Long- and short-range correlations and their event-scale dependence in high-multiplicity pp collisions at √s = 13 TeV

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Longand short-range correlations and their event-scale dependence in highmultiplicity pp collisions at √s = 13 TeV © 2021 CERN Published version ALICE collaboration ALICE collaboration. (2021). Longand short-range correlations and their event-scale dependence in high-multiplicity pp collisions at √s = 13 TeV. Journal of High Energy Physics, 2021(5), Article 290. https://doi.org/10.1007/JHEP05(2021)290 2021 JHEP05(2021)290 Published for SISSA by Springer Received:January 30, 2021 Revised:April 6, 2021 Accepted:May 12, 2021 Published:May 31, 2021 Longand short-range correlations and their event-scale dependence in high-multiplicity pp collisions at √s= 13 TeV The ALICE collaboration E-mail: [email protected] Abstract: Two-particle angular correlations are measured in high-multiplicity protonproton collisions at √s= 13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(∆η∼0) and long-range (1.6<|∆η|<1.8) in pseudorapidity are extracted on the near-side (∆ϕ∼0). They are reported as a function of transverse momentum (pT) in the range 1 < pT<4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-pTleading particles or jets for varying pT thresholds. The results demonstrate that the long-range “ridge” yield, possibly related to the collective behavior of the system, is present in events with high-pTprocesses as well. The magnitudes of the shortand long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement for the pTdependency, while overestimating the event-scale dependency. Keywords: Heavy Ion Experiments ArXiv ePrint: 2101.03110 Open Access, Copyright CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP05(2021)290 JHEP05(2021)290 Contents 1 Introduction 1 2 Experimental setup 3 3 Analysis procedure 4 4 Systematic uncertainties of the measured yields 5 5 Results 7 5.1 Ridge yield 7 5.2 Event-scale dependence of the ridge yield 10 6 Conclusions 11 The ALICE collaboration 20 1 Introduction In high-energy nucleus-nucleus collisions at RHIC [1–4] and LHC [5–7], significant correlations are observed between particles emitted over a wide pseudorapidity range. The origin of these observations are collective effects, which are related to the formation of a strongly interacting quark-gluon plasma (QGP), which exhibits hydrodynamic behavior (see the reviews [8–10]). Recent theoretical [11–13] and experimental [14–17] advancements have contributed significantly to the understanding of the transport properties of the QGP. Similar long-range correlations are also observed in high-multiplicity proton-proton (pp) [18–21], proton-nucleus (pA) [22–25], and light nucleus-nucleus collisions [26,27]. The fact that these correlations extend over a large range in pseudorapidity implies that they originate from early times in these collisions and thus suggest that hydrodynamic behavior is present even in these small systems, although the volume and lifetime of the medium produced in such a collision system are expected to be small, and there are other mechanisms which can produce similar flow-like signals [28,29]. Measurements of two-particle angular correlations provide information on many physical effects, including collectivity, hadronization, fragmentation, and femtoscopic effects [30], and are typically quantified as a function of ∆η, the relative pseudorapidity, and ∆ϕ, the separation in azimuthal angle, of particle pairs. The long-range structure of two-particle angular correlations is well suited to analyze collective effects, since it is not created by resonance decays nor fragmentation of high-momentum partons. A typical source of long-range correlations in Monte Carlo pp generators is the momentum conservation. The enhancement in the yield of two-particle correlations at small ∆ϕthat extends over a large ∆ηis – 1 – JHEP05(2021)290 dubbed “ridge” due to its characteristic shape in the ∆η–∆ϕplane. The shape of these ∆ϕcorrelations can be studied via a Fourier decomposition [31,32]. The second and third order terms are the dominant harmonic coefficients. In heavy-ion collisions, harmonic coefficients can be related to the collision geometry and density fluctuations of the colliding nuclei [33–35] and to transport properties of the QGP in relativistic viscous hydrodynamic models [11–13,36,37]. The ridge structures in high-multiplicity pp and p-Pb events have been attributed to initial-state or final-state effects. Initial-state effects, usually attributed to gluon saturation [38,39], can form long-range correlations along the longitudinal direction. The final-state effects might be parton-induced interactions [40] or collective phenomena due to hydrodynamic behavior of the produced matter arising in a high-density system possibly formed in these collisions [41,42]. Hybrid models implementing both effects are generally used in hydrodynamic simulations [43,44]. EPOS LHC describes collectivity in small systems with a parameterized hydrodynamic evolution of the high-energy density region, so called “core”, formed by many color string fields [45]. The proton shape and its fluctuations are also important to model small systems [44]. To understand the influence of initialor final-state effects, and to possibly disentangle the two, a quantitative description of the measurements in small systems [46,47] needs to account for details of the initial state. Systematic studies of these correlation effects from small to large systems are being performed, both experimentally [21] and theoretically [47]. However, the quantitative description of the full set of experimental data has not been achieved yet. A summary of various explanations for the observed correlations in small systems is given in [29,48,49]. Besides the hybrid models mentioned above, alternative approaches were developed to describe collectivity in small systems. A microscopic model for collectivity was implemented in the PYTHIA 8 event generator, which is based on interacting strings (string shoving) and is called the “string shoving model” [50]. In this model, strings repel each other in the transverse direction, which results in microscopic transverse pressure and, consequently, in long-range correlations. PYTHIA 8 with string shoving can qualitatively reproduce the near-side (∆ϕ∼0) ridge yield measured by the CMS Collaboration [20]. This challenges the hydrodynamic picture and predicts modifications of the jet fragmentation properties [51]. It is expected that final-state interactions affect also produced jets if they are the source of collectivity in small systems. Proving the presence of jet quenching [52,53] would be another crucial evidence of the existence of a high-density strongly-interacting system, possibly a QGP, in high-multiplicity pp collisions. However, there is no evidence observed so far for the jet quenching effect in high-multiplicity pp and p-Pb collisions [54– 57]. Jet fragmentation can be studied in two-particle angular correlations in short-range correlations around (∆η,∆ϕ) = (0,0) [58]. To further investigate the interplay of jet production and collective effects in small systems, longand short-range correlations are studied simultaneously in high-multiplicity pp collisions at √s= 13 TeV using the ALICE LHC Run 2 data collected with the highmultiplicity event trigger in 2016–2018. In this article, the near-side per-trigger yield at large pseudorapidity separation is presented as a function of transverse momentum. – 2 – JHEP05(2021)290 The results are compared with previous measurements by the CMS Collaboration [19]. In addition, the ridge yield and near-side jet-like correlations with the event-scale selection are reported. The event-scale selection is done by requiring a minimum transverse momentum of the leading particle or the reconstructed jet at midrapidity, which is expected to bias the impact parameter of pp collisions to be smaller on average [59,60]. At the same time, the transverse momentum of the leading particle or the reconstructed jet is a measure of the momentum transfer in the hard parton scattering [61,62]. The event-scale dependence of the second-flow harmonic v2has previously been studied in pp collisions with and without a tagged Zboson, where little or no dependence was observed [63]. The experimental setup and analysis method are described in section 2and 3, respectively. The sources of systematic uncertainties are discussed in section 4. The results and comparisons with model calculations of the measurements are presented in section 5. Finally, results are summarized in section 6. 2 Experimental setup The analysis is carried out with data samples of pp collisions at √s= 13 TeV collected from 2016 to 2018 during the LHC Run 2 period. The full description of the ALICE detector and its performance in the LHC Run 2 can be found in [64,65]. The present analysis utilizes the V0 [66], the Inner Tracking System (ITS) [67], and the Time Projection Chamber (TPC) [68] detectors. The V0 detector consists of two stations placed on both sides of the interaction point, V0A and V0C, each made of 32 plastic scintillator tiles, covering the full azimuthal angle within the pseudorapidity intervals 2.8< η < 5.1and −3.7< η < −1.7, respectively. The V0 is used to provide a minimum bias (MB) and a high-multiplicity (HM) trigger. The minimum bias trigger is obtained by a time coincidence of V0A and V0C signals. The charged particle multiplicity selection is done on the sum of the V0A and V0C signals, which is denoted as V0M. The high-multiplicity trigger requires that the V0M signal exceeds 5 times the mean value measured in minimum bias collisions, selecting the 0.1% of MB events that have the largest V0 multiplicity. The analyzed data samples of minimum bias and high-multiplicity pp events at √s=13TeV correspond to integrated luminosities of 19 nb−1and 11 pb−1, respectively [69]. The primary vertex position is reconstructed from the measured signals in the Silicon Pixel Detector (SPD), which forms the innermost two layers of the ITS. Reconstructed primary vertices of selected events are required to be located within 8 cm from the center of the detector along the beam direction. The probability of pileup events is about 0.6% in MB events. Pileup events can be resolved and are rejected if the longitudinal displacement of their primary vertices is larger than 0.8 cm. Charged-particle tracks are reconstructed by the ITS and TPC, which are operated in a uniform solenoidal magnetic field of 0.5 T along the beam direction. The ITS is a silicon tracker with six layers of silicon sensors where the SPD [70] comprises the two innermost layers, the next two layers called the Silicon Drift Detector (SDD), and the outermost layers named the Silicon Strip Detector (SSD). The ITS and TPC, covering the – 3 – JHEP05(2021)290 full azimuthal range, have acceptances up to |η|<1.4and 0.9, respectively, for detection of charged particles emitted within 8 cm from the primary vertex position (zvtx) along the beam direction. The tracking of charged particles is done with the combined information of the ITS and TPC that enables the reconstruction of tracks down to 0.15 GeV/c, where the efficiency is about 65%. The efficiency reaches 80% for intermediate pT, 1 to 5 GeV/c. The pTresolution is around 1% for primary tracks with pT<1GeV/c, and linearly increases up to 6% at pT∼40 GeV/c[71]. The charged particle selection criteria are optimized to make the efficiency uniform over the full TPC volume to mitigate the effect of small regions where some of the ITS layers are inactive. The selection consists of two track classes. Those belonging to the first class are required to have at least one hit in the SPD. Tracks from the second class do not have any SPD associated hit and their initial point is instead constrained to the primary vertex [72]. 3 Analysis procedure The two-particle correlation function is measured as a function of the relative pseudorapidity (∆η) and the azimuthal angle difference (∆ϕ) between the trigger and the associated particles, 1 Ntrig d2Npair d∆ηd∆ϕ=B(0,0) S(∆η, ∆ϕ) B(∆η, ∆ϕ)pT,trig, pT,assoc ,(3.1) where pT,trig and pT,assoc (pT,trig > pT,assoc) are the transverse momenta of the trigger and associated particles, respectively, Ntrig is the number of trigger particles, and Npair is the number of trigger-associated particle pairs. The average number of pairs in the same event and in mixed events are denoted as S(∆η, ∆ϕ)and B(∆η, ∆ϕ), respectively. Normalization of B(∆η, ∆ϕ)is done with its value at ∆ηand ∆ϕ= 0, represented as B(0,0). Acceptance effects are corrected by dividing S(∆η, ∆ϕ)with B(∆η, ∆ϕ)/B(0,0). The right-hand side of eq. (1) is corrected for the track reconstruction efficiency, which is mainly relevant for the associated particles, as a function of pTand pseudorapidity. Primary vertices of events to be mixed are required to be within the same, 2 cm wide, zvtx interval [58,73] for each multiplicity class. The final per-trigger yield is constructed by averaging correlation functions over these primary vertex bins. Ridge yields at large ∆ηare extracted for various multiplicity classes and pTintervals. The large ∆ηrange is selected as 1.6<|∆η|<1.8, which is the range where the tracking quality — efficiency and precision — is the best. The ridge yield is only reported for pT>1GeV/c. Below 1GeV/c, the jet-like contribution to the correlation function extends into the region where the ridge yield is measured, 1.6 <|∆η|<1.8. In this region, the ∆ϕ distribution, or the so-called per-trigger yield, is expressed as 1 Ntrig dNpair d∆ϕ=Z1.6<|∆η|<1.8 1 Ntrig d2Npair d∆ηd∆ϕ!1 δ∆η d∆η−CZYAM ,(3.2) where δ∆η=0.4 is the normalization factor to get the per-trigger yield per unit of pseudorapidity. – 4 – JHEP05(2021)290 The baseline of the correlation is subtracted by means of the Zero-Yield-At-Minimum (ZYAM) procedure [74]. The minimum yield (CZYAM)at ∆ϕ= ∆ϕmin in the ∆ϕprojection (note that the value of ∆ϕmin can be different in data and in models) is obtained from a fit function, which fits the data with a Fourier series up to the third harmonic. By construction, the yield at ∆ϕmin is zero after subtracting CZYAM from the ∆ϕprojection. The ridge yield (Yridge) is obtained by integrating the near-side peak of the ∆ϕprojection over |∆ϕ|<|∆ϕmin|after the ZYAM procedure, Yridge =Z|∆ϕ|<|∆ϕmin| 1 Ntrig dNpair d∆ϕd∆ϕ. (3.3) The ridge yield is further studied in events having a hard jet or a high-pTleading particle in the midrapidity region. This event scale is set by requiring a minimum pTof the leading track (pT,LP) or the reconstructed jet (pch T,jet) at midrapidity. The leading track is selected within |η|<0.9and the full azimuthal angle. Jets are reconstructed with charged particles only (track-based jets) with the anti-kTalgorithm [75,76] and the resolution parameter R=0.4. The recombination scheme used in this article is the pT scheme. Jets are selected in |ηjet|<0.4and in the full azimuthal angle. The pTof jets pch T,jet is corrected for the underlying event density that is measured using the kTalgorithm with R=0.2 [77]. To quantify the variation of the near-side jet-like peak with event-scale selections with a minimum pT,LP or pch T,jet, the near-side jet-like peak yield is extracted from the near-side ∆ηcorrelations. The near-side is defined as |∆ϕ|<1.28, where the correlation function is projected on the ∆ηaxis. The projection range, 1.28, is chosen to fully cover ∆ϕmin. The near-side ∆ηcorrelations are then constructed as 1 Ntrig dNpair d∆η=Z|∆ϕ|<1.28 1 Ntrig d2Npair d∆ηd∆ϕ!1 δ∆ϕ d∆ϕ−DZYAM ,(3.4) where δ∆ϕ=2.56 is the normalization factor to get per-trigger yield per unit of azimuthal angle. The minimum yield (DZYAM)of the ∆ηcorrelations is found within |∆η|<1.6 and used for the subtraction from the ∆ηcorrelations, which results in zero-yield at the minimum. The near-side jet-like peak yield (Ynear) is measured by integrating the ∆η correlations over |∆η|<1.6, Ynear =Z|∆η|<1.6 1 Ntrig dNpair d∆η!d∆η(3.5) 4 Systematic uncertainties of the measured yields The systematic uncertainties of Yridge and Ynear are estimated by varying the analysis selection criteria and corrections and are summarized in table 1. The systematic uncertainties are independent of the event-scale selection except for DZYAM (see below), as expected, since the multiplicity is weakly dependent on the event scale and the ALICE detector is optimized for much higher multiplicities (Pb-Pb collisions), this is in agreement with our expectations. – 5 – JHEP05(2021)290 Sources Systematic uncertainty (%) Yridge Ynear Pileup rejection ±0.8–3.9 ±0.2–2.2 Primary vertex ±0.5–2.4 ±1.1 Tracking ±2.0–4.0 ±1.5–3.4 ZYAM ±2.1–5.1 ±2.2–4.8 Event mixing ±1.0–4.4 ±0.5–1.7 Efficiency correction ±2.5 ±3.1 Jet contamination −18.8–25.9 (pT<2 GeV/c) N.A. Total (in quadrature) +4.9–9.4 −19.4–21.0±3.9–7.3 Table 1. The relative systematic uncertainty of Yridge and Ynear. Numbers given in ranges correspond to minimum and maximum uncertainties. The uncertainty associated to the pileup rejection is estimated by measuring the changes of results with different rejection criteria from the default one. It is mainly estimated by varying the minimal number of track contributors required for reconstruction of pileup event vertices from 3 to 5. The estimated uncertainty of Yridge is 0.8–3.9%. The corresponding uncertainty of Ynear is estimated to be 0.2–2.2%. Another source of systematic uncertainty is related to the selected range of the primary vertex. The accepted range is changed from |zvtx|<8 cm to |zvtx|<6 cm. The narrower primary vertex selection allows one to test acceptance effects on the measurement. The estimated uncertainty of Yridge is 0.5–2.4%. The uncertainty for Ynear is estimated to be 1.1%. An additional source of systematic uncertainty is related to the track selection criteria. The corresponding uncertainty is estimated by employing other track selection criteria, denoted global tracks, which are optimized for particle identification. The selection criteria of the global tracks are almost identical to the hybrid tracks. Each global track is required to have at least one SPD hit. Due to inefficient parts of the SPD, the azimuthal distribution of global tracks is not uniform. The uncertainties associated with the track selection are estimated to be 2.0–4.0% and 1.5–3.4% for Yridge and Ynear, respectively. The systematic uncertainty of Yridge resulting from the ZYAM procedure is estimated by varying the range of the fit, which is used to find the minimum, from |∆ϕ|< π/2 down to |∆ϕ|<1.2. The estimated uncertainty of Yridge is 2.1–5.1%. The corresponding uncertainty on Ynear is estimated by varying the range from |∆η|<1.6 to |∆η|<1.5 and 1.7. The estimated uncertainty of Ynear is 2.2% for the unbiased case and increases to 4.8% for the largest event-scale selections. This is the only systematic uncertainty for which a significant dependence on the event scale is observed, reflecting a non-negligible dependence of the near-side magnitude and shape on the event-scale selection. The source of systematic uncertainty is associated to the choice of the width of zvtx bins that are used in the event mixing method. The default value of 2 cm is changed to 1 cm. The resulting uncertainty of Yridge is 1.0–4.4%. The uncertainty for Ynear is about 0.5– – 6 – JHEP05(2021)290 1− 0 1 η ∆ 1− 0 1 2 3 4 (rad.) ϕ ∆ 0.15 0.155 0.16 0.165 0.17 0.175 )ϕ∆ dη∆/d pair N 2 ) (d trig N(1/ = 13 TeVsALICE, pp 100% V0M−0 c < 2 GeV/ T,trig(assoc) p1 < 1− 0 1 η ∆ 1− 0 1 2 3 4 (rad.) ϕ ∆ 0.455 0.46 0.465 0.47 0.475 0.48 0.485 0.49 0.495 )ϕ∆ dη∆/d pair N 2 ) (d trig N(1/ = 13 TeVsALICE, pp 0.1% V0M−0 c < 2 GeV/ T,trig(assoc) p1 < Figure 1. Two-particle correlation functions as functions of ∆ηand ∆ϕin minimum-bias events (0–100%, left) and high-multiplicity (0–0.1%, right). Note that the near-side jet peaks exceed the chosen range of the z-axis. The intervals of pT,trig and pT,assoc are 1 <pT<2 GeV/cin both cases. 1.7%. The uncertainty from the efficiency correction for charged particles is estimated by comparing correlation functions of true particles with correlation functions of reconstructed tracks with the efficiency correction in simulation. The estimated uncertainties are 2.5% and 3.1% for Yridge and Ynear, respectively. In the limited η-acceptance of ALICE, the ridge structure is not flat in ∆ηsuggesting that jet-like correlations (non-flow) could contribute, implying that they would impact the ridge-yield extraction. We stress that the models used for comparisons also contain such a non-flow effect, but differences in jet-like correlations between data and MC models could influence the interpretation. To account for the related uncertainty, the variation of the yield with ∆ηbetween 1.5 and 1.8, which should be an upper limit of the residual jet-like contamination, is used as a systematic uncertainty of the ridge yield. The estimated upper limit of the uncertainty is −25.9% for the 1.0 < pT<1.5 GeV/crange, −18.8% for the 1.5 < pT<2.0 GeV/crange, −18.9% for the 1.0 < pT<2.0 GeV/crange, and negligible for pT>2.0GeV/c. This uncertainty is considered only for the measured ridge yields. 5 Results 5.1 Ridge yield Figure 1shows the per-trigger yield obtained from eq. (1) for 1 < pT,trig (pT,assoc)<2 GeV/c in pp collisions at √s=13 TeV for minimum bias events (left) and high-multiplicity events (right). It is worth noting that the z-axes for the yield of the correlations is properly scaled in order to zoom in the ridge yield, as a result, the jet peaks are sheared off in both figures. The ridge structure is clearly observed in the high-multiplicity class while it is less significant in the minimum bias events. The away-side yield is populated mostly by back-to-back jet correlations. Figure 2shows ∆ϕprojections of the two-particle correlation functions obtained in the range 1.6 <|∆η|<1.8 for several track pTintervals after the ZYAM subtraction (see eq. (2)). The results are shown for various pTintervals in the minimum bias class (upper) – 7 – JHEP05(2021)290 cas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research | Natural Sciences, the VILLUM FONDEN and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE), Department of Science and Technology, Government of India (DST), University Grants Commission, Government of India (UGC) and Council of Scientific and Industrial Research (CSIR), India; Indonesian Institute of Science, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology, Nagasaki Institute of Applied Science (IIST), Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) and Dirección General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Católica del Perú, Peru; Ministry of Science and Higher Education, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics and Ministry of Research and Innovation and Institute of Atomic Physics, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science of the Russian Federation, National Research Centre Kurchatov Institute, Russian Science Foundation and Russian Foundation for Basic Research, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSDTA) and Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF) and United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America. 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Manso136, V. Manzari54, Y. Mao7, J. Mareš67, G.V. Margagliotti24, A. Margotti55, A. Marín109, C. Markert121, M. Marquard69, N.A. Martin106, P. Martinengo35, J.L. Martinez127, M.I. Martínez46, G. Martínez García117, S. Masciocchi109, M. Masera25, A. Masoni56, – 21 – JHEP05(2021)290 L. Massacrier79, A. Mastroserio140,54, A.M. Mathis107, O. Matonoha82, P.F.T. Matuoka123, A. Matyja120, C. Mayer120, A.L. Mazuecos35, F. Mazzaschi25, M. Mazzilli35,54, M.A. Mazzoni59, A.F. Mechler69, F. Meddi22, Y. Melikyan64, A. Menchaca-Rocha72, C. Mengke28,7, E. Meninno116,30, A.S. Menon127, M. Meres13, S. Mhlanga126, Y. Miake135, L. Micheletti25, L.C. Migliorin137, D.L. Mihaylov107, K. Mikhaylov76,94, A.N. Mishra146,70, D. Miśkowiec109, A. Modak4, N. Mohammadi35, A.P. Mohanty63, B. Mohanty88, M. Mohisin Khan16, Z. Moravcova91, C. Mordasini107, D.A. Moreira De Godoy145, L.A.P. Moreno46, I. Morozov64, A. Morsch35, T. Mrnjavac35, V. Muccifora53, E. Mudnic36, D. Mühlheim145, S. Muhuri142, J.D. Mulligan81, A. Mulliri23, M.G. Munhoz123, R.H. Munzer69, H. Murakami134, S. Murray126, L. Musa35, J. Musinsky65, C.J. Myers127, J.W. Myrcha143, B. Naik50, R. Nair87, B.K. Nandi50, R. Nania55, E. Nappi54, M.U. Naru14, A.F. Nassirpour82, C. Nattrass132, S. Nazarenko111, A. Neagu20, L. Nellen70, S.V. Nesbo37, G. Neskovic40, D. Nesterov115, B.S. Nielsen91, S. Nikolaev90, S. Nikulin90, V. Nikulin100, F. Noferini55, S. Noh12, P. Nomokonov76, J. Norman129, N. Novitzky135, P. Nowakowski143, A. Nyanin90, J. Nystrand21, M. Ogino84, A. Ohlson82, J. Oleniacz143, A.C. Oliveira Da Silva132, M.H. Oliver147, A. Onnerstad128, C. Oppedisano60, A. Ortiz Velasquez70, T. Osako47, A. Oskarsson82, J. Otwinowski120, K. Oyama84, Y. Pachmayer106, S. Padhan50, D. Pagano141, G. Paić70, A. Palasciano54, J. Pan144, S. Panebianco139, P. Pareek142, J. Park62, J.E. Parkkila128, S. Parmar102, S.P. Pathak127, B. Paul23, J. Pazzini141, H. Pei7, T. Peitzmann63, X. Peng7, L.G. Pereira71, H. Pereira Da Costa139, D. Peresunko90, G.M. Perez8, S. Perrin139, Y. Pestov5, V. Petráček38, M. Petrovici49, R.P. Pezzi71, S. Piano61, M. Pikna13, P. Pillot117, O. Pinazza55,35, L. Pinsky127, C. Pinto27, S. Pisano53, M. Płoskoń81, M. Planinic101, F. Pliquett69, M.G. Poghosyan98, B. Polichtchouk93, N. Poljak101, A. Pop49, S. Porteboeuf-Houssais136, J. Porter81, V. Pozdniakov76, S.K. Prasad4, R. Preghenella55, F. Prino60, C.A. Pruneau144, I. Pshenichnov64, M. Puccio35, S. Qiu92, L. Quaglia25, R.E. Quishpe127, S. Ragoni113, A. Rakotozafindrabe139, L. Ramello32, F. Rami138, S.A.R. Ramirez46, A.G.T. Ramos34, R. Raniwala104, S. Raniwala104, S.S. Räsänen45, R. Rath51, I. Ravasenga92, K.F. Read98,132, A.R. Redelbach40, K. RedlichV,87, A. Rehman21, P. Reichelt69, F. Reidt35, R. Renfordt69, Z. Rescakova39, K. Reygers106, A. Riabov100, V. Riabov100, T. Richert82,91, M. Richter20, P. Riedler35, W. Riegler35, F. Riggi27, C. Ristea68, S.P. Rode51, M. Rodríguez Cahuantzi46, K. Røed20, R. Rogalev93, E. Rogochaya76, T.S. Rogoschinski69, D. Rohr35, D. Röhrich21, P.F. Rojas46, P.S. Rokita143, F. Ronchetti53, A. Rosano33,57, E.D. Rosas70, A. Rossi58, A. Rotondi29, A. Roy51, P. Roy112, N. Rubini26, O.V. Rueda82, R. Rui24, B. Rumyantsev76, A. Rustamov89, E. Ryabinkin90, Y. Ryabov100, A. Rybicki120, H. Rytkonen128, W. Rzesa143, O.A.M. Saarimaki45, R. Sadek117, S. Sadovsky93, J. Saetre21, K. Šafařík38, S.K. Saha142, S. Saha88, B. Sahoo50, P. Sahoo50, R. Sahoo51, S. Sahoo66, D. Sahu51, P.K. Sahu66, J. Saini142, S. Sakai135, S. Sambyal103, V. SamsonovI,100,95, D. Sarkar144, N. Sarkar142, P. Sarma43, V.M. Sarti107, M.H.P. Sas147,63, J. Schambach98,121, H.S. Scheid69, C. Schiaua49, R. Schicker106, A. Schmah106, C. Schmidt109, H.R. Schmidt105, M.O. Schmidt106, M. Schmidt105, N.V. Schmidt98,69, A.R. Schmier132, R. Schotter138, J. Schukraft35, Y. Schutz138, K. Schwarz109, K. Schweda109, G. Scioli26, E. Scomparin60, J.E. Seger15, Y. Sekiguchi134, D. Sekihata134, I. Selyuzhenkov109,95, S. Senyukov138, J.J. Seo62, D. Serebryakov64, L. Šerkšnyt˙e107, A. Sevcenco68, A. Shabanov64, A. Shabetai117, R. Shahoyan35, W. Shaikh112, A. Shangaraev93, A. Sharma102, H. Sharma120, M. Sharma103, N. Sharma102, S. Sharma103, O. Sheibani127, A.I. Sheikh142, K. Shigaki47, M. Shimomura85, S. Shirinkin94, Q. Shou41, Y. Sibiriak90, S. Siddhanta56, T. Siemiarczuk87, T.F.D. Silva123, D. Silvermyr82, G. Simatovic92, G. Simonetti35, B. Singh107, R. Singh88, R. Singh103, R. Singh51, V.K. Singh142, V. Singhal142, T. Sinha112, B. Sitar13, M. Sitta32, T.B. Skaali20, G. Skorodumovs106, M. Slupecki45, N. Smirnov147, R.J.M. Snellings63, C. Soncco114, J. Song127, A. Songmoolnak118, F. Soramel28, – 22 – JHEP05(2021)290 S. Sorensen132, I. Sputowska120, J. Stachel106, I. Stan68, P.J. Steffanic132, S.F. Stiefelmaier106, D. Stocco117, M.M. Storetvedt37, C.P. Stylianidis92, A.A.P. Suaide123, T. Sugitate47, C. Suire79, M. Suljic35, R. Sultanov94, M. Šumbera97, V. Sumberia103, S. Sumowidagdo52, S. Swain66, A. Szabo13, I. Szarka13, U. Tabassam14, S.F. Taghavi107, G. Taillepied136, J. Takahashi124, G.J. Tambave21, S. Tang136,7, Z. Tang130, M. Tarhini117, M.G. Tarzila49, A. Tauro35, G. Tejeda Muñoz46, A. Telesca35, L. Terlizzi25, C. Terrevoli127, G. Tersimonov3, S. Thakur142, D. Thomas121, R. Tieulent137, A. Tikhonov64, A.R. Timmins127, M. Tkacik119, A. Toia69, N. Topilskaya64, M. Toppi53, F. Torales-Acosta19, S.R. Torres38, A. Trifiró33,57, S. Tripathy70, T. Tripathy50, S. Trogolo28, G. Trombetta34, L. Tropp39, V. Trubnikov3, W.H. Trzaska128, T.P. Trzcinski143, B.A. Trzeciak38, A. Tumkin111, R. Turrisi58, T.S. Tveter20, K. Ullaland21, E.N. Umaka127, A. Uras137, M. Urioni141, G.L. Usai23, M. Vala39, N. Valle29, S. Vallero60, N. van der Kolk63, L.V.R. van Doremalen63, M. van Leeuwen92, P. Vande Vyvre35, D. Varga146, Z. Varga146, M. Varga-Kofarago146, A. Vargas46, M. Vasileiou86, A. Vasiliev90, O. Vázquez Doce107, V. Vechernin115, E. Vercellin25, S. Vergara Limón46, L. Vermunt63, R. Vértesi146, M. Verweij63, L. Vickovic36, Z. Vilakazi133, O. Villalobos Baillie113, G. Vino54, A. Vinogradov90, T. Virgili30, V. Vislavicius91, A. Vodopyanov76, B. Volkel35, M.A. Völkl105, K. Voloshin94, S.A. Voloshin144, G. Volpe34, B. von Haller35, I. Vorobyev107, D. Voscek119, J. Vrláková39, B. Wagner21, M. Weber116, A. Wegrzynek35, S.C. Wenzel35, J.P. Wessels145, J. Wiechula69, J. Wikne20, G. Wilk87, J. Wilkinson109, G.A. Willems145, E. Willsher113, B. Windelband106, M. Winn139, W.E. Witt132, J.R. Wright121, Y. Wu130, R. Xu7, S. Yalcin78, Y. Yamaguchi47, K. Yamakawa47, S. Yang21, S. Yano47,139, Z. Yin7, H. Yokoyama63, I.-K. Yoo17, J.H. Yoon62, S. Yuan21, A. Yuncu106, V. Yurchenko3, V. Zaccolo24, A. Zaman14, C. Zampolli35, H.J.C. Zanoli63, N. Zardoshti35, A. Zarochentsev115, P. Závada67, N. Zaviyalov111, H. Zbroszczyk143, M. Zhalov100, S. Zhang41, X. Zhang7, Y. Zhang130, V. Zherebchevskii115, Y. Zhi11, D. Zhou7, Y. Zhou91, J. Zhu7,109, Y. Zhu7, A. Zichichi26, G. Zinovjev3, N. Zurlo141 Affiliation notes IDeceased II Also at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy III Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy IV Also at: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear, Physics, Moscow, Russia VAlso at: Institute of Theoretical Physics, University of Wroclaw, Poland Collaboration Institutes 1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2AGH University of Science and Technology, Cracow, Poland 3Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine 4Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India 5Budker Institute for Nuclear Physics, Novosibirsk, Russia 6California Polytechnic State University, San Luis Obispo, California, United States 7Central China Normal University, Wuhan, China – 23 –