁴¹ S. Feigl,¹³¹ L. Feligioni,⁹⁹ C. Feng,⁵⁸b E. J. Feng,³⁵ M. Feng,⁴⁷ M. J. Fenton,⁵⁵

A. B. Fenyuk,¹²¹ L. Feremenga,⁸ J. Ferrando,⁴⁴ A. Ferrari,¹⁶⁹ P. Ferrari,¹¹⁸ R. Ferrari,⁶⁸a D. E. Ferreira de Lima,⁵⁹b

A. Ferrer,¹⁷¹ D. Ferrere,⁵² C. Ferretti,¹⁰³ F. Fiedler,⁹⁷ A. Filipčič,⁸⁹ F. Filthaut,¹¹⁷ K. D. Finelli,²⁵ M. C. N. Fiolhais,¹³⁷a,¹³⁷c,u

L. Fiorini,¹⁷¹ C. Fischer,¹⁴ W. C. Fisher,¹⁰⁴ N. Flaschel,⁴⁴ I. Fleck,¹⁴⁸ P. Fleischmann,¹⁰³ R. R. M. Fletcher,¹³⁴ T. Flick,¹⁷⁹

B. M. Flierl,¹¹² L. M. Flores,¹³⁴ L. R. Flores Castillo,⁶¹a F. M. Follega,⁷³a,⁷³b N. Fomin,¹⁷ G. T. Forcolin,⁷³a,⁷³b A. Formica,¹⁴²

F. A. Förster,¹⁴ A. C. Forti,⁹⁸ A. G. Foster,²¹ D. Fournier,¹²⁹ H. Fox,⁸⁷ S. Fracchia,¹⁴⁶ P. Francavilla,⁶⁹a,⁶⁹b M. Franchini,²³b,²³a

S. Franchino,⁵⁹a D. Francis,³⁵ L. Franconi,¹³¹ M. Franklin,⁵⁷ M. Frate,¹⁶⁸ M. Fraternali,⁶⁸a,⁶⁸b A. N. Fray,⁹⁰ D. Freeborn,⁹²

S. M. Fressard-Batraneanu,³⁵ B. Freund,¹⁰⁷ W. S. Freund,⁷⁸b E. M. Freundlich,⁴⁵ D. C. Frizzell,¹²⁵ D. Froidevaux,³⁵

J. A. Frost,¹³² C. Fukunaga,¹⁶¹ E. Fullana Torregrosa,¹⁷¹ T. Fusayasu,¹¹⁴ J. Fuster,¹⁷¹ O. Gabizon,¹⁵⁷ A. Gabrielli,²³b,²³a

A. Gabrielli,¹⁸ G. P. Gach,⁸¹a S. Gadatsch,⁵² P. Gadow,¹¹³ G. Gagliardi,⁵³b,⁵³a L. G. Gagnon,¹⁰⁷ C. Galea,²⁷b

B. Galhardo,¹³⁷a,¹³⁷c E. J. Gallas,¹³² B. J. Gallop,¹⁴¹ P. Gallus,¹³⁹ G. Galster,³⁹ R. Gamboa Goni,⁹⁰ K. K. Gan,¹²³

S. Ganguly,¹⁷⁷ J. Gao,⁵⁸a Y. Gao,⁸⁸ Y. S. Gao,¹⁵⁰,h C. García,¹⁷¹ J. E. García Navarro,¹⁷¹ J. A. García Pascual,¹⁵a

M. Garcia-Sciveres,¹⁸ R. W. Gardner,³⁶ N. Garelli,¹⁵⁰ V. Garonne,¹³¹ K. Gasnikova,⁴⁴ A. Gaudiello,⁵³b,⁵³a G. Gaudio,⁶⁸a

I. L. Gavrilenko,¹⁰⁸ A. Gavrilyuk,¹⁰⁹ C. Gay,¹⁷² G. Gaycken,²⁴ E. N. Gazis,¹⁰ C. N. P. Gee,¹⁴¹ J. Geisen,⁵¹ M. Geisen,⁹⁷

M. P. Geisler,⁵⁹a K. Gellerstedt,⁴³a,⁴³b C. Gemme,⁵³b M. H. Genest,⁵⁶ C. Geng,¹⁰³ S. Gentile,⁷⁰a,⁷⁰b S. George,⁹¹

D. Gerbaudo,¹⁴ G. Gessner,⁴⁵ S. Ghasemi,¹⁴⁸ M. Ghasemi Bostanabad,¹⁷³ M. Ghneimat,²⁴ B. Giacobbe,²³b S. Giagu,⁷⁰a,⁷⁰b

N. Giangiacomi,²³b,²³a P. Giannetti,⁶⁹a A. Giannini,⁶⁷a,⁶⁷b S. M. Gibson,⁹¹ M. Gignac,¹⁴³ D. Gillberg,³³ G. Gilles,¹⁷⁹

D. M. Gingrich,³f M. P. Giordani,⁶⁴a,⁶⁴c F. M. Giorgi,²³b P. F. Giraud,¹⁴² P. Giromini,⁵⁷ G. Giugliarelli,⁶⁴a,⁶⁴c D. Giugni,⁶⁶a

F. Giuli,¹³² M. Giulini,⁵⁹b S. Gkaitatzis,¹⁵⁹ I. Gkialas,⁹,v E. L. Gkougkousis,¹⁴ P. Gkountoumis,¹⁰ L. K. Gladilin,¹¹¹

C. Glasman,⁹⁶ J. Glatzer,¹⁴ P. C. F. Glaysher,⁴⁴ A. Glazov,⁴⁴ M. Goblirsch-Kolb,²⁶ J. Godlewski,⁸² S. Goldfarb,¹⁰²

T. Golling,⁵² D. Golubkov,¹²¹ A. Gomes,¹³⁷a,¹³⁷b R. Goncalves Gama,⁷⁸a R. Gonçalo,¹³⁷a G. Gonella,⁵⁰ L. Gonella,²¹

A. Gongadze,⁷⁷ F. Gonnella,²¹ J. L. Gonski,⁵⁷ S. González de la Hoz,¹⁷¹ S. Gonzalez-Sevilla,⁵² L. Goossens,³⁵

P. A. Gorbounov,¹⁰⁹ H. A. Gordon,²⁹ B. Gorini,³⁵ E. Gorini,⁶⁵a,⁶⁵b A. Gorišek,⁸⁹ A. T. Goshaw,⁴⁷ C. Gössling,⁴⁵

M. I. Gostkin,⁷⁷ C. A. Gottardo,²⁴ C. R. Goudet,¹²⁹ D. Goujdami,³⁴c A. G. Goussiou,¹⁴⁵ N. Govender,³²b,w C. Goy,⁵

E. Gozani,¹⁵⁷ I. Grabowska-Bold,⁸¹a P. O. J. Gradin,¹⁶⁹ E. C. Graham,⁸⁸ J. Gramling,¹⁶⁸ E. Gramstad,¹³¹ S. Grancagnolo,¹⁹

V. Gratchev,¹³⁵ P. M. Gravila,²⁷f F. G. Gravili,⁶⁵a,⁶⁵b C. Gray,⁵⁵ H. M. Gray,¹⁸ Z. D. Greenwood,⁹³,x C. Grefe,²⁴

K. Gregersen,⁹⁴ I. M. Gregor,⁴⁴ P. Grenier,¹⁵⁰ K. Grevtsov,⁴⁴ N. A. Grieser,¹²⁵ J. Griffiths,⁸ A. A. Grillo,¹⁴³ K. Grimm,¹⁵⁰,y

S. Grinstein,¹⁴,z Ph. Gris,³⁷ J.-F. Grivaz,¹²⁹ S. Groh,⁹⁷ E. Gross,¹⁷⁷ J. Grosse-Knetter,⁵¹ G. C. Grossi,⁹³ Z. J. Grout,⁹²

C. Grud,¹⁰³ A. Grummer,¹¹⁶ L. Guan,¹⁰³ W. Guan,¹⁷⁸ J. Guenther,³⁵ A. Guerguichon,¹²⁹ F. Guescini,¹⁶⁵a D. Guest,¹⁶⁸

R. Gugel,⁵⁰ B. Gui,¹²³ T. Guillemin,⁵ S. Guindon,³⁵ U. Gul,⁵⁵ C. Gumpert,³⁵ J. Guo,⁵⁸c W. Guo,¹⁰³ Y. Guo,⁵⁸a,aa Z. Guo,⁹⁹

R. Gupta,⁴¹ S. Gurbuz,¹²c G. Gustavino,¹²⁵ B. J. Gutelman,¹⁵⁷ P. Gutierrez,¹²⁵ C. Gutschow,⁹² C. Guyot,¹⁴² M. P. Guzik,⁸¹a

C. Gwenlan,¹³² C. B. Gwilliam,⁸⁸ A. Haas,¹²² C. Haber,¹⁸ H. K. Hadavand,⁸ N. Haddad,³⁴c A. Hadef,⁵⁸a S. Hageböck,²⁴

M. Hagihara,¹⁶⁶ H. Hakobyan,¹⁸¹,a M. Haleem,¹⁷⁴ J. Haley,¹²⁶ G. Halladjian,¹⁰⁴ G. D. Hallewell,⁹⁹ K. Hamacher,¹⁷⁹

P. Hamal,¹²⁷ K. Hamano,¹⁷³ A. Hamilton,³²a G. N. Hamity,¹⁴⁶ K. Han,⁵⁸a,bb L. Han,⁵⁸a S. Han,¹⁵d K. Hanagaki,⁷⁹,cc

M. Hance,¹⁴³ D. M. Handl,¹¹² B. Haney,¹³⁴ R. Hankache,¹³³ P. Hanke,⁵⁹a E. Hansen,⁹⁴ J. B. Hansen,³⁹ J. D. Hansen,³⁹

M. C. Hansen,²⁴ P. H. Hansen,³⁹ K. Hara,¹⁶⁶ A. S. Hard,¹⁷⁸ T. Harenberg,¹⁷⁹ S. Harkusha,¹⁰⁵ P. F. Harrison,¹⁷⁵

N. M. Hartmann,¹¹² Y. Hasegawa,¹⁴⁷ A. Hasib,⁴⁸ S. Hassani,¹⁴² S. Haug,²⁰ R. Hauser,¹⁰⁴ L. Hauswald,⁴⁶ L. B. Havener,³⁸

M. Havranek,¹³⁹ C. M. Hawkes,²¹ R. J. Hawkings,³⁵ D. Hayden,¹⁰⁴ C. Hayes,¹⁵² C. P. Hays,¹³² J. M. Hays,⁹⁰

H. S. Hayward,⁸⁸ S. J. Haywood,¹⁴¹ M. P. Heath,⁴⁸ V. Hedberg,⁹⁴ L. Heelan,⁸ S. Heer,²⁴ K. K. Heidegger,⁵⁰ J. Heilman,³³

S. Heim,⁴⁴ T. Heim,¹⁸ B. Heinemann,⁴⁴,dd J. J. Heinrich,¹¹² L. Heinrich,¹²² C. Heinz,⁵⁴ J. Hejbal,¹³⁸ L. Helary,³⁵ A. Held,¹⁷²

S. Hellesund,¹³¹ S. Hellman,⁴³a,⁴³b C. Helsens,³⁵ R. C. W. Henderson,⁸⁷ Y. Heng,¹⁷⁸ S. Henkelmann,¹⁷²

A. M. Henriques Correia,³⁵ G. H. Herbert,¹⁹ H. Herde,²⁶ V. Herget,¹⁷⁴ Y. Hernández Jiménez,³²c H. Herr,⁹⁷
M. G. Herrmann,¹¹² G. Herten,⁵⁰ R. Hertenberger,¹¹² L. Hervas,³⁵ T. C. Herwig,¹³⁴ G. G. Hesketh,⁹² N. P. Hessey,¹⁶⁵a

J. W. Hetherly,⁴¹ S. Higashino,⁷⁹ E. Higón-Rodriguez,¹⁷¹ K. Hildebrand,³⁶ E. Hill,¹⁷³ J. C. Hill,³¹ K. K. Hill,²⁹ K. H. Hiller,⁴⁴

S. J. Hillier,²¹ M. Hils,⁴⁶ I. Hinchliffe,¹⁸ M. Hirose,¹³⁰ D. Hirschbuehl,¹⁷⁹ B. Hiti,⁸⁹ O. Hladik,¹³⁸ D. R. Hlaluku,³²c

X. Hoad,⁴⁸ J. Hobbs,¹⁵² N. Hod,¹⁶⁵a M. C. Hodgkinson,¹⁴⁶ A. Hoecker,³⁵ M. R. Hoeferkamp,¹¹⁶ F. Hoenig,¹¹² D. Hohn,²⁴

D. Hohov,¹²⁹ T. R. Holmes,³⁶ M. Holzbock,¹¹² M. Homann,⁴⁵ S. Honda,¹⁶⁶ T. Honda,⁷⁹ T. M. Hong,¹³⁶ A. Hönle,¹¹³

B. H. Hooberman,¹⁷⁰ W. H. Hopkins,¹²⁸ Y. Horii,¹¹⁵ P. Horn,⁴⁶ A. J. Horton,¹⁴⁹ L. A. Horyn,³⁶ J-Y. Hostachy,⁵⁶

A. Hostioc,¹⁴⁵ S. Hou,¹⁵⁵ A. Hoummada,³⁴a J. Howarth,⁹⁸ J. Hoya,⁸⁶ M. Hrabovsky,¹²⁷ I. Hristova,¹⁹ J. Hrivnac,¹²⁹

A. Hrynevich,¹⁰⁶ T. Hryn'ova,⁵ P. J. Hsu,⁶² S.-C. Hsu,¹⁴⁵ Q. Hu,²⁹ S. Hu,⁵⁸c Y. Huang,¹⁵a Z. Hubacek,¹³⁹ F. Hubaut,⁹⁹

M. Huebner,²⁴ F. Huegging,²⁴ T. B. Huffman,¹³² E. W. Hughes,³⁸ M. Huhtinen,³⁵ R. F. H. Hunter,³³ P. Huo,¹⁵² A. M. Hupe,³³

N. Huseynov,⁷⁷,e J. Huston,¹⁰⁴ J. Huth,⁵⁷ R. Hyneman,¹⁰³ G. Iacobucci,⁵² G. Iakovidis,²⁹ I. Ibragimov,¹⁴⁸

L. Iconovidou-Fayard,¹²⁹ Z. Idrissi,³⁴e P. Iengo,³⁵ R. Ignazzi,³⁹ O. Igonkina,¹¹⁸,ee R. Iguchi,¹⁶⁰ T. Iizawa,⁵² Y. Ikegami,⁷⁹

M. Ikeno,⁷⁹ D. Iliadis,¹⁵⁹ N. Ilic,¹¹⁷ F. Iltzsche,⁴⁶ G. Introzzi,⁶⁸a,⁶⁸b M. Iodice,⁷²a K. Iordanidou,³⁸ V. Ippolito,⁷⁰a,⁷⁰b

M. F. Isacson,¹⁶⁹ N. Ishijima,¹³⁰ M. Ishino,¹⁶⁰ M. Ishitsuka,¹⁶² W. Islam,¹²⁶ C. Issever,¹³² S. Istin,¹⁵⁷ F. Ito,¹⁶⁶

J. M. Iturbe Ponce,⁶¹a R. Iuppa,⁷³a,⁷³b A. Ivina,¹⁷⁷ H. Iwasaki,⁷⁹ J. M. Izen,⁴² V. Izzo,⁶⁷a P. Jacka,¹³⁸ P. Jackson,¹

R. M. Jacobs,²⁴ V. Jain,² G. Jäkel,¹⁷⁹ K. B. Jakobi,⁹⁷ K. Jakobs,⁵⁰ S. Jakobsen,⁷⁴ T. Jakoubek,¹³⁸ D. O. Jamin,¹²⁶

D. K. Jana,⁹³ R. Jansky,⁵² J. Janssen,²⁴ M. Janus,⁵¹ P. A. Janus,⁸¹a G. Jarlskog,⁹⁴ N. Javadov,⁷⁷,e T. Javůrek,³⁵

M. Javurkova,⁵⁰ F. Jeanneau,¹⁴² L. Jeanty,¹⁸ J. Jejelava,¹⁵⁶a,ff A. Jelinskas,¹⁷⁵ P. Jenni,⁵⁰,gg J. Jeong,⁴⁴ N. Jeong,⁴⁴

S. Jézéquel,⁵ H. Ji,¹⁷⁸ J. Jia,¹⁵² H. Jiang,⁷⁶ Y. Jiang,⁵⁸a Z. Jiang,¹⁵⁰,hh S. Jiggins,⁵⁰ F. A. Jimenez Morales,³⁷

J. Jimenez Pena,¹⁷¹ S. Jin,¹⁵c A. Jinaru,²⁷b O. Jinnouchi,¹⁶² H. Jivan,³²c P. Johansson,¹⁴⁶ K. A. Johns,⁷ C. A. Johnson,⁶³

W. J. Johnson,¹⁴⁵ K. Jon-And,⁴³a,⁴³b R. W. L. Jones,⁸⁷ S. D. Jones,¹⁵³ S. Jones,⁷ T. J. Jones,⁸⁸ J. Jongmanns,⁵⁹a

P. M. Jorge,¹³⁷a,¹³⁷b J. Jovicevic,¹⁶⁵a X. Ju,¹⁸ J. J. Junggeburth,¹¹³ A. Juste Rozas,¹⁴,z A. Kaczmarska,⁸² M. Kado,¹²⁹

H. Kagan,¹²³ M. Kagan,¹⁵⁰ T. Kaji,¹⁷⁶ E. Kajomovitz,¹⁵⁷ C. W. Kalderon,⁹⁴ A. Kaluza,⁹⁷ S. Kama,⁴¹ A. Kamenshchikov,¹²¹

L. Kanjir,⁸⁹ Y. Kano,¹⁶⁰ V. A. Kantserov,¹¹⁰ J. Kanzaki,⁷⁹ B. Kaplan,¹²² L. S. Kaplan,¹⁷⁸ D. Kar,³²c M. J. Kareem,¹⁶⁵b

E. Karentzos,¹⁰ S. N. Karpov,⁷⁷ Z. M. Karpova,⁷⁷ V. Kartvelishvili,⁸⁷ A. N. Karyukhin,¹²¹ L. Kashif,¹⁷⁸ R. D. Kass,¹²³

A. Kastanas,¹⁵¹ Y. Kataoka,¹⁶⁰ C. Kato,⁵⁸d,⁵⁸c J. Katzy,⁴⁴ K. Kawade,⁸⁰ K. Kawagoe,⁸⁵ T. Kawamoto,¹⁶⁰ G. Kawamura,⁵¹

E. F. Kay,⁸⁸ V. F. Kazanin,¹²⁰b,¹²⁰a R. Keeler,¹⁷³ R. Kehoe,⁴¹ J. S. Keller,³³ E. Kellermann,⁹⁴ J. J. Kempster,²¹ J. Kendrick,²¹

O. Kepka,¹³⁸ S. Kersten,¹⁷⁹ B. P. Kerševan,⁸⁹ R. A. Keyes,¹⁰¹ M. Khader,¹⁷⁰ F. Khalil-Zada,¹³ A. Khanov,¹²⁶

A. G. Kharlamov,¹²⁰b,¹²⁰a T. Kharlamova,¹²⁰b,¹²⁰a E. E. Khoda,¹⁷² A. Khodinov,¹⁶³ T. J. Khoo,⁵² E. Khramov,⁷⁷

J. Khubua,¹⁵⁶b S. Kido,⁸⁰ M. Kiehn,⁵² C. R. Kilby,⁹¹ Y. K. Kim,³⁶ N. Kimura,⁶⁴a,⁶⁴c O. M. Kind,¹⁹ B. T. King,⁸⁸

D. Kirchmeier,⁴⁶ J. Kirk,¹⁴¹ A. E. Kiryunin,¹¹³ T. Kishimoto,¹⁶⁰ D. Kisielewska,⁸¹a V. Kitali,⁴⁴ O. Kivernyk,⁵ E. Kladiva,²⁸b,a

T. Klapdor-Kleingrothaus,⁵⁰ M. H. Klein,¹⁰³ M. Klein,⁸⁸ U. Klein,⁸⁸ K. Kleinknecht,⁹⁷ P. Klimek,¹¹⁹ A. Klimentov,²⁹

R. Klingenberg,⁴⁵,a T. Klingl,²⁴ T. Klioutchnikova,³⁵ F. F. Klitzner,¹¹² P. Kluit,¹¹⁸ S. Kluth,¹¹³ E. Kneringer,⁷⁴

E. B. F. G. Knoops,⁹⁹ A. Knue,⁵⁰ A. Kobayashi,¹⁶⁰ D. Kobayashi,⁸⁵ T. Kobayashi,¹⁶⁰ M. Kobel,⁴⁶ M. Kocian,¹⁵⁰ P. Kodys,¹⁴⁰

P. T. Koenig,²⁴ T. Koffas,³³ E. Koffeman,¹¹⁸ N. M. Köhler,¹¹³ T. Koi,¹⁵⁰ M. Kolb,⁵⁹b I. Koletsou,⁵ T. Kondo,⁷⁹

N. Kondrashova,⁵⁸c K. Köneke,⁵⁰ A. C. König,¹¹⁷ T. Kono,⁷⁹ R. Konoplich,¹²²,ii V. Konstantinides,⁹² N. Konstantinidis,⁹²

B. Konya,⁹⁴ R. Kopeliansky,⁶³ S. Koperny,⁸¹a K. Korcyl,⁸² K. Kordas,¹⁵⁹ G. Koren,¹⁵⁸ A. Korn,⁹² I. Korolkov,¹⁴

E. V. Korolkova,¹⁴⁶ N. Korotkova,¹¹¹ O. Kortner,¹¹³ S. Kortner,¹¹³ T. Kosek,¹⁴⁰ V. V. Kostyukhin,²⁴ A. Kotwal,⁴⁷

A. Koulouris,¹⁰ A. Kourkoumeli-Charalampidi,⁶⁸a,⁶⁸b C. Kourkoumelis,⁹ E. Kourlitis,¹⁴⁶ V. Kouskoura,²⁹

A. B. Kowalewska,⁸² R. Kowalewski,¹⁷³ T. Z. Kowalski,⁸¹a C. Kozakai,¹⁶⁰ W. Kozanecki,¹⁴² A. S. Kozhin,¹²¹

V. A. Kramarenko,¹¹¹ G. Kramberger,⁸⁹ D. Krasnopevtsev,⁵⁸a M. W. Krasny,¹³³ A. Krasznahorkay,³⁵ D. Krauss,¹¹³

J. A. Kremer,⁸¹a J. Kretzschmar,⁸⁸ P. Krieger,¹⁶⁴ K. Krizka,¹⁸ K. Kroeninger,⁴⁵ H. Kroha,¹¹³ J. Kroll,¹³⁸ J. Kroll,¹³⁴

J. Krstic,¹⁶ U. Kruchonak,⁷⁷ H. Krüger,²⁴ N. Krumnack,⁷⁶ M. C. Kruse,⁴⁷ T. Kubota,¹⁰² S. Kuday,⁴b J. T. Kuechler,¹⁷⁹

S. Kuehn,³⁵ A. Kugel,⁵⁹a F. Kuger,¹⁷⁴ T. Kuhl,⁴⁴ V. Kukhtin,⁷⁷ R. Kukla,⁹⁰ Y. Kulchitsky,¹⁰⁵ S. Kuleshov,¹⁴⁴b

Y. P. Kulinich,¹⁷⁰ M. Kuna,⁵⁶ T. Kunigo,⁸³ A. Kupco,¹³⁸ T. Kupfer,⁴⁵ O. Kuprash,¹⁵⁸ H. Kurashige,⁸⁰ L. L. Kurchaninov,¹⁶⁵a

Y. A. Kurochkin,¹⁰⁵ M. G. Kurth,¹⁵d E. S. Kuwertz,³⁵ M. Kuze,¹⁶² A. K. Kvam,¹⁴⁵ J. Kvita,¹²⁷ T. Kwan,¹⁰¹ A. La Rosa,¹¹³

J. L. La Rosa Navarro,⁷⁸d L. La Rotonda,⁴⁰b,⁴⁰a F. La Ruffa,⁴⁰b,⁴⁰a C. Lacasta,¹⁷¹ F. Lacava,⁷⁰a,⁷⁰b J. Lacey,⁴⁴ D. P. J. Lack,⁹⁸

H. Lacker,¹⁹ D. Lacour,¹³³ E. Ladygin,⁷⁷ R. Lafaye,⁵ B. Laforge,¹³³ T. Lagouri,³²c S. Lai,⁵¹ S. Lammers,⁶³ W. Lampl,⁷

E. Lançon,²⁹ U. Landgraf,⁵⁰ M. P. J. Landon,⁹⁰ M. C. Lanfermann,⁵² V. S. Lang,⁴⁴ J. C. Lange,¹⁴ R. J. Langenberg,³⁵

A. J. Lankford,¹⁶⁸ F. Lanni,²⁹ K. Lantzsch,²⁴ A. Lanza,⁶⁸a A. Lapertosa,⁵³b,⁵³a S. Laplace,¹³³ J. F. Laporte,¹⁴² T. Lari,⁶⁶a
F. Lasagni Manghi,²³b,²³a M. Lassnig,³⁵ T. S. Lau,⁶¹a A. Laudrain,¹²⁹ M. Lavorgna,⁶⁷a,⁶⁷b A. T. Law,¹⁴³ M. Lazzaroni,⁶⁶a,⁶⁶b B. Le,¹⁰² O. Le Dortz,¹³³ E. Le Guirriec,⁹⁹ E. P. Le Quilleuc,¹⁴² M. LeBlanc,⁷ T. LeCompte,⁶ F. Ledroit-Guillon,⁵⁶

C. A. Lee,²⁹ G. R. Lee,¹⁴⁴a L. Lee,⁵⁷ S. C. Lee,¹⁵⁵ B. Lefebvre,¹⁰¹ M. Lefebvre,¹⁷³ F. Legger,¹¹² C. Leggett,¹⁸ K. Lehmann,¹⁴⁹ N. Lehmann,¹⁷⁹ G. Lehmann Miotto,³⁵ W. A. Leight,⁴⁴ A. Leisos,¹⁵⁹,⁷j M. A. L. Leite,⁷⁸d R. Leitner,¹⁴⁰ D. Lellouch,¹⁷⁷

B. Lemmer,⁵¹ K. J. C. Leney,⁹² T. Lenz,²⁴ B. Lenzi,³⁵ R. Leone,⁷ S. Leone,⁶⁹a C. Leonidopoulos,⁴⁸ G. Lerner,¹⁵³ C. Leroy,¹⁰⁷ R. Les,¹⁶⁴ A. A. J. Lesage,¹⁴² C. G. Lester,³¹ M. Levchenko,¹³⁵ J. Levêque,⁵ D. Levin,¹⁰³ L. J. Levinson,¹⁷⁷ D. Lewis,⁹⁰

B. Li,¹⁰³ C-Q. Li,⁵⁸a,⁷k H. Li,⁵⁸b L. Li,⁵⁸c M. Li,¹⁵a Q. Li,¹⁵d Q. Y. Li,⁵⁸a S. Li,⁵⁸d,⁵⁸c X. Li,⁵⁸c Y. Li,¹⁴⁸ Z. Liang,¹⁵a B. Liberti,⁷¹a A. Liblong,¹⁶⁴ K. Lie,⁶¹c S. Liem,¹¹⁸ A. Limosani,¹⁵⁴ C. Y. Lin,³¹ K. Lin,¹⁰⁴ T. H. Lin,⁹⁷ R. A. Linck,⁶³ J. H. Lindon,²¹

B. E. Lindquist,¹⁵² A. L. Lionti,⁵² E. Lipeles,¹³⁴ A. Lipniacka,¹⁷ M. Lisovyi,⁵⁹b T. M. Liss,¹⁷⁰,¹¹ A. Lister,¹⁷² A. M. Litke,¹⁴³

J. D. Little,⁸ B. Liu,⁷⁶ B. L. Liu,⁶ H. B. Liu,²⁹ H. Liu,¹⁰³ J. B. Liu,⁵⁸a J. K. K. Liu,¹³² K. Liu,¹³³ M. Liu,⁵⁸a P. Liu,¹⁸ Y. Liu,¹⁵a Y. L. Liu,⁵⁸a Y. W. Liu,⁵⁸a M. Livan,⁶⁸a,⁶⁸b A. Lleres,⁵⁶ J. Llorente Merino,¹⁵a S. L. Lloyd,⁹⁰ C. Y. Lo,⁶¹b F. Lo Sterzo,⁴¹

E. M. Lobodzinska,⁴⁴ P. Loch,⁷ T. Lohse,¹⁹ K. Lohwasser,¹⁴⁶ M. Lokajicek,¹³⁸ B. A. Long,²⁵ J. D. Long,¹⁷⁰ R. E. Long,⁸⁷

L. Longo,⁶⁵a,⁶⁵b K. A. Looper,¹²³ J. A. Lopez,¹⁴⁴b I. Lopez Paz,¹⁴ A. Lopez Solis,¹⁴⁶ J. Lorenz,¹¹² N. Lorenzo Martinez,⁵

M. Losada,²² P. J. Lösel,¹¹² A. Lösle,⁵⁰ X. Lou,⁴⁴ X. Lou,¹⁵a A. Lounis,¹²⁹ J. Love,⁶ P. A. Love,⁸⁷ J. J. Lozano Bahilo,¹⁷¹

H. Lu,⁶¹a M. Lu,⁵⁸a N. Lu,¹⁰³ Y. J. Lu,⁶² H. J. Lubatti,¹⁴⁵ C. Luci,⁷⁰a,⁷⁰b A. Lucotte,⁵⁶ C. Luedtke,⁵⁰ F. Luehring,⁶³ I. Luise,¹³³ L. Luminari,⁷⁰a B. Lund-Jensen,¹⁵¹ M. S. Lutz,¹⁰⁰ P. M. Luzi,¹³³ D. Lynn,²⁹ R. Lysak,¹³⁸ E. Lytken,⁹⁴ F. Lyu,¹⁵a

V. Lyubushkin,⁷⁷ H. Ma,²⁹ L. L. Ma,⁵⁸b Y. Ma,⁵⁸b G. Maccarrone,⁴⁹ A. Macchiolo,¹¹³ C. M. Macdonald,¹⁴⁶

J. Machado Miguens,¹³⁴,¹³⁷b D. Madaffari,¹⁷¹ R. Madar,³⁷ W. F. Mader,⁴⁶ A. Madsen,⁴⁴ N. Madysa,⁴⁶ J. Maeda,⁸⁰

K. Maekawa,¹⁶⁰ S. Maeland,¹⁷ T. Maeno,²⁹ A. S. Maevskiy,¹¹¹ V. Magerl,⁵⁰ C. Maidantchik,⁷⁸b T. Maier,¹¹²

A. Maio,¹³⁷a,¹³⁷b,¹³⁷d O. Majersky,²⁸a S. Majewski,¹²⁸ Y. Makida,⁷⁹ N. Makovec,¹²⁹ B. Malaescu,¹³³ Pa. Malecki,⁸²

V. P. Maleev,¹³⁵ F. Malek,⁵⁶ U. Mallik,⁷⁵ D. Malon,⁶ C. Malone,³¹ S. Maltezos,¹⁰ S. Malyukov,³⁵ J. Mamuzic,¹⁷¹

G. Mancini,⁴⁹ I. Mandić,⁸⁹ J. Maneira,¹³⁷a L. Manhaes de Andrade Filho,⁷⁸a J. Manjarres Ramos,⁴⁶ K. H. Mankinen,⁹⁴

A. Mann,¹¹² A. Manousos,⁷⁴ B. Mansoulie,¹⁴² J. D. Mansour,¹⁵a M. Mantoani,⁵¹ S. Manzoni,⁶⁶a,⁶⁶b G. Marceca,³⁰

L. March,⁵² L. Marchese,¹³² G. Marchiori,¹³³ M. Marcisovsky,¹³⁸ C. A. Marin Tobon,³⁵ M. Marjanovic,³⁷ D. E. Marley,¹⁰³

F. Marroquim,⁷⁸b Z. Marshall,¹⁸ M. U. F. Martensson,¹⁶⁹ S. Marti-Garcia,¹⁷¹ C. B. Martin,¹²³ T. A. Martin,¹⁷⁵ V. J. Martin,⁴⁸ B. Martin dit Latour,¹⁷ M. Martinez,¹⁴,⁷ V. I. Martinez Outschoorn,¹⁰⁰ S. Martin-Haugh,¹⁴¹ V. S. Martoiu,²⁷b

A. C. Martyniuk,⁹² A. Marzin,³⁵ L. Masetti,⁹⁷ T. Mashimo,¹⁶⁰ R. Mashinistov,¹⁰⁸ J. Masik,⁹⁸ A. L. Maslennikov,¹²⁰b,¹²⁰a

L. H. Mason,¹⁰² L. Massa,⁷¹a,⁷¹b P. Massarotti,⁶⁷a,⁶⁷b P. Mastrandrea,⁵ A. Mastroberardino,⁴⁰b,⁴⁰a T. Masubuchi,¹⁶⁰

P. Mättig,¹⁷⁹ J. Maurer,²⁷b B. Maček,⁸⁹ S. J. Maxfield,⁸⁸ D. A. Maximov,¹²⁰b,¹²⁰a R. Mazini,¹⁵⁵ I. Maznas,¹⁵⁹ S. M. Mazza,¹⁴³

N. C. Mc Fadden,¹¹⁶ G. Mc Goldrick,¹⁶⁴ S. P. Mc Kee,¹⁰³ A. McCarn,¹⁰³ T. G. McCarthy,¹¹³ L. I. McClymont,⁹²

E. F. McDonald,¹⁰² J. A. Mcfayden,³⁵ G. Mchedlidze,⁵¹ M. A. McKay,⁴¹ K. D. McLean,¹⁷³ S. J. McMahon,¹⁴¹

P. C. McNamara,¹⁰² C. J. McNicol,¹⁷⁵ R. A. McPherson,¹⁷³,⁰ J. E. Mdhluli,³²c Z. A. Meadows,¹⁰⁰ S. Meehan,¹⁴⁵

T. M. Megy,⁵⁰ S. Mehlhase,¹¹² A. Mehta,⁸⁸ T. Meideck,⁵⁶ B. Meirose,⁴² D. Melini,¹⁷¹,mm B. R. Mellado Garcia,³²c

J. D. Mellenthin,⁵¹ M. Melo,²⁸a F. Meloni,⁴⁴ A. Melzer,²⁴ S. B. Menary,⁹⁸ E. D. Mendes Gouveia,¹³⁷a L. Meng,⁸⁸

X. T. Meng,¹⁰³ A. Mengarelli,²³b,²³a S. Menke,¹¹³ E. Meoni,⁴⁰b,⁴⁰a S. Mergelmeyer,¹⁹ C. Merlassino,²⁰ P. Mermod,⁵²

L. Merola,⁶⁷a,⁶⁷b C. Meroni,⁶⁶a F. S. Merritt,³⁶ A. Messina,⁷⁰a,⁷⁰b J. Metcalfe,⁶ A. S. Mete,¹⁶⁸ C. Meyer,¹³⁴ J. Meyer,¹⁵⁷ J.-P. Meyer,¹⁴² H. Meyer Zu Theenhausen,⁵⁹a F. Miano,¹⁵³ R. P. Middleton,¹⁴¹ L. Mijović,⁴⁸ G. Mikenberg,¹⁷⁷

M. Mikestikova,¹³⁸ M. Mikuž,⁸⁹ M. Milesi,¹⁰² A. Milic,¹⁶⁴ D. A. Millar,⁹⁰ D. W. Miller,³⁶ A. Milov,¹⁷⁷ D. A. Milstead,⁴³a,⁴³b

A. A. Minaenko,¹²¹ M. Miñano Moya,¹⁷¹ I. A. Minashvili,¹⁵⁶b A. I. Mincer,¹²² B. Mindur,⁸¹a M. Mineev,⁷⁷ Y. Minegishi,¹⁶⁰

Y. Ming,¹⁷⁸ L. M. Mir,¹⁴ A. Mirto,⁶⁵a,⁶⁵b K. P. Mistry,¹³⁴ T. Mitani,¹⁷⁶ J. Mitrevski,¹¹² V. A. Mitsou,¹⁷¹ A. Miucci,²⁰

P. S. Miyagawa,¹⁴⁶ A. Mizukami,⁷⁹ J. U. Mjörnmark,⁹⁴ T. Mkrtchyan,¹⁸¹ M. Mlynarikova,¹⁴⁰ T. Moa,⁴³a,⁴³b K. Mochizuki,¹⁰⁷ P. Mogg,⁵⁰ S. Mohapatra,³⁸ S. Molander,⁴³a,⁴³b R. Moles-Valls,²⁴ M. C. Mondragon,¹⁰⁴ K. Mönig,⁴⁴ J. Monk,³⁹

E. Monnier,⁹⁹ A. Montalbano,¹⁴⁹ J. Montejo Berlingen,³⁵ F. Monticelli,⁸⁶ S. Monzani,⁶⁶a N. Morange,¹²⁹ D. Moreno,²²

M. Moreno Llácer,³⁵ P. Morettini,⁵³b M. Morgenstern,¹¹⁸ S. Morgenstern,⁴⁶ D. Mori,¹⁴⁹ M. Morii,⁵⁷ M. Morinaga,¹⁷⁶

V. Morisbak,¹³¹ A. K. Morley,³⁵ G. Mornacchi,³⁵ A. P. Morris,⁹² J. D. Morris,⁹⁰ L. Morvaj,¹⁵² P. Moschovakos,¹⁰

M. Mosidze,¹⁵⁶b H. J. Moss,¹⁴⁶ J. Moss,¹⁵⁰,nn K. Motohashi,¹⁶² R. Mount,¹⁵⁰ E. Mountricha,³⁵ E. J. W. Moyse,¹⁰⁰

S. Muanza,⁹⁹ F. Mueller,¹¹³ J. Mueller,¹³⁶ R. S. P. Mueller,¹¹² D. Muenstermann,⁸⁷ G. A. Mullier,²⁰ F. J. Munoz Sanchez,⁹⁸

P. Murin,²⁸b W. J. Murray,¹⁷⁵,¹⁴¹ A. Murrone,⁶⁶a,⁶⁶b M. Muškinja,⁸⁹ C. Mwewa,³²a A. G. Myagkov,¹²¹,oo J. Myers,¹²⁸

M. Myska,¹³⁹ B. P. Nachman,¹⁸ O. Nackenhorst,⁴⁵ K. Nagai,¹³² K. Nagano,⁷⁹ Y. Nagasaka,⁶⁰ M. Nagel,⁵⁰ E. Nagy,⁹⁹
A. M. Nairz,³⁵ Y. Nakahama,¹¹⁵ K. Nakamura,⁷⁹ T. Nakamura,¹⁶⁰ I. Nakano,¹²⁴ H. Nanjo,¹³⁰ F. Napolitano,⁵⁹a

R. F. Naranjo Garcia,⁴⁴ R. Narayan,¹¹ D. I. Narrias Villar,⁵⁹a I. Naryshkin,¹³⁵ T. Naumann,⁴⁴ G. Navarro,²² R. Nayyar,⁷
H. A. Neal,¹⁰³a P. Y. Nechaeva,¹⁰⁸ T. J. Neep,¹⁴² A. Negri,⁶⁸a,⁶⁸b M. Negrini,²³b S. Nektarijevic,¹¹⁷ C. Nellist,⁵¹

M. E. Nelson,¹³² S. Nemecek,¹³⁸ P. Nemethy,¹²² M. Nessi,³⁵,pp M. S. Neubauer,¹⁷⁰ M. Neumann,¹⁷⁹ P. R. Newman,²¹

T. Y. Ng,⁶¹c Y. S. Ng,¹⁹ H. D. N. Nguyen,⁹⁹ T. Nguyen Manh,¹⁰⁷ E. Nibigira,³⁷ R. B. Nickerson,¹³² R. Nicolaidou,¹⁴²

J. Nielsen,¹⁴³ N. Nikiforou,¹¹ V. Nikolaenko,¹²¹,oo I. Nikolic-Audit,¹³³ K. Nikolopoulos,²¹ P. Nilsson,²⁹ Y. Ninomiya,⁷⁹

A. Nisati,⁷⁰a N. Nishu,⁵⁸c R. Nisius,¹¹³ I. Nitsche,⁴⁵ T. Nitta,¹⁷⁶ T. Nobe,¹⁶⁰ Y. Noguchi,⁸³ M. Nomachi,¹³⁰ I. Nomidis,¹³³

M. A. Nomura,²⁹ T. Nooney,⁹⁰ M. Nordberg,³⁵ N. Norjoharuddeen,¹³² T. Novak,⁸⁹ O. Novgorodova,⁴⁶ R. Novotny,¹³⁹

L. Nozka,¹²⁷ K. Ntekas,¹⁶⁸ E. Nurse,⁹² F. Nuti,¹⁰² F. G. Oakham,³³,f H. Oberlack,¹¹³ T. Obermann,²⁴ J. Ocariz,¹³³ A. Ochi,⁸⁰

I. Ochoa,³⁸ J. P. Ochoa-Ricoux,¹⁴⁴a K. O'Connor,²⁶ S. Oda,⁸⁵ S. Odaka,⁷⁹ S. Oerdek,⁵¹ A. Oh,⁹⁸ S. H. Oh,⁴⁷ C. C. Ohm,¹⁵¹
H. Oide,⁵³b,⁵³a M. L. Ojeda,¹⁶⁴ H. Okawa,¹⁶⁶ Y. Okazaki,⁸³ Y. Okumura,¹⁶⁰ T. Okuyama,⁷⁹ A. Olariu,²⁷b

L. F. Oleiro Seabra,¹³⁷a S. A. Olivares Pino,¹⁴⁴a D. Oliveira Damazio,²⁹ J. L. Oliver,¹ M. J. R. Olsson,³⁶ A. Olszewski,⁸²
J. Olszowska,⁸² D. C. O'Neil,¹⁴⁹ A. Onofre,¹³⁷a,¹³⁷e K. Onogi,¹¹⁵ P. U. E. Onyisi,¹¹ H. Oppen,¹³¹ M. J. Oreglia,³⁶

G. E. Orellana,⁸⁶ Y. Oren,¹⁵⁸ D. Orestano,⁷²a,⁷²b E. C. Orgill,⁹⁸ N. Orlando,⁶¹b A. A. O'Rourke,⁴⁴ R. S. Orr,¹⁶⁴

B. Osculati,⁵³b,⁵³a,ª V. O'Shea,⁵⁵ R. Ospanov,⁵⁸a G. Otero y Garzon,³⁰ H. Otono,⁸⁵ M. Ouchrif,³⁴d F. Ould-Saada,¹³¹

A. Ouraou,¹⁴² Q. Ouyang,¹⁵a M. Owen,⁵⁵ R. E. Owen,²¹ V. E. Ozcan,¹²⁶ N. Ozturk,⁸ J. Pacalt,¹²⁷ H. A. Pacey,³¹ K. Pachal,¹⁴⁹

A. Pacheco Pages,¹⁴ L. Pacheco Rodriguez,¹⁴² C. Padilla Aranda,¹⁴ S. Pagan Griso,¹⁸ M. Paganini,¹⁸⁰ G. Palacino,⁶³

S. Palazzo,⁴⁰b,⁴⁰a S. Palestini,³⁵ M. Palka,⁸¹b D. Pallin,³⁷ I. Panagoulias,¹⁰ C. E. Pandini,³⁵ J. G. Panduro Vazquez,⁹¹ P. Pani,³⁵
G. Panizzo,⁶⁴a,⁶⁴c L. Paolozzi,⁵² T. D. Papadopoulou,¹⁰ K. Papageorgiou,⁹,v A. Paramonov,⁶ D. Paredes Hernandez,⁶¹b

S. R. Paredes Saenz,¹³² B. Parida,¹⁶³ A. J. Parker,⁸⁷ K. A. Parker,⁴⁴ M. A. Parker,³¹ F. Parodi,⁵³b,⁵³a J. A. Parsons,³⁸

U. Parzefall,⁵⁰ V. R. Pascuzzi,¹⁶⁴ J. M. P. Pasner,¹⁴³ E. Pasqualucci,⁷⁰a S. Passaggio,⁵³b F. Pastore,⁹¹ P. Pasuwan,⁴³a,⁴³b

S. Pataraia,⁹⁷ J. R. Pater,⁹⁸ A. Pathak,¹⁷⁸,g T. Pauly,³⁵ B. Pearson,¹¹³ M. Pedersen,¹³¹ L. Pedraza Diaz,¹¹⁷ R. Pedro,¹³⁷a,¹³⁷b

S. V. Peleganchuk,¹²⁰b,¹²⁰a O. Penc,¹³⁸ C. Peng,¹⁵d H. Peng,⁵⁸a B. S. Peralva,⁷⁸a M. M. Perego,¹⁴² A. P. Pereira Peixoto,¹³⁷a
D. V. Perepelitsa,²⁹ F. Peri,¹⁹ L. Perini,⁶⁶a,⁶⁶b H. Pernegger,³⁵ S. Perrella,⁶⁷a,⁶⁷b V. D. Peshekhonov,⁷⁷,a K. Peters,⁴⁴

R. F. Y. Peters,⁹⁸ B. A. Petersen,³⁵ T. C. Petersen,³⁹ E. Petit,⁵⁶ A. Petridis,¹ C. Petridou,¹⁵⁹ P. Petroff,¹²⁹ M. Petrov,¹³²

F. Petrucci,⁷²a,⁷²b M. Pettee,¹⁸⁰ N. E. Pettersson,¹⁰⁰ A. Peyaud,¹⁴² R. Pezoa,¹⁴⁴b T. Pham,¹⁰² F. H. Phillips,¹⁰⁴ P. W. Phillips,¹⁴¹
M. W. Phipps,¹⁷⁰ G. Piacquadio,¹⁵² E. Pianori,¹⁸ A. Picazio,¹⁰⁰ M. A. Pickering,¹³² R. H. Pickles,⁹⁸ R. Piegaia,³⁰

J. E. Pilcher,³⁶ A. D. Pilkington,⁹⁸ M. Pinamonti,⁷¹a,⁷¹b J. L. Pinfold,³ M. Pitt,¹⁷⁷ M.-A. Pleier,²⁹ V. Pleskot,¹⁴⁰

E. Plotnikova,⁷⁷ D. Pluth,⁷⁶ P. Podberezko,¹²⁰b,¹²⁰a R. Poettgen,⁹⁴ R. Poggi,⁵² L. Poggioli,¹²⁹ I. Pogrebnyak,¹⁰⁴ D. Pohl,²⁴

I. Pokharel,⁵¹ G. Polesello,⁶⁸a A. Poley,¹⁸ A. Policicchio,⁷⁰a,⁷⁰b R. Polifka,³⁵ A. Polini,²³b C. S. Pollard,⁴⁴ V. Polychronakos,²⁹

D. Ponomarenko,¹¹⁰ L. Pontecorvo,³⁵ G. A. Popeneciu,²⁷d D. M. Portillo Quintero,¹³³ S. Pospisil,¹³⁹ K. Potamianos,⁴⁴

I. N. Potrap,⁷⁷ C. J. Potter,³¹ H. Potti,¹¹ T. Poulsen,⁹⁴ J. Poveda,³⁵ T. D. Powell,¹⁴⁶ M. E. Pozo Astigarraga,³⁵ P. Pralavorio,⁹⁹

S. Prell,⁷⁶ D. Price,⁹⁸ M. Primavera,⁶⁵a S. Prince,¹⁰¹ N. Proklova,¹¹⁰ K. Prokofiev,⁶¹c F. Prokoshin,¹⁴⁴b S. Protopopescu,²⁹
J. Proudfoot,⁶ M. Przybycien,⁸¹a A. Puri,¹⁷⁰ P. Puzo,¹²⁹ J. Qian,¹⁰³ Y. Qin,⁹⁸ A. Quadt,⁵¹ M. Queitsch-Maitland,⁴⁴

A. Qureshi,¹ P. Rados,¹⁰² F. Ragusa,⁶⁶a,⁶⁶b G. Rahal,⁹⁵ J. A. Raine,⁵² S. Rajagopalan,²⁹ A. Ramirez Morales,⁹⁰ T. Rashid,¹²⁹

S. Raspopov,⁵ M. G. Ratti,⁶⁶a,⁶⁶b D. M. Rauch,⁴⁴ F. Rauscher,¹¹² S. Rave,⁹⁷ B. Ravina,¹⁴⁶ I. Ravinovich,¹⁷⁷ J. H. Rawling,⁹⁸

M. Raymond,³⁵ A. L. Read,¹³¹ N. P. Readioff,⁵⁶ M. Reale,⁶⁵a,⁶⁵b D. M. Rebuzzi,⁶⁸a,⁶⁸b A. Redelbach,¹⁷⁴ G. Redlinger,²⁹

R. Reece,¹⁴³ R. G. Reed,³²c K. Reeves,⁴² L. Rehnisch,¹⁹ J. Reichert,¹³⁴ D. Reikher,¹⁵⁸ A. Reiss,⁹⁷ C. Rembser,³⁵ H. Ren,¹⁵d

M. Rescigno,⁷⁰a S. Resconi,⁶⁶a E. D. Resseguie,¹³⁴ S. Rettie,¹⁷² E. Reynolds,²¹ O. L. Rezanova,¹²⁰b,¹²⁰a P. Reznicek,¹⁴⁰

E. Ricci,⁷³a,⁷³b R. Richter,¹¹³ S. Richter,⁴⁴ E. Richter-Was,⁸¹b O. Ricken,²⁴ M. Ridel,¹³³ P. Rieck,¹¹³ C. J. Riegel,¹⁷⁹

O. Rifki,⁴⁴ M. Rijssenbeek,¹⁵² A. Rimoldi,⁶⁸a,⁶⁸b M. Rimoldi,²⁰ L. Rinaldi,²³b G. Ripellino,¹⁵¹ B. Ristić,⁸⁷ E. Ritsch,³⁵

I. Riu,¹⁴ J. C. Rivera Vergara,¹⁴⁴a F. Rizatdinova,¹²⁶ E. Rizvi,⁹⁰ C. Rizzi,¹⁴ R. T. Roberts,⁹⁸ S. H. Robertson,¹⁰¹,o

D. Robinson,³¹ J. E. M. Robinson,⁴⁴ A. Robson,⁵⁵ E. Rocco,⁹⁷ C. Roda,⁶⁹a,⁶⁹b Y. Rodina,⁹⁹ S. Rodriguez Bosca,¹⁷¹

A. Rodriguez Perez,¹⁴ D. Rodriguez Rodriguez,¹⁷¹ A. M. Rodríguez Vera,¹⁶⁵b S. Roe,³⁵ C. S. Rogan,⁵⁷ O. Røhne,¹³¹

R. Röhrig,¹¹³ C. P. A. Roland,⁶³ J. Roloff,⁵⁷ A. Romaniouk,¹¹⁰ M. Romano,²³b,²³a N. Rompotis,⁸⁸ M. Ronzani,¹²² L. Roos,¹³³
S. Rosati,⁷⁰a K. Rosbach,⁵⁰ P. Rose,¹⁴³ N-A. Rosien,⁵¹ E. Rossi,⁴⁴ E. Rossi,⁷²a,⁷²b E. Rossi,⁶⁷a,⁶⁷b L. P. Rossi,⁵³b

L. Rossini,⁶⁶a,⁶⁶b J. H. N. Rosten,³¹ R. Rosten,¹⁴ M. Rotaru,²⁷b J. Rothberg,¹⁴⁵ D. Rousseau,¹²⁹ D. Roy,³²c A. Rozanov,⁹⁹

Y. Rozen,¹⁵⁷ X. Ruan,³²c F. Rubio,¹⁵⁰ F. Rühr,⁵⁰ A. Ruiz-Martinez,¹⁷¹ Z. Rurikova,⁵⁰ N. A. Rusakovitch,⁷⁷ H. L. Russell,¹⁰¹

J. P. Rutherfoord,⁷ E. M. Rüttinger,⁴⁴,qq Y. F. Ryabov,¹³⁵ M. Rybar,¹⁷⁰ G. Rybkin,¹²⁹ S. Ryu,⁶ A. Ryzhov,¹²¹ G. F. Rzehorz,⁵¹
P. Sabatini,⁵¹ G. Sabato,¹¹⁸ S. Sacerdoti,¹²⁹ H. F.-W. Sadrozinski,¹⁴³ R. Sadykov,⁷⁷ F. Safai Tehrani,⁷⁰a P. Saha,¹¹⁹
M. Sahinsoy,⁵⁹a A. Sahu,¹⁷⁹ M. Saimpert,⁴⁴ M. Saito,¹⁶⁰ T. Saito,¹⁶⁰ H. Sakamoto,¹⁶⁰ A. Sakharov,¹²²,ii D. Salamani,⁵²
G. Salamanna,⁷²a,⁷²b J. E. Salazar Loyola,¹⁴⁴b D. Salek,¹¹⁸ P. H. Sales De Bruin,¹⁶⁹ D. Salihagic,¹¹³ A. Salnikov,¹⁵⁰ J. Salt,¹⁷¹
D. Salvatore,⁴⁰b,⁴⁰a F. Salvatore,¹⁵³ A. Salvucci,⁶¹a,⁶¹b,⁶¹c A. Salzburger,³⁵ J. Samarati,³⁵ D. Sammel,⁵⁰ D. Sampsonidis,¹⁵⁹
D. Sampsonidou,¹⁵⁹ J. Sánchez,¹⁷¹ A. Sanchez Pineda,⁶⁴a,⁶⁴c H. Sandaker,¹³¹ C. O. Sander,⁴⁴ M. Sandhoff,¹⁷⁹ C. Sandoval,²²
D. P. C. Sankey,¹⁴¹ M. Sannino,⁵³b,⁵³a Y. Sano,¹¹⁵ A. Sansoni,⁴⁹ C. Santoni,³⁷ H. Santos,¹³⁷a I. Santoyo Castillo,¹⁵³
A. Santra,¹⁷¹ A. Sapronov,⁷⁷ J. G. Saraiva,¹³⁷a,¹³⁷d O. Sasaki,⁷⁹ K. Sato,¹⁶⁶ E. Sauvan,⁵ P. Savard,¹⁶⁴,f N. Savic,¹¹³
R. Sawada,¹⁶⁰ C. Sawyer,¹⁴¹ L. Sawyer,⁹³,x C. Sbarra,²³b A. Sbrizzi,²³a T. Scanlon,⁹² J. Schaarschmidt,¹⁴⁵ P. Schacht,¹¹³
B. M. Schachtner,¹¹² D. Schaefer,³⁶ L. Schaefer,¹³⁴ J. Schaeffer,⁹⁷ S. Schaepe,³⁵ U. Schäfer,⁹⁷ A. C. Schaffer,¹²⁹
D. Schaile,¹¹² R. D. Schamberger,¹⁵² N. Scharnberg,⁹⁸ V. A. Schegelsky,¹³⁵ D. Scheirich,¹⁴⁰ F. Schenck,¹⁹ M. Schernau,¹⁶⁸
C. Schiavi,⁵³b,⁵³a S. Schier,¹⁴³ L. K. Schildgen,²⁴ Z. M. Schillaci,²⁶ E. J. Schioppa,³⁵ M. Schioppa,⁴⁰b,⁴⁰a K. E. Schleicher,⁵⁰
S. Schlenker,³⁵ K. R. Schmidt-Sommerfeld,¹¹³ K. Schmieden,³⁵ C. Schmitt,⁹⁷ S. Schmitt,⁴⁴ S. Schmitz,⁹⁷
J. C. Schmoeckel,⁴⁴ U. Schnoor,⁵⁰ L. Schoeffel,¹⁴² A. Schoening,⁵⁹b E. Schopf,²⁴ M. Schott,⁹⁷ J. F. P. Schouwenberg,¹¹⁷
J. Schovancova,³⁵ S. Schramm,⁵² A. Schulte,⁹⁷ H.-C. Schultz-Coulon,⁵⁹a M. Schumacher,⁵⁰ B. A. Schumm,¹⁴³
Ph. Schune,¹⁴² A. Schwartzman,¹⁵⁰ T. A. Schwarz,¹⁰³ Ph. Schwemling,¹⁴² R. Schwienhorst,¹⁰⁴ A. Sciandra,²⁴ G. Sciolla,²⁶
M. Scornajenghi,⁴⁰b,⁴⁰a F. Scuri,⁶⁹a F. Scutti,¹⁰² L. M. Scyboz,¹¹³ J. Searcy,¹⁰³ C. D. Sebastiani,⁷⁰a,⁷⁰b P. Seema,¹⁹
S. C. Seidel,¹¹⁶ A. Seiden,¹⁴³ T. Seiss,³⁶ J. M. Seixas,⁷⁸b G. Sekhniaidze,⁶⁷a K. Sekhon,¹⁰³ S. J. Sekula,⁴¹
N. Semprini-Cesari,²³b,²³a S. Sen,⁴⁷ S. Senkin,³⁷ C. Serfon,¹³¹ L. Serin,¹²⁹ L. Serkin,⁶⁴a,⁶⁴b M. Sessa,⁵⁸a H. Severini,¹²⁵
F. Sforza,¹⁶⁷ A. Sfyrla,⁵² E. Shabalina,⁵¹ J. D. Shahinian,¹⁴³ N. W. Shaikh,⁴³a,⁴³b L. Y. Shan,¹⁵a R. Shang,¹⁷⁰ J. T. Shank,²⁵
M. Shapiro,¹⁸ A. S. Sharma,¹ A. Sharma,¹³² P. B. Shatalov,¹⁰⁹ K. Shaw,¹⁵³ S. M. Shaw,⁹⁸ A. Shcherbakova,¹³⁵ Y. Shen,¹²⁵
N. Sherafati,³³ A. D. Sherman,²⁵ P. Sherwood,⁹² L. Shi,¹⁵⁵,rr S. Shimizu,⁷⁹ C. O. Shimmin,¹⁸⁰ M. Shimojima,¹¹⁴
I. P. J. Shipsey,¹³² S. Shirabe,⁸⁵ M. Shiyakova,⁷⁷ J. Shlomi,¹⁷⁷ A. Shmeleva,¹⁰⁸ D. Shoaleh Saadi,¹⁰⁷ M. J. Shochet,³⁶
S. Shojaii,¹⁰² D. R. Shope,¹²⁵ S. Shrestha,¹²³ E. Shulga,¹¹⁰ P. Sicho,¹³⁸ A. M. Sickles,¹⁷⁰ P. E. Sidebo,¹⁵¹
E. Sideras Haddad,³²c O. Sidiropoulou,³⁵ A. Sidoti,²³b,²³a F. Siegert,⁴⁶ Dj. Sijacki,¹⁶ J. Silva,¹³⁷a M. Silva Jr.,¹⁷⁸
M. V. Silva Oliveira,⁷⁸a S. B. Silverstein,⁴³a L. Simic,⁷⁷ S. Simion,¹²⁹ E. Simioni,⁹⁷ M. Simon,⁹⁷ R. Simoniello,⁹⁷
P. Sinervo,¹⁶⁴ N. B. Sinev,¹²⁸ M. Sioli,²³b,²³a G. Siragusa,¹⁷⁴ I. Siral,¹⁰³ S. Yu. Sivoklokov,¹¹¹ J. Sjölin,⁴³a,⁴³b P. Skubic,¹²⁵
M. Slater,²¹ T. Slavicek,¹³⁹ M. Slawinska,⁸² K. Sliwa,¹⁶⁷ R. Slovak,¹⁴⁰ V. Smakhtin,¹⁷⁷ B. H. Smart,⁵ J. Smiesko,²⁸a
N. Smirnov,¹¹⁰ S. Yu. Smirnov,¹¹⁰ Y. Smirnov,¹¹⁰ L. N. Smirnova,¹¹¹ O. Smirnova,⁹⁴ J. W. Smith,⁵¹ M. N. K. Smith,³⁸
M. Smizanska,⁸⁷ K. Smolek,¹³⁹ A. Smykiewicz,⁸² A. A. Snesarev,¹⁰⁸ I. M. Snyder,¹²⁸ S. Snyder,²⁹ R. Sobie,¹⁷³,o
A. M. Soffa,¹⁶⁸ A. Soffer,¹⁵⁸ A. Søgaard,⁴⁸ D. A. Soh,¹⁵⁵ G. Sokhrannyi,⁸⁹ C. A. Solans Sanchez,³⁵ M. Solar,¹³⁹
E. Yu. Soldatov,¹¹⁰ U. Soldevila,¹⁷¹ A. A. Solodkov,¹²¹ A. Soloshenko,⁷⁷ O. V. Solovyjanov,¹²¹ V. Solovyev,¹³⁵ P. Sommer,¹⁴⁶
H. Son,¹⁶⁷ W. Song,¹⁴¹ W. Y. Song,¹⁶⁵b A. Sopczak,¹³⁹ F. Sopkova,²⁸b C. L. Sotiropoulou,⁶⁹a,⁶⁹b S. Sottocornola,⁶⁸a,⁶⁸b
R. Soualah,⁶⁴a,⁶⁴c,ss A. M. Soukharev,¹²⁰b,¹²⁰a D. South,⁴⁴ B. C. Sowden,⁹¹ S. Spagnolo,⁶⁵a,⁶⁵b M. Spalla,¹¹³
M. Spangenberg,¹⁷⁵ F. Spanò,⁹¹ D. Sperlich,¹⁹ F. Spettel,¹¹³ T. M. Spieker,⁵⁹a R. Spighi,²³b G. Spigo,³⁵ L. A. Spiller,¹⁰²
D. P. Spiteri,⁵⁵ M. Spousta,¹⁴⁰ A. Stabile,⁶⁶a,⁶⁶b R. Stamen,⁵⁹a S. Stamm,¹⁹ E. Stanecka,⁸² R. W. Stanek,⁶ C. Stanescu,⁷²a
B. Stanislaus,¹³² M. M. Stanitzki,⁴⁴ B. Stapf,¹¹⁸ S. Stapnes,¹³¹ E. A. Starchenko,¹²¹ G. H. Stark,³⁶ J. Stark,⁵⁶ S. H. Stark,³⁹
P. Staroba,¹³⁸ P. Starovoitov,⁵⁹a S. Stärz,³⁵ R. Staszewski,⁸² M. Stegler,⁴⁴ P. Steinberg,²⁹ B. Stelzer,¹⁴⁹ H. J. Stelzer,³⁵
O. Stelzer-Chilton,¹⁶⁵a H. Stenzel,⁵⁴ T. J. Stevenson,⁹⁰ G. A. Stewart,³⁵ M. C. Stockton,¹²⁸ G. Stoicea,²⁷b P. Stolte,⁵¹
S. Stonjek,¹¹³ A. Straessner,⁴⁶ J. Strandberg,¹⁵¹ S. Strandberg,⁴³a,⁴³b M. Strauss,¹²⁵ P. Strizenec,²⁸b R. Ströhmer,¹⁷⁴
D. M. Strom,¹²⁸ R. Stroynowski,⁴¹ A. Strubig,⁴⁸ S. A. Stucci,²⁹ B. Stugu,¹⁷ J. Stupak,¹²⁵ N. A. Styles,⁴⁴ D. Su,¹⁵⁰ J. Su,¹³⁶
S. Suchek,⁵⁹a Y. Sugaya,¹³⁰ M. Suk,¹³⁹ V. V. Sulin,¹⁰⁸ M. J. Sullivan,⁸⁸ D. M. S. Sultan,⁵² S. Sultansoy,⁴c T. Sumida,⁸³
S. Sun,¹⁰³ X. Sun,³ K. Suruliz,¹⁵³ C. J. E. Suster,¹⁵⁴ M. R. Sutton,¹⁵³ S. Suzuki,⁷⁹ M. Svatos,¹³⁸ M. Swiatlowski,³⁶
S. P. Swift,² A. Sydorenko,⁹⁷ I. Sykora,²⁸a T. Sykora,¹⁴⁰ D. Ta,⁹⁷ K. Tackmann,⁴⁴,tt J. Taenzer,¹⁵⁸ A. Taffard,¹⁶⁸
R. Tafirout,¹⁶⁵a E. Tahirovic,⁹⁰ N. Taiblum,¹⁵⁸ H. Takai,²⁹ R. Takashima,⁸⁴ E. H. Takasugi,¹¹³ K. Takeda,⁸⁰ T. Takeshita,¹⁴⁷
Y. Takubo,⁷⁹ M. Talby,⁹⁹ A. A. Talyshev,¹²⁰b,¹²⁰a J. Tanaka,¹⁶⁰ M. Tanaka,¹⁶² R. Tanaka,¹²⁹ B. B. Tannenwald,¹²³
S. Tapia Araya,¹⁴⁴b S. Tapprogge,⁹⁷ A. Tarek Abouelfadl Mohamed,¹³³ S. Tarem,¹⁵⁷ G. Tarna,²⁷b,⁸ G. F. Tartarelli,⁶⁶a
P. Tas,¹⁴⁰ M. Tasevsky,¹³⁸ T. Tashiro,⁸³ E. Tassi,⁴⁰b,⁴⁰a A. Tavares Delgado,¹³⁷a,¹³⁷b Y. Tayalati,³⁴e A. C. Taylor,¹¹⁶
A. J. Taylor,⁴⁸ G. N. Taylor,¹⁰² P. T. E. Taylor,¹⁰² W. Taylor,¹⁶⁵b A. S. Tee,⁸⁷ P. Teixeira-Dias,⁹¹ H. Ten Kate,³⁵ P. K. Teng,¹⁵⁵
J. J. Teoh,¹¹⁸ S. Terada,⁷⁹ K. Terashi,¹⁶⁰ J. Terron,⁹⁶ S. Terzo,¹⁴ M. Testa,⁴⁹ R. J. Teuscher,¹⁶⁴,o S. J. Thais,¹⁸⁰
T. Theveneaux-Pelzer,⁴⁴ F. Thiele,³⁹ D. W. Thomas,⁹¹ J. P. Thomas,²¹ A. S. Thompson,⁵⁵ P. D. Thompson,²¹
L. A. Thomsen,¹⁸⁰ E. Thomson,¹³⁴ Y. Tian,³⁸ R. E. Ticse Torres,⁵¹ V. O. Tikhomirov,¹⁰⁸,uu Yu. A. Tikhonov,¹²⁰b,¹²⁰a
S. Timoshenko,¹¹⁰ P. Tipton,¹⁸⁰ S. Tisserant,⁹⁹ K. Todome,¹⁶² S. Todorova-Nova,⁵ S. Todt,⁴⁶ J. Tojo,⁸⁵ S. Tokár,²⁸a
K. Tokushuku,⁷⁹ E. Tolley,¹²³ K. G. Tomiwa,³²c M. Tomoto,¹¹⁵ L. Tompkins,¹⁵⁰,hh K. Toms,¹¹⁶ B. Tong,⁵⁷ P. Tornambe,⁵⁰
E. Torrence,¹²⁸ H. Torres,⁴⁶ E. Torró Pastor,¹⁴⁵ C. Tosciri,¹³² J. Toth,⁹⁹,vv F. Touchard,⁹⁹ D. R. Tovey,¹⁴⁶ C. J. Treado,¹²²
T. Trefzger,¹⁷⁴ F. Tresoldi,¹⁵³ A. Tricoli,²⁹ I. M. Trigger,¹⁶⁵a S. Trincaz-Duvoid,¹³³ M. F. Tripiana,¹⁴ W. Trischuk,¹⁶⁴
B. Trocmé,⁵⁶ A. Trofymov,¹²⁹ C. Troncon,⁶⁶a M. Trovatelli,¹⁷³ F. Trovato,¹⁵³ L. Truong,³²b M. Trzebinski,⁸² A. Trzupek,⁸²
F. Tsai,⁴⁴ J. C.-L. Tseng,¹³² P. V. Tsiareshka,¹⁰⁵ A. Tsirigotis,¹⁵⁹ N. Tsirintanis,⁹ V. Tsiskaridze,¹⁵² E. G. Tskhadadze,¹⁵⁶a
I. I. Tsukerman,¹⁰⁹ V. Tsulaia,¹⁸ S. Tsuno,⁷⁹ D. Tsybychev,¹⁵²,¹⁶³ Y. Tu,⁶¹b A. Tudorache,²⁷b V. Tudorache,²⁷b
T. T. Tulbure,²⁷a A. N. Tuna,⁵⁷ S. Turchikhin,⁷⁷ D. Turgeman,¹⁷⁷ I. Turk Cakir,⁴b,ww R. Turra,⁶⁶a P. M. Tuts,³⁸ E. Tzovara,⁹⁷
G. Ucchielli,²³b,²³a I. Ueda,⁷⁹ M. Ughetto,⁴³a,⁴³b F. Ukegawa,¹⁶⁶ G. Unal,³⁵ A. Undrus,²⁹ G. Unel,¹⁶⁸ F. C. Ungaro,¹⁰²
Y. Unno,⁷⁹ K. Uno,¹⁶⁰ J. Urban,²⁸b P. Urquijo,¹⁰² P. Urrejola,⁹⁷ G. Usai,⁸ J. Usui,⁷⁹ L. Vacavant,⁹⁹ V. Vacek,¹³⁹ B. Vachon,¹⁰¹
K. O. H. Vadla,¹³¹ A. Vaidya,⁹² C. Valderanis,¹¹² E. Valdes Santurio,⁴³a,⁴³b M. Valente,⁵² S. Valentinetti,²³b,²³a A. Valero,¹⁷¹
L. Valéry,⁴⁴ R. A. Vallance,²¹ A. Vallier,⁵ J. A. Valls Ferrer,¹⁷¹ T. R. Van Daalen,¹⁴ H. Van der Graaf,¹¹⁸ P. Van Gemmeren,⁶
J. Van Nieuwkoop,¹⁴⁹ I. Van Vulpen,¹¹⁸ M. Vanadia,⁷¹a,⁷¹b W. Vandelli,³⁵ A. Vaniachine,¹⁶³ P. Vankov,¹¹⁸ R. Vari,⁷⁰a
E. W. Varnes,⁷ C. Varni,⁵³b,⁵³a T. Varol,⁴¹ D. Varouchas,¹²⁹ K. E. Varvell,¹⁵⁴ G. A. Vasquez,¹⁴⁴b J. G. Vasquez,¹⁸⁰
F. Vazeille,³⁷ D. Vazquez Furelos,¹⁴ T. Vazquez Schroeder,¹⁰¹ J. Veatch,⁵¹ V. Vecchio,⁷²a,⁷²b L. M. Veloce,¹⁶⁴
F. Veloso,¹³⁷a,¹³⁷c S. Veneziano,⁷⁰a A. Ventura,⁶⁵a,⁶⁵b M. Venturi,¹⁷³ N. Venturi,³⁵ V. Vercesi,⁶⁸a M. Verducci,⁷²a,⁷²b
C. M. Vergel Infante,⁷⁶ C. Vergis,²⁴ W. Verkerke,¹¹⁸ A. T. Vermeulen,¹¹⁸ J. C. Vermeulen,¹¹⁸ M. C. Vetterli,¹⁴⁹,f
N. Viaux Maira,¹⁴⁴b M. Vicente Barreto Pinto,⁵² I. Vichou,¹⁷⁰.a T. Vickey,¹⁴⁶ O. E. Vickey Boeriu,¹⁴⁶ G. H. A. Viehhauser,¹³²
S. Viel,¹⁸ L. Vigani,¹³² M. Villa,²³b,²³a M. Villaplana Perez,⁶⁶a,⁶⁶b E. Vilucchi,⁴⁹ M. G. Vincter,³³ V. B. Vinogradov,⁷⁷
A. Vishwakarma,⁴⁴ C. Vittori,²³b,²³a I. Vivarelli,¹⁵³ S. Vlachos,¹⁰ M. Vogel,¹⁷⁹ P. Vokac,¹³⁹ G. Volpi,¹⁴
S. E. von Buddenbrock,³²c E. Von Toerne,²⁴ V. Vorobel,¹⁴⁰ K. Vorobev,¹¹⁰ M. Vos,¹⁷¹ J. H. Vossebeld,⁸⁸ N. Vranjes,¹⁶
M. Vranjes Milosavljevic,¹⁶ V. Vrba,¹³⁹ M. Vreeswijk,¹¹⁸ T. Šfiligoj,⁸⁹ R. Vuillermet,³⁵ I. Vukotic,³⁶ T. Ženiš,²⁸a
L. Živković,¹⁶ P. Wagner,²⁴ W. Wagner,¹⁷⁹ J. Wagner-Kuhr,¹¹² H. Wahlberg,⁸⁶ S. Wahrmund,⁴⁶ K. Wakamiya,⁸⁰
V. M. Walbrecht,¹¹³ J. Walder,⁸⁷ R. Walker,¹¹² S. D. Walker,⁹¹ W. Walkowiak,¹⁴⁸ V. Wallangen,⁴³a,⁴³b A. M. Wang,⁵⁷
C. Wang,⁵⁸b,⁵⁸a F. Wang,¹⁷⁸ H. Wang,¹⁸ H. Wang,³ J. Wang,¹⁵⁴ J. Wang,⁵⁹b P. Wang,⁴¹ Q. Wang,¹²⁵ R.-J. Wang,¹³³ R. Wang,⁵⁸a
R. Wang,⁶ S. M. Wang,¹⁵⁵ W. T. Wang,⁵⁸a W. Wang,¹⁵c,xx W. X. Wang,⁵⁸a,xx Y. Wang,⁵⁸a,kk Z. Wang,⁵⁸c C. Wanotayaroj,⁴⁴
A. Warburton,¹⁰¹ C. P. Ward,³¹ D. R. Wardrope,⁹² A. Washbrook,⁴⁸ P. M. Watkins,²¹ A. T. Watson,²¹ M. F. Watson,²¹
G. Watts,¹⁴⁵ S. Watts,⁹⁸ B. M. Waugh,⁹² A. F. Webb,¹¹ S. Webb,⁹⁷ C. Weber,¹⁸⁰ M. S. Weber,²⁰ S. A. Weber,³³
S. M. Weber,⁵⁹a A. R. Weidberg,¹³² B. Weinert,⁶³ J. Weingarten,⁴⁵ M. Weirich,⁹⁷ C. Weiser,⁵⁰ P. S. Wells,³⁵ T. Wenaus,²⁹
T. Wengler,³⁵ S. Wenig,³⁵ N. Wermes,²⁴ M. D. Werner,⁷⁶ P. Werner,³⁵ M. Wessels,⁵⁹a T. D. Weston,²⁰ K. Whalen,¹²⁸
N. L. Whallon,¹⁴⁵ A. M. Wharton,⁷⁸ A. S. White,¹⁰³ A. White,⁸ M. J. White,¹ R. White,¹⁴⁴b D. Whiteson,¹⁶⁸
B. W. Whitmore,⁸⁷ F. J. Wickens,¹⁴¹ W. Wiedenmann,¹⁷⁸ M. Wielers,¹⁴¹ C. Wiglesworth,³⁹ L. A. M. Wiik-Fuchs,⁵⁰
A. Wildauer,¹¹³ F. Wilk,⁹⁸ H. G. Wilkens,³⁵ L. J. Wilkins,⁹¹ H. H. Williams,¹³⁴ S. Williams,³¹ C. Willis,¹⁰⁴ S. Willocq,¹⁰⁰
J. A. Wilson,²¹ I. Wingerter-Seez,⁵ E. Winkels,¹⁵³ F. Winklmeier,¹²⁸ O. J. Winston,¹⁵³ B. T. Winter,²⁴ M. Wittgen,¹⁵⁰
M. Wobisch,⁹³ A. Wolf,⁹⁷ T. M. H. Wolf,¹¹⁸ R. Wolff,⁹⁹ M. W. Wolter,⁸² H. Wolters,¹³⁷a,¹³⁷c V. W. S. Wong,¹⁷²
N. L. Woods,¹⁴³ S. D. Worm,²¹ B. K. Wosiek,⁸² K. W. Woźniak,⁸² K. Wraight,⁵⁵ M. Wu,³⁶ S. L. Wu,¹⁷⁸ X. Wu,⁵² Y. Wu,⁵⁸a
T. R. Wyatt,⁹⁸ B. M. Wynne,⁴⁸ S. Xella,³⁹ Z. Xi,¹⁰³ L. Xia,¹⁷⁵ D. Xu,¹⁵a H. Xu,⁵⁸a,⁵⁸b L. Xu,²⁹ T. Xu,¹⁴² W. Xu,¹⁰³ B. Yabsley,¹⁵⁴
S. Yacoob,³²a K. Yajima,¹³⁰ D. P. Yallup,⁹² D. Yamaguchi,¹⁶² Y. Yamaguchi,¹⁶² A. Yamamoto,⁷⁹ T. Yamanaka,¹⁶⁰
F. Yamane,⁸⁰ M. Yamatani,¹⁶⁰ T. Yamazaki,¹⁶⁰ Y. Yamazaki,⁸⁰ Z. Yan,²⁵ H. J. Yang,⁵⁸c,⁵⁸d H. T. Yang,¹⁸ S. Yang,⁷⁵
Y. Yang,¹⁶⁰ Z. Yang,¹⁷ W.-M. Yao,¹⁸ Y. C. Yap,⁴⁴ Y. Yasu,⁷⁹ E. Yatsenko,⁵⁸c,⁵⁸d J. Ye,⁴¹ S. Ye,²⁹ I. Yeletskikh,⁷⁷ E. Yigitbasi,²⁵
E. Yildirim,⁹⁷ K. Yorita,¹⁷⁶ K. Yoshihara,¹³⁴ C. J. S. Young,³⁵ C. Young,¹⁵⁰ J. Yu,⁸ J. Yu,⁷⁶ X. Yue,⁵⁹a S. P. Y. Yuen,²⁴
B. Zabinski,⁸² G. Zacharis,¹⁰ E. Zaffaroni,⁵² R. Zaidan,¹⁴ A. M. Zaitsev,¹²¹,oo T. Zakareishvili,¹⁵⁶b N. Zakharchuk,³³
J. Zalieckas,¹⁷ S. Zambito,⁵⁷ D. Zanzi,³⁵ D. R. Zaripovas,⁵⁵ S. V. Zeißner,⁴⁵ C. Zeitnitz,¹⁷⁹ G. Zemaityte,¹³² J. C. Zeng,¹⁷⁰
Q. Zeng,¹⁵⁰ O. Zenin,¹²¹ D. Zerwas,¹²⁹ M. Zgubič,¹³² D. F. Zhang,⁵⁸b D. Zhang,¹⁰³ F. Zhang,¹⁷⁸ G. Zhang,⁵⁸a H. Zhang,¹⁵c
J. Zhang,⁶ L. Zhang,¹⁵c L. Zhang,⁵⁸a M. Zhang,¹⁷⁰ P. Zhang,¹⁵c R. Zhang,⁵⁸a R. Zhang,²⁴ X. Zhang,⁵⁸b Y. Zhang,¹⁵d
Z. Zhang,¹²⁹ P. Zhao,⁴⁷ X. Zhao,⁴¹ Y. Zhao,⁵⁸b,¹²⁹,bb Z. Zhao,⁵⁸a A. Zhemchugov,⁷⁷ Z. Zheng,¹⁰³ D. Zhong,¹⁷⁰ B. Zhou,¹⁰³
C. Zhou,¹⁷⁸ L. Zhou,⁴¹ M. S. Zhou,¹⁵d M. Zhou,¹⁵² N. Zhou,⁵⁸c Y. Zhou,⁷ C. G. Zhu,⁵⁸b H. L. Zhu,⁵⁸a H. Zhu,¹⁵a J. Zhu,¹⁰³
Y. Zhu,⁵⁸ᵃ X. Zhuang,¹⁵ᵃ K. Zhukov,¹⁰⁸ V. Zhulanov,¹²⁰b,¹²⁰ᵃ A. Zibell,¹⁷⁴ D. Zieminska,⁶³ N. I. Zimine,⁷⁷ S. Zimmermann,⁵⁰ Z. Zinonos,¹¹³ M. Zinser,⁹⁷ M. Ziolkowski,¹⁴⁸ G. Zobernig,¹⁷⁸ A. Zoccoli,²³b,²³ᵃ K. Zoch,⁵¹ T. G. Zorbas,¹⁴⁶ R. Zou,³⁶ M. Zur Nedden,¹⁹ and L. Zwalinski³⁵

(ATLAS Collaboration)

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

²Physics Department, SUNY Albany, Albany, New York, USA

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

⁴⁵Department of Physics, Ankara University, Ankara, Turkey

⁴ᵇIstanbul Aydin University, Istanbul, Turkey

⁴ᶜDivision of Physics, TOBB University of Economics and Technology, Ankara, Turkey

⁵LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France

⁶High Energy Physics Division, Argonne National Laboratory, Argonne, Illinois, USA

⁷Department of Physics, University of Arizona, Tucson, Arizona, USA

⁸Department of Physics, University of Texas at Arlington, Arlington, Texas, USA

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, Texas, USA

¹²ᵃBahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey

¹²ᵇIstanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey

¹²ᶜDepartment of Physics, Bogazici University, Istanbul, Turkey

¹²ᵈDepartment of Physics Engineering, Gaziantep University, Gaziantep, Turkey

¹³Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan

¹⁴Institut de Física d'Altes Energies (IFAE),
Barcelona Institute of Science and Technology, Barcelona, Spain

¹⁵ᵃInstitute of High Energy Physics, Chinese Academy of Sciences, Beijing, China

¹⁵ᵇPhysics Department, Tsinghua University, Beijing, China

¹⁵ᶜDepartment of Physics, Nanjing University, Nanjing, China

¹⁵ᵈUniversity of Chinese Academy of Science (UCAS), Beijing, China

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

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

¹⁸Physics Division, Lawrence Berkeley National Laboratory and University of California,
Berkeley, California, USA

¹⁹Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany

²⁰Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics,
University of Bern, Bern, Switzerland

²¹School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom

²²Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia

²³ᵃDipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy

²³ᵇINFN Sezione di Bologna, Italy

²⁴Physikalisches Institut, Universität Bonn, Bonn, Germany

²⁵Department of Physics, Boston University, Boston, Massachusetts, USA

²⁶Department of Physics, Brandeis University, Waltham, Massachusetts, USA

²⁷ᵃTransilvania University of Brasov, Brasov, Romania

²⁷ᵇHoria Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania

²⁷ᶜDepartment of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania

²⁷ᵈNational Institute for Research and Development of Isotopic and Molecular Technologies,
Physics Department, Cluj-Napoca, Romania

²⁷ᵉUniversity Politehnica Bucharest, Bucharest, Romania

²⁷ᵉWest University in Timisoara, Timisoara, Romania

²⁸ᵃFaculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic

²⁸ᵇDepartment of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences,
Kosice, Slovak Republic

²⁹Physics Department, Brookhaven National Laboratory, Upton, New York, USA

³⁰Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina

³¹Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom

³²Institute of Physics, University of Cape Town, Cape Town, South Africa
³²ᵇDepartment of Mechanical Engineering Science, University of Johannesburg,
Johannesburg, South Africa

³²ᶜSchool of Physics, University of the Witwatersrand, Johannesburg, South Africa

³³Department of Physics, Carleton University, Ottawa ON, Canada

³⁴ᵃFaculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies—Université
Hassan II, Casablanca, Morocco

³⁴ᵇCentre National de l'Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat, Morocco

³⁴ᶜFaculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Morocco

³⁴ᵈFaculté des Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco

³⁴ᵉFaculté des sciences, Université Mohammed V, Rabat, Morocco

³⁵CERN, Geneva, Switzerland

³⁶Enrico Fermi Institute, University of Chicago, Chicago, Illinois, USA

³⁷LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France

³⁸Nevis Laboratory, Columbia University, Irvington, New York, USA

³⁹Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

⁴⁰ᵃDipartimento di Fisica, Università della Calabria, Rende, Italy

⁴⁰ᵇINFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Italy

⁴¹Physics Department, Southern Methodist University, Dallas, Texas, USA

⁴²Physics Department, University of Texas at Dallas, Richardson, Texas, USA

⁴³ᵃDepartment of Physics, Stockholm University, Sweden

⁴³ᵇOskar Klein Centre, Stockholm, Sweden

⁴⁴Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany

⁴⁵Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany

⁴⁶Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany

⁴⁷Department of Physics, Duke University, Durham, North Carolina, USA

⁴⁸SUPA—School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom

⁴⁹INFN e Laboratori Nazionali di Frascati, Frascati, Italy

⁵⁰Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany

⁵¹II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany

⁵²Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland

⁵³ᵃDipartimento di Fisica, Università di Genova, Genova, Italy

⁵³ᵇINFN Sezione di Genova, Italy

⁵⁴II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany

⁵⁵SUPA—School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom

⁵⁶LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France

⁵⁷Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, Massachusetts, USA

⁵⁸ᵃDepartment of Modern Physics and State Key Laboratory of Particle Detection and Electronics,
University of Science and Technology of China, Hefei, China

⁵⁸ᵇInstitute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics
and Particle Irradiation (MOE), Shandong University, Qingdao, China

⁵⁸ᶜSchool of Physics and Astronomy, Shanghai Jiao Tong University,
KLPPAC-MoE, SKLPPC, Shanghai, China

⁵⁸ᵈTsung-Dao Lee Institute, Shanghai, China

⁵⁹ᵃKirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

⁵⁹ᵇPhysikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

⁶⁰Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan

⁶¹ᵃDepartment of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China

⁶¹ᵇDepartment of Physics, University of Hong Kong, Hong Kong, China

⁶¹ᶜDepartment of Physics and Institute for Advanced Study, Hong Kong University of Science and
Technology, Clear Water Bay, Kowloon, Hong Kong, China

⁶²Department of Physics, National Tsing Hua University, Hsinchu, Taiwan

⁶³Department of Physics, Indiana University, Bloomington, Indiana, USA

⁶⁴ᵃINFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy

⁶⁴ᵇICTP, Trieste, Italy

⁶⁴ᶜDipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy

⁶⁵ᵃINFN Sezione di Lecce, Italy

⁶⁵ᵇDipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy

⁶⁶ᵃINFN Sezione di Milano, Italy

⁶⁶ᵇDipartimento di Fisica, Università di Milano, Milano, Italy

⁶⁷ᵃINFN Sezione di Napoli, Italy
67b Dipartimento di Fisica, Università di Napoli, Napoli, Italy

68a INFN Sezione di Pavia, Italy

68b Dipartimento di Fisica, Università di Pavia, Pavia, Italy

69a INFN Sezione di Pisa, Italy

69b Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy

70a INFN Sezione di Roma, Italy

70b Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy

71a INFN Sezione di Roma Tor Vergata, Italy

71b Dipartimento di Fisica, Università di Roma Tor Vergata, Roma, Italy

72a INFN Sezione di Roma Tre, Italy

72b Dipartimento di Matematica e Fisica, Università Roma Tre, Roma, Italy

73a INFN-TIFPA, Italy

73b Università degli Studi di Trento, Trento, Italy

74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria

75 University of Iowa, Iowa City, Iowa, USA

76 Department of Physics and Astronomy, Iowa State University, Ames, Iowa, USA

77 Joint Institute for Nuclear Research, Dubna, Russia

78a Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF),
Juiz de Fora, Brazil

78b Universidade Federal do Rio de Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil

78c Universidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil

78d Instituto de Física, Universidade de São Paulo, São Paulo, Brazil

79 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan

80 Graduate School of Science, Kobe University, Kobe, Japan

81a AGH University of Science and Technology, Faculty of Physics and Applied Computer Science,
Krakow, Poland

81b Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland

82 Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland

83 Faculty of Science, Kyoto University, Kyoto, Japan

84 Kyoto University of Education, Kyoto, Japan

85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University,
Fukuoka, Japan

86 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina

87 Physics Department, Lancaster University, Lancaster, United Kingdom

88 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom

89 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics,
University of Ljubljana, Ljubljana, Slovenia

90 School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom

91 Department of Physics, Royal Holloway University of London, Egham, United Kingdom

92 Department of Physics and Astronomy, University College London, London, United Kingdom

93 Louisiana Tech University, Ruston, Los Angeles, USA

94 Fysiska institutionen, Lunds universitet, Lund, Sweden

95 Centre de Calcul de l'Institut National de Physique Nucléaire et de Physique des Particules (IN2P3),
Villeurbanne, France

96 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain
97 Institut für Physik, Universität Mainz, Mainz, Germany

98 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom

99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France

100 Department of Physics, University of Massachusetts, Amherst, Massachusetts, USA

101 Department of Physics, McGill University, Montreal QC, Canada

102 School of Physics, University of Melbourne, Victoria, Australia

103 Department of Physics, University of Michigan, Ann Arbor, Michigan, USA

104 Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA

105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus

106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus

107 Group of Particle Physics, University of Montreal, Montreal QC, Canada

108 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia

109 Institute for Theoretical and Experimental Physics (ITEP), Moscow, Russia

110 National Research Nuclear University MEPhI, Moscow, Russia
¹¹¹D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia

¹¹²Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany

¹¹³Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany

¹¹⁴Nagasaki Institute of Applied Science, Nagasaki, Japan

¹¹⁵Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan

¹¹⁶Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, USA

¹¹⁷Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands

¹¹⁸Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands

¹¹⁹Department of Physics, Northern Illinois University, DeKalb, Illinois, USA

¹²⁰aBudker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk, Russia

¹²⁰bNovosibirsk State University Novosibirsk, Russia

¹²¹Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia

¹²²Department of Physics, New York University, New York, New York, USA

¹²³The Ohio State University, Columbus, Ohio, USA

¹²⁴Faculty of Science, Okayama University, Okayama, Japan

¹²⁵Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, Oklahoma, USA

¹²⁶Department of Physics, Oklahoma State University, Stillwater, Oklahoma, USA

¹²⁷Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic

¹²⁸Center for High Energy Physics, University of Oregon, Eugene, Oregon, USA

¹²⁹LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France

¹³⁰Graduate School of Science, Osaka University, Osaka, Japan

¹³¹Department of Physics, University of Oslo, Oslo, Norway

¹³²Department of Physics, Oxford University, Oxford, United Kingdom

¹³³LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France

¹³⁴Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

¹³⁵Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia

¹³⁶Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

¹³⁷aLaboratório de Instrumentação e Física Experimental de Partículas—LIP, Portugal

¹³⁷bDepartamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

¹³⁷cDepartamento de Física, Universidade de Coimbra, Coimbra, Portugal

¹³⁷dCentro de Física Nuclear da Universidade de Lisboa, Lisboa, Portugal

¹³⁷eDepartamento de Física, Universidade do Minho, Braga, Portugal

¹³⁷fDepartamento de Física Teórica y del Cosmo, Universidad de Granada, Granada (Spain), Spain

¹³⁷gDepart Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

¹³⁸Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic

¹³⁹Czech Technical University in Prague, Prague, Czech Republic

¹⁴⁰Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic

¹⁴¹Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom

¹⁴²IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France

¹⁴³Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, California, USA

¹⁴⁴aDepartamento de Física, Pontifícia Universidade Católica de Chile, Santiago, Chile

¹⁴⁴bDepartamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile

¹⁴⁵Department of Physics, University of Washington, Seattle, Washington, USA

¹⁴⁶Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom

¹⁴⁷Department of Physics, Shinshu University, Nagano, Japan

¹⁴⁸Department Physik, Universität Siegen, Siegen, Germany

¹⁴⁹Department of Physics, Simon Fraser University, Burnaby BC, Canada

¹⁵⁰SLAC National Accelerator Laboratory, Stanford, California, USA

¹⁵¹Physics Department, Royal Institute of Technology, Stockholm, Sweden

¹⁵²Departments of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA

¹⁵³Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom

¹⁵⁴School of Physics, University of Sydney, Sydney, Australia

¹⁵⁵Institute of Physics, Academia Sinica, Taipei, Taiwan

¹⁵⁶aE. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia
<sup>156b</sup>High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia

<sup>157</sup>Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel

<sup>158</sup>Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel

<sup>159</sup>Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

<sup>160</sup>International Center for Elementary Particle Physics and Department of Physics,
University of Tokyo, Tokyo, Japan

<sup>161</sup>Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan

<sup>162</sup>Department of Physics, Tokyo Institute of Technology, Tokyo, Japan

<sup>163</sup>Tomsk State University, Tomsk, Russia

<sup>164</sup>Department of Physics, University of Toronto, Toronto ON, Canada

<sup>165a</sup>TRIUMF, Vancouver BC, Canada

<sup>165b</sup>Department of Physics and Astronomy, York University, Toronto ON, Canada

<sup>166</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>167</sup>Department of Physics and Astronomy, Tufts University, Medford, Massachusetts, USA

<sup>168</sup>Department of Physics and Astronomy, University of California Irvine, Irvine, California, USA

<sup>169</sup>Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden

<sup>170</sup>Department of Physics, University of Illinois, Urbana, Illinois, USA

<sup>171</sup>Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia—CSIC, Valencia, Spain

<sup>172</sup>Department of Physics, University of British Columbia, Vancouver BC, Canada

<sup>173</sup>Department of Physics and Astronomy, University of Victoria, Victoria BC, Canada

<sup>174</sup>Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany

<sup>175</sup>Department of Physics, University of Warwick, Coventry, United Kingdom

<sup>176</sup>Waseda University, Tokyo, Japan

<sup>177</sup>Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel

<sup>178</sup>Department of Physics, University of Wisconsin, Madison, Wisconsin, USA

<sup>179</sup>Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik,
Bergische Universität Wuppertal, Wuppertal, Germany

<sup>180</sup>Department of Physics, Yale University, New Haven, Connecticut, USA

<sup>181</sup>Yerevan Physics Institute, Yerevan, Armenia

<sup>a</sup>Deceased.

<sup>b</sup>Also at Department of Physics, King's College London, London, United Kingdom.

<sup>c</sup>Also at Istanbul University, Dept. of Physics, Istanbul, Turkey.

<sup>d</sup>Also at Instituto de Física Teórica de la Universidad Autónoma de Madrid, Spain.

<sup>e</sup>Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan.

<sup>f</sup>Also at TRIUMF, Vancouver BC, Canada.

<sup>g</sup>Also at Department of Physics and Astronomy, University of Louisville, Louisville, Kentucky, USA.

<sup>h</sup>Also at Department of Physics, California State University, Fresno, California, USA.

<sup>i</sup>Also at Department of Physics, University of Fribourg, Fribourg, Switzerland.

<sup>j</sup>Also at Departament de Física de la Universitat Autonoma de Barcelona, Barcelona, Spain.

<sup>k</sup>Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia.

<sup>l</sup>Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China.

<sup>m</sup>Also at Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.

<sup>n</sup>Also at Universita di Napoli Parthenope, Napoli, Italy.

<sup>o</sup>Also at Institute of Particle Physics (IPP), Canada.

<sup>p</sup>Also at II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany.

<sup>q</sup>Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy.

<sup>r</sup>Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania.

<sup>s</sup>Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France.

<sup>t</sup>Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia.

<sup>u</sup>Also at Borough of Manhattan Community College, City University of New York, New York, USA.

<sup>v</sup>Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece.

<sup>w</sup>Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa.

<sup>x</sup>Also at Louisiana Tech University, Ruston, Louisiana, USA.

<sup>y</sup>Also at California State University, East Bay, USA.

<sup>z</sup>Also at Institutucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain.

<sup>aa</sup>Also at Department of Physics, University of Michigan, Ann Arbor, Michigan, USA.

<sup>bb</sup>Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France.

<sup>cc</sup>Also at Graduate School of Science, Osaka University, Osaka, Japan.
dd Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.

ee Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands.

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

gg Also at CERN, Geneva, Switzerland.

hh Also at Department of Physics, Stanford University, USA.

ii Also at Manhattan College, New York, New York, USA.

jj Also at Hellenic Open University, Patras, Greece.

kk Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France.

ll Also at The City College of New York, New York, New York, USA.

mm Also at Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain.

nn Also at Department of Physics, California State University, Sacramento, California, USA.

oo Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia.

pp Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland.

qq Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom.

rr Also at School of Physics, Sun Yat-sen University, Guangzhou, China.

ss Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates.

tt Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany.

uu Also at National Research Nuclear University MEPhI, Moscow, Russia.

vv Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary.

ww Also at Giresun University, Faculty of Engineering, Giresun, Turkey.

xx Also at Institute of Physics, Academia Sinica, Taipei, Taiwan.