Jet fragmentation transverse momentum measurements from di-hadron correlations in √s = 7 TeV pp and √sNN = 5.02 TeV p–Pb collisions

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/ Jet fragmentation transverse momentum measurements from di-hadron correlations in √s = 7 TeV pp and √sNN = 5.02 TeV p–Pb collisions © The Author(s) 2019 Published version ALICE Collaboration ALICE Collaboration. (2019). Jet fragmentation transverse momentum measurements from dihadron correlations in √s = 7 TeV pp and √sNN = 5.02 TeV p–Pb collisions. Journal of High Energy Physics, 2019(3), Article 169. https://doi.org/10.1007/jhep03(2019)169 2019 JHEP03(2019)169 Published for SISSA by Springer Received:December 4, 2018 Revised:February 7, 2019 Accepted:February 11, 2019 Published:March 26, 2019 Jet fragmentation transverse momentum measurements from di-hadron correlations in √s=7 TeV pp and √sNN =5.02 TeV p–Pb collisions The ALICE collaboration E-mail: [email protected] Abstract: The transverse structure of jets was studied via jet fragmentation transverse momentum (jT) distributions, obtained using two-particle correlations in proton-proton and proton-lead collisions, measured with the ALICE experiment at the LHC. The highest transverse momentum particle in each event is used as the trigger particle and the region 3< pTt <15 GeV/c is explored in this study. The measured distributions show a clear narrow Gaussian component and a wide non-Gaussian one. Based on Pythia simulations, the narrow component can be related to non-perturbative hadronization and the wide component to quantum chromodynamical splitting. The width of the narrow component shows a weak dependence on the transverse momentum of the trigger particle, in agreement with the expectation of universality of the hadronization process. On the other hand, the width of the wide component shows a rising trend suggesting increased branching for higher transverse momentum. The results obtained in pp collisions at √s= 7 TeV and in p–Pb collisions at √sNN = 5.02 TeV are compatible within uncertainties and hence no significant cold nuclear matter effects are observed. The results are compared to previous measurements from CCOR and PHENIX as well as to Pythia 8 and Herwig 7 simulations. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1811.09742 Open Access, Copyright CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP03(2019)169 JHEP03(2019)169 Contents 1 Introduction 1 2 Experimental setup and data samples 2 3 Analysis method 3 4 Systematic uncertainties 7 5 Results and discussions 8 6 Conclusions 12 The ALICE collaboration 17 1 Introduction Jets are collimated sprays of hadrons originating from the fragmentation of hard partons produced in high-energy particle collisions. Studying the jet fragmentation can provide information about QCD color coherence phenomena, such as angular ordering [1], and constrain hadronization models [2–4]. The transverse fragmentation of partons is often studied using the jet fragmentation transverse momentum, jT, that describes the momentum component of particles produced in the fragmentation perpendicular to the momentum vector of the hard parton initiating the fragmentation. Previously, jThas been studied using two-particle correlations by the CCOR collaboration at ISR with pp collisions at centerof-mass energy √s= 31, 45 and 63 GeV [5] and the PHENIX collaboration at RHIC with pp collisions at √s= 200 GeV [6] and d–Au collisions at center-of-mass energy per nucleon pair √sNN = 200 GeV [7]. Jet measurements to study jThave been done by the CDF collaboration at the Tevatron with p¯p collisions at √s= 1.96 TeV [8] and the ATLAS collaboration at the LHC with Pb–Pb collisions at √sNN = 2.76 TeV [9]. Jet fragmentation in QCD consists of two different steps [10]. After the hard scattering, partons go through a QCD induced showering step, where gluons are emitted and the high virtuality of the parton is reduced. Since the transverse momentum scale (Q2) is large during the showering, perturbative QCD calculations can be applied. When Q2becomes of the order of ΛQCD, partons hadronize into final-state particles through a non-perturbative process. Two distinct components, related to the showering and hadronization phases, can be identified from the measured jTdistributions. The presence of a heavy nucleus as in p–A collisions might alter the fragmentation process. One possible mechanism for this is initial or final-state scattering of partons inside the nucleus. This is expected to lead to a broadening of jets, since the scattered partons – 1 – JHEP03(2019)169 are likely to deviate from their original direction [11]. Also the nuclear parton distribution functions can change the relative contributions of quarks and gluons compared to free nucleons, for example via gluon saturation and shadowing effects [12,13]. Understanding the implications of these cold nuclear matter effects will provide an important baseline for similar measurements in heavy-ion collisions. In this paper, the jTdistributions are studied using two-particle correlations, measured by the ALICE detector in √s= 7 TeV pp and √sNN = 5.02 TeV p–Pb collisions. The correlation approach is chosen as opposed to full jet reconstruction based on the discussion in refs. [14,15], where it is argued that two-particle correlations are more sensitive to the soft and non-perturtabive parts of the jet fragmentation. This is important for the separation of the two jTcomponents and in searching for cold nuclear matter effects that are expected to play a larger role at lower momenta. This paper is structured as follows. The event and track selection together with the used data samples are described in section 2. The analysis details are discussed in section 3, followed by the systematic uncertainty analysis in section 4. The obtained results are shown in section 5and the observations are summarized in section 6. 2 Experimental setup and data samples This analysis uses two different datasets. The √s= 7 TeV pp (3.0·108events, integrated luminosity Lint = 4.8 nb−1) collisions were recorded in 2010 and the √sNN = 5.02 TeV p–Pb (1.3·108events, Lint = 62 µb−1) collisions were recorded in 2013 by the ALICE detector [16]. The details of the performance of the ALICE detector during LHC Run 1 (2009–2013) are presented in ref. [17]. The charged particle tracks used in this analysis are reconstructed using the Inner Tracking System (ITS) [18] and the Time Projection Chamber (TPC) [19]. The tracking detectors are located inside a large solenoidal magnet which provides a homogeneous magnetic field of 0.5 T. They are used to reconstruct the tracks within a pseudorapidity range of |η|<0.9 over the full azimuth. The ITS consists of six layers of silicon detectors: the two innermost layers are the Silicon Pixel Detector (SPD), the two middle layers are the Silicon Drift Detector (SDD) and the two outermost layers are the Silicon Strip Detector (SSD). The TPC is a gas-filled detector capable of providing three-dimensional tracking information over a large volume. Combining information from the ITS and the TPC, the momenta of charged particles from 0.15 to 100 GeV/c can be determined with a resolution ranging from 1 to 10%. For tracks without the ITS information, the momentum resolution is comparable to that of ITS+TPC tracks below transverse momentum pT= 10 GeV/c, but for higher momenta the resolution reaches 20% at pT= 50 GeV/c [17,20]. Charged particle tracks with pT>0.3 GeV/c in the region |η|<0.8 are selected for the analysis. Events are triggered based on the information of the V0 detector [21] together with the SPD. The V0 detector consists of two scintillator stations, one on each side of the interaction point, covering −3.7< η < −1.7 (V0C) and 2.8< η < 5.1 (V0A). For the 2010 pp collisions, the minimum bias (MB) triggered events are required to have at least one hit from a charged particle traversing the SPD or either side of the V0. The pseudorapidity coverage of the – 2 – JHEP03(2019)169 SPD is |η|<2 in the first layer and |η|<1.5 in the second layer. Combining this with the acceptance of the V0, the particles are detected in the range −3.7< η < 5.1. The minimum bias trigger definition for the 2013 p–Pb collisions is slightly different. Events are required to have signals in both V0A and V0C. This condition is also used later offline to reduce the contamination of the data sample from beam-gas events by using the timing difference of the signal between the two stations [17]. For the pp collisions, similar track cuts as in ref. [22] are used: at least two hits in the ITS are required, one of which needs to be in the three innermost layers, and 70 hits out of 159 are required in the TPC. In addition, the distance of the closest approach (DCA) of the track to the primary vertex is required to be smaller than 2 cm in the beam direction. In the transverse direction, a pTdependent cut DCA <0.0105 cm + 0.035 cm ·p−1.1 Tis used, where pTis measured in units of GeV/c. These track cuts are tuned to minimize the contamination from secondary particles. For the p–Pb collisions the tracks are selected following the so called hybrid approach, which is described in detail in ref. [23]. This approach differs from the one presented above in the selection of ITS tracks. The tracks with at least one hit in the SPD and at least two hits in the whole ITS are always accepted. In addition, tracks with fewer than two hits in the ITS or no hits in the SPD are accepted, but only if an additional vertex constraint is fulfilled. The DCA cuts are also looser: smaller than 3.2 cm in the beam direction and smaller than 2.4 cm in the transverse direction. With this track selection, the azimuthal angle (ϕ) distribution is as uniform as possible, because it is not affected by dead regions in SPD. This is important for a two-particle correlation analysis. The momentum resolutions of the two classes of particles are comparable up to pT≈10 GeV/c, but after that, tracks without ITS requirements have a worse resolution [17,20]. 3 Analysis method The analysis is performed by measuring two-particle correlation functions. In each event, the trigger particle is chosen to be the charged particle with the highest reconstructed pT inside the acceptance region, called the leading particle. For the momentum range studied in the analysis, simulation studies show that the direction of the leading particle can be assumed in good approximation that one of the jet axis, which is the axis defined by the momentum vector of the hard parton initiating the jet fragmentation. The associated particles close in the phase-space to the leading one are then interpreted as jet fragments. The jet fragmentation transverse momentum, jT, is defined as the component of the associated particle momentum, ~pa, transverse to the trigger particle momentum, ~pt. The resulting ~ jTis illustrated in figure 1. The length of the ~ jTvector is jT=|~pt×~pa| |~pt|.(3.1) It is commonly interpreted as a transverse kick with respect to the initial hard parton momentum that is given to a fragmenting particle during the fragmentation process. In other words, jTmeasures the momentum spread of the jet fragments around the jet axis. – 3 – JHEP03(2019)169 Figure 1. Illustration of ~ jTand xk. The jet fragmentation transverse momentum, ~ jT, is defined as the transverse momentum component of the associated particle momentum, ~pa, with respect to the trigger particle momentum, ~pt. The fragmentation variable xkis the projection of ~pato ~ptdivided by pt. In the analysis, results are presented in bins of the fragmentation variable xkwhich is defined as the projection of the momentum of the associated to the trigger particle one, divided by the momentum of the trigger particle: xk=~pt·~pa ~p2 t .(3.2) This is also illustrated in figure 1. Because xkis defined as a fraction of the trigger particle momentum, it is intuitive to define a three-dimensional near side with respect to the axis defined by the trigger momentum. The associated particle is defined to be in the near side if it is in the same hemisphere as the trigger particle: ~pt·~pa>0.(3.3) The results have been binned in xkrather than associated particle transverse momentum (pTa) because the definition of jT(eq. (3.1)) has an explicit pTa dependence. Bins in pTa would bias the results since pairs with larger jTare more likely to be in bins of larger pTa. In the case of xkthis bias is not present, since xkand jTmeasure momentum components along perpendicular axes. Another advantage for using xkis that the relative pTof the associate particles with respect to trigger pT(pTt) stays the same in different pTt bins. It was verified with a Pythia 8 [2,24] Monash tune simulation that the average fraction of the leading parton momentum taken by the leading particle (hzti) varies less than 0.05 units inside the used xkbins 0.2< xk<0.4, 0.4< xk<0.6, and 0.6< xk<1.0, with lower pTt bins having slightly larger hztithan higher bins. The extracted jTdistribution is of the form 1 Ntrigg 1 jT dN djTpTt, xk, jT=Cassociated(pTa)CAcc(∆η, ∆ϕ)Npairs(pTt, xk, jT) jTNtrigg ∆jT ,(3.4) where Ntrigg is the number of triggers, Npairs(pTt, xk, jT) is the number of trigger-associated pairs, ∆jTis the bin width of the used jTbin, Cassociated(pTa) is the single track efficiency – 4 – JHEP03(2019)169 correction for the associated particle and CAcc(∆η, ∆ϕ) is the pair acceptance correction. The single track efficiency correction is estimated by Monte Carlo simulations of Pythia 6 [25], Pythia 8 or DPMJET [26] events, using GEANT3 [27] detector simulation and event reconstruction. The pair acceptance correction is the inverse of the normalized mixed event distribution sampled at the corresponding (∆η, ∆ϕ) value. The mixed event distribution is constructed by correlating trigger and associated particles from different events in the data sample. In the mixed event distribution, away-side particles must be included to properly correct for detector and acceptance effects. In this study, the jTdistribution is determined by pairing all charged particles inside each xkbin with the leading particle and calculating jTfor each of these pairs in an event. After that, two distinct components are extracted from the jTdistribution. A generator level Pythia 8 simulation was performed to gain support for the separation of these components. To create a clean di-jet event sample, Pythia 8 was initialized to produce two hard gluons with a constant invariant mass for each event. The final-state QCD shower in Pythia 8 is modeled as a timelike shower, as explained in ref. [28]. Two simulations were studied, one where the final-state shower was present and one where it was disabled. Without the final-state shower, the hadronization of the leading parton via Lund string fragmentation [29] develops without a QCD showering phase preceding it. When the final-state shower is allowed, the partons go through both showering and hadronization. The results of this study are presented in figure 2. The squares show a nearly Gaussian distribution resulting from the case when the final-state shower is disabled. The circles are obtained when the final-state shower is enabled. A long tail is observed which was not seen in the case with final-state shower off. To estimate the QCD showering component, it is assumed that hadronization dominates at low jT, and the distributions from the two simulations coincide at jT= 0. The “hadronization only”-distribution in figure 2needs to be scaled with a factor of 0.63 for this, since without QCD splittings the partons hadronize at higher scale, producing more particles. With the subtraction of the “hadronization only”-distribution from the total one, the QCD showering part can be separated. This is represented by the diamond symbols in figure 2. This study shows a possible factorization of the showering and hadronization parts of the jet fragmentation in Pythia 8. Based on simulations, template fit functions for hadronization and showering components have been estimated and used to extract the corresponding terms from the data. Since ~ jTis a two-dimensional vector, using twodimensional forms for the fit functions allows to extract the final results from the functions more easily. Assuming that there is no dependence on the polar angle of the vector, the angle can be integrated out and the distributions written as a function of the length of the vector. The hadronization part can be described by a Gaussian: fG(jT) = A2 A2 1 e−j2 T 2A2 1,(3.5) and the showering part by an inverse gamma function of the form: fIG(jT) = A3AA4−1 5 Γ(A4−1) e−A5 jT jA4+1 T ,(3.6) – 5 – JHEP03(2019)169 2 4 6 8 10 )c (GeV/ T j 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 ) 2 eVG/ 2 c ( T jd Nd T j 1 trigg N 1 gluon + gluon→) 2 cPYTHIA 8: M(100 GeV/ 0.6< x< 0.4⊗ c< 8 GeV/ Tt p< 6 FSR on FSR off×0.63 Soft radiation (FSR on - FSR off) Gaussian fit to FSR off Inverse Gamma fit to soft radiation Figure 2. Results from a Pythia 8 study with a di-gluon initial state. The circular symbols are obtained when the final-state shower is enabled. The square symbols show the distribution without final-state showering. The diamond symbols representing soft radiation are obtained as a difference between the other two distributions. The distribution without final-state showering is fitted with a Gaussian and the soft radiation part with an inverse gamma function. where A1...5are the free fit parameters and Γ is the gamma function. In this paper, the hadronization part will be called the narrow component and the showering part the wide component. In the data, in addition to the signal, a background component mostly due to the underlying event is observed. Examples of measured jTdistributions with background included and subtracted are presented in figure 3. An η-gap method is used to estimate the background contribution. Pairs with |∆η|>1.0 are considered as background from the underlying event. The background templates for the analysis are built by randomizing the pseudorapidities for the trigger and the associated particles, following the inclusive charged particle pseudorapidity distributions. Twenty randomized pairs are generated from each background pair to improve the statistics for the background. The template histograms, generated in bins of pTt and xk, are then fitted to the jTdistribution together with a sum of a Gaussian function and an inverse gamma function. It can be seen from figure 3that the fit is in good agreement with the data, except in the region around jT∼0.4 GeV/c, where the data shows an increase with respect to the fit function. Pythia studies show that this structure is caused by correlations from neutral meson decays, dominated by decays of ρ0and ω, where one of the decay daughters is the leading charged particle in the event. The effect of this structure is taken into account in the evaluation of the systematic uncertainties. The goal of the analysis is to determine the root-mean-square RMS = qj2 Tvalues and yields of the narrow and wide jTcomponents. These are calculated from the parameters of the fit functions in equations (3.5) and (3.6). – 6 – JHEP03(2019)169 0 5 10 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 ) 2 eVG/ 2 c ( T jd Nd T j 1 trigg N 1 = 7 TeV MBsALICE pp 0.4< x< 0.2⊗ c< 8 GeV/ LP Tt p< 6 distribution T j > 1.0η∆ bkgd, T j Three component fit Narrow component Wide component 0 5 10 )c (GeV/ T j 0.6 0.8 1 1.2 1.4 fit distribution 0 2 4 5− 10 4− 10 3− 10 2− 10 1− 10 1 ) 2 eVG/ 2 c ( T jd Nd T j 1 trigg N 1 = 7 TeV MBsALICE pp 0.4< x< 0.2⊗ c< 8 GeV/ LP Tt p< 6 signal T j Two component fit Narrow component Wide component 0 2 4 )c (GeV/ T j 0.6 0.8 1 1.2 1.4 fit signal Figure 3.Left: measured jTdistribution including a three-component fit. The three components describe the background (circular symbols), hadronization (long dashed line), and showering (short dashed line). Right: the same jTdistribution but with background subtracted. 4 Systematic uncertainties The systematic uncertainties considered for this analysis arise from the background determination, the signal fitting procedure and the cuts used to select the tracks. The uncertainties related to the tracking are estimated from variations of the track selection cuts defined in section 2. The resulting variations of the RMS and yield are below 3% in most cases, but effects up to 17% are observed for the yield of the wide component. The tracking efficiency contributes to the uncertainty of the yields only. This uncertainty is estimated from the difference between data and simulation in the TPC-ITS track matching efficiency as is previously done in refs. [30] and [31]. For pp collisions this uncertainty is 5% and for p–Pb ones 4%. The effect due to the subleading track being reconstructed as a leading track was studied using simulations and found to be negligible due to steep slope of the trigger spectrum. The main source of uncertainty from the background evaluation comes from the background region definition. As an alternative method to the default procedure, uncorrelated background templates are generated from particles with R=p∆ϕ2+ ∆η2>1 instead of those at large ∆η, and pseudorapidities for the particle pairs are randomized together with azimuthal angles. The associated uncertainty is typically below 5%, but for the yield of the wide component the uncertainty can grow up to 46% in the lowest pTt and xkbins where the signal to background ratio is the worst (0.84 for pp and 0.33 for p–Pb). Changing the size of the η-gap produces small uncertainties compared to other sources, usually below – 7 – JHEP03(2019)169 Bundesministerium f¨ur Bildung, Wissenschaft, Forschung und Technologie (BMBF) and GSI Helmholtzzentrum f¨ur 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; Centro Fermi — Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi and Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology, Nagasaki Institute of Applied Science (IIST), Japan Society for the Promotion of Science (JSPS) KAKENHI and Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnolog´ıa, through Fondo de Cooperaci´on Internacional en Ciencia y Tecnolog´ıa (FONCICYT) and Direcci´on 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 Cat´olica del Per´u, Peru; Ministry of Science and Higher Education and National Science Centre, 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 Romanian National Agency for Science, Technology and Innovation, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science of the Russian Federation and National Research Centre Kurchatov Institute, 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; National Science and Technology Development Agency (NSDTA), Suranaree University of Technology (SUT) 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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Mart´ınez Garc´ıa113, M. Martinez Pedreira34, S. Masciocchi104, M. Masera26, A. Masoni54, L. Massacrier61, E. Masson113, – 18 – JHEP03(2019)169 A. Mastroserio52,137, A.M. Mathis116,103, P.F.T. Matuoka120, A. Matyja117,129, C. Mayer117, M. Mazzilli33, M.A. Mazzoni57, F. Meddi23, Y. Melikyan91, A. Menchaca-Rocha72, E. Meninno30, M. Meres14, S. Mhlanga124, Y. Miake132, L. Micheletti26, M.M. Mieskolainen43, D.L. Mihaylov103, K. Mikhaylov75,64, A. Mischke63, A.N. Mishra70, D. Mi´skowiec104, J. Mitra140, C.M. Mitu68, N. Mohammadi34, A.P. Mohanty63, B. Mohanty85, M. Mohisin Khan17,iv, M.M. Mondal66, C. Mordasini103, D.A. Moreira De Godoy143, L.A.P. Moreno44, S. Moretto29, A. Morreale113, A. Morsch34, T. Mrnjavac34, V. Muccifora51, E. Mudnic35, D. M¨uhlheim143, S. Muhuri140, J.D. Mulligan145, M.G. Munhoz120, K. M¨unning42, R.H. Munzer69, H. Murakami131, S. Murray73, L. Musa34, J. Musinsky65, C.J. Myers125, J.W. Myrcha141, B. Naik48, R. Nair84, B.K. Nandi48, R. Nania53,10, E. Nappi52, M.U. 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Selyuzhenkov104,91, S. Senyukov135, E. Serradilla72, P. Sett48, A. Sevcenco68, A. Shabanov62, A. Shabetai113, R. Shahoyan34, W. Shaikh107, A. Shangaraev90, A. Sharma98, A. Sharma99, M. Sharma99, N. Sharma98, A.I. Sheikh140, K. Shigaki45, M. Shimomura82, S. Shirinkin64, Q. Shou6,110, Y. Sibiriak87, S. Siddhanta54, T. Siemiarczuk84, D. Silvermyr80, G. Simatovic89, G. Simonetti103,34, R. Singh85, R. Singh99, V. Singhal140, T. Sinha107, B. Sitar14, M. Sitta32, T.B. Skaali21, M. Slupecki126, – 19 – JHEP03(2019)169 N. Smirnov145, R.J.M. Snellings63, T.W. Snellman126, J. Sochan115, C. Soncco109, J. Song60, A. Songmoolnak114, F. Soramel29, S. Sorensen129, F. Sozzi104, I. Sputowska117, J. Stachel102, I. Stan68, P. Stankus94, E. Stenlund80, D. Stocco113, M.M. Storetvedt36, P. Strmen14, A.A.P. Suaide120, T. Sugitate45, C. Suire61, M. Suleymanov15, M. Suljic34, R. Sultanov64, M. ˇ Sumbera93, S. Sumowidagdo50, K. Suzuki112, S. Swain66, A. Szabo14, I. Szarka14, U. Tabassam15, J. Takahashi121, G.J. Tambave22, N. Tanaka132, M. Tarhini113, M.G. Tarzila47, A. Tauro34, G. Tejeda Mu˜noz44, A. Telesca34, C. Terrevoli29,125, D. Thakur49, S. Thakur140, D. Thomas118, F. Thoresen88, R. Tieulent134, A. Tikhonov62, A.R. Timmins125, A. Toia69, N. Topilskaya62, M. Toppi51, S.R. Torres119, S. Tripathy49, T. Tripathy48, S. Trogolo26, G. Trombetta33, L. Tropp38, V. Trubnikov2, W.H. Trzaska126, T.P. Trzcinski141, B.A. Trzeciak63, T. Tsuji131, A. Tumkin106, R. Turrisi56, T.S. Tveter21, K. Ullaland22, E.N. Umaka125, A. Uras134, G.L. Usai24, A. Utrobicic97, M. Vala38,115, L. Valencia Palomo44, N. Valle138, N. van der Kolk63, L.V.R. van Doremalen63, J.W. Van Hoorne34, M. van Leeuwen63, P. Vande Vyvre34, D. Varga144, A. Vargas44, M. Vargyas126, R. Varma48, M. Vasileiou83, A. Vasiliev87, O. V´azquez Doce103,116, V. Vechernin111, A.M. Veen63, E. Vercellin26, S. Vergara Lim´on44, L. Vermunt63, R. Vernet7, R. V´ertesi144, L. Vickovic35, J. Viinikainen126, Z. Vilakazi130, O. Villalobos Baillie108, A. Villatoro Tello44, G. Vino52, A. Vinogradov87, T. Virgili30, V. Vislavicius88, A. Vodopyanov75, B. Volkel34, M.A. V¨olkl101, K. Voloshin64, S.A. Voloshin142, G. Volpe33, B. von Haller34, I. Vorobyev116,103, D. Voscek115, J. Vrl´akov´a38, B. Wagner22, M. Wang6, Y. Watanabe132, M. Weber112, S.G. Weber104, A. Wegrzynek34, D.F. Weiser102, S.C. Wenzel34, J.P. Wessels143, U. Westerhoff143, A.M. Whitehead124, E. Widmann112, J. Wiechula69, J. Wikne21, G. Wilk84, J. Wilkinson53, G.A. Willems34,143, E. Willsher108, B. Windelband102, W.E. Witt129, Y. Wu128, R. Xu6, S. Yalcin77, K. Yamakawa45, S. Yano136, Z. Yin6, H. Yokoyama63,132, I.-K. Yoo18, J.H. Yoon60, S. Yuan22, V. Yurchenko2, V. Zaccolo25,58, A. Zaman15, C. Zampolli34, H.J.C. Zanoli120, N. Zardoshti108,34, A. Zarochentsev111, P. Z´avada67, N. Zaviyalov106, H. Zbroszczyk141, M. Zhalov96, X. Zhang6, Y. Zhang6, Z. Zhang6,133, C. Zhao21, V. Zherebchevskii111, N. Zhigareva64, D. Zhou6, Y. Zhou88, Z. Zhou22, H. Zhu6, J. Zhu6, Y. Zhu6, A. Zichichi27,10, M.B. Zimmermann34, G. Zinovjev2, N. Zurlo139 iDeceased ii Dipartimento DET del Politecnico di Torino, Turin, Italy iii M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear, Physics, Moscow, Russia iv Department of Applied Physics, Aligarh Muslim University, Aligarh, India vInstitute of Theoretical Physics, University of Wroclaw, Poland vi Georgia State University, Atlanta, Georgia, United States 1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine 3Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India 4Budker Institute for Nuclear Physics, Novosibirsk, Russia 5California Polytechnic State University, San Luis Obispo, California, United States 6Central China Normal University, Wuhan, China 7Centre de Calcul de l’IN2P3, Villeurbanne, Lyon, France 8Centro de Aplicaciones Tecnol´ogicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 9Centro de Investigaci´on y de Estudios Avanzados (CINVESTAV), Mexico City and M´erida, Mexico – 20 – JHEP03(2019)169 10 Centro Fermi — Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Rome, Italy 11 Chicago State University, Chicago, Illinois, United States 12 China Institute of Atomic Energy, Beijing, China 13 Chonbuk National University, Jeonju, Republic of Korea 14 Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovakia 15 COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan 16 Creighton University, Omaha, Nebraska, United States 17 Department of Physics, Aligarh Muslim University, Aligarh, India 18 Department of Physics, Pusan National University, Pusan, Republic of Korea 19 Department of Physics, Sejong University, Seoul, Republic of Korea 20 Department of Physics, University of California, Berkeley, California, United States 21 Department of Physics, University of Oslo, Oslo, Norway 22 Department of Physics and Technology, University of Bergen, Bergen, Norway 23 Dipartimento di Fisica dell’Universit`a ‘La Sapienza’ and Sezione INFN, Rome, Italy 24 Dipartimento di Fisica dell’Universit`a and Sezione INFN, Cagliari, Italy 25 Dipartimento di Fisica dell’Universit`a and Sezione INFN, Trieste, Italy 26 Dipartimento di Fisica dell’Universit`a and Sezione INFN, Turin, Italy 27 Dipartimento di Fisica e Astronomia dell’Universit`a and Sezione INFN, Bologna, Italy 28 Dipartimento di Fisica e Astronomia dell’Universit`a and Sezione INFN, Catania, Italy 29 Dipartimento di Fisica e Astronomia dell’Universit`a and Sezione INFN, Padova, Italy 30 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Universit`a and Gruppo Collegato INFN, Salerno, Italy 31 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy 32 Dipartimento di Scienze e Innovazione Tecnologica dell’Universit`a del Piemonte Orientale and INFN Sezione di Torino, Alessandria, Italy 33 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy 34 European Organization for Nuclear Research (CERN), Geneva, Switzerland 35 Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia 36 Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway 37 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 38 Faculty of Science, P.J. ˇ Saf´arik University, Koˇsice, Slovakia 39 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universit¨at Frankfurt, Frankfurt, Germany 40 Gangneung-Wonju National University, Gangneung, Republic of Korea 41 Gauhati University, Department of Physics, Guwahati, India 42 Helmholtz-Institut f¨ur Strahlenund Kernphysik, Rheinische Friedrich-Wilhelms-Universit¨at Bonn, Bonn, Germany 43 Helsinki Institute of Physics (HIP), Helsinki, Finland 44 High Energy Physics Group, Universidad Aut´onoma de Puebla, Puebla, Mexico 45 Hiroshima University, Hiroshima, Japan 46 Hochschule Worms, Zentrum f¨ur Technologietransfer und Telekommunikation (ZTT), Worms, Germany 47 Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 48 Indian Institute of Technology Bombay (IIT), Mumbai, India 49 Indian Institute of Technology Indore, Indore, India 50 Indonesian Institute of Sciences, Jakarta, Indonesia 51 INFN, Laboratori Nazionali di Frascati, Frascati, Italy 52 INFN, Sezione di Bari, Bari, Italy 53 INFN, Sezione di Bologna, Bologna, Italy – 21 – JHEP03(2019)169 54 INFN, Sezione di Cagliari, Cagliari, Italy 55 INFN, Sezione di Catania, Catania, Italy 56 INFN, Sezione di Padova, Padova, Italy 57 INFN, Sezione di Roma, Rome, Italy 58 INFN, Sezione di Torino, Turin, Italy 59 INFN, Sezione di Trieste, Trieste, Italy 60 Inha University, Incheon, Republic of Korea 61 Institut de Physique Nucl´eaire d’Orsay (IPNO), Institut National de Physique Nucl´eaire et de Physique des Particules (IN2P3/CNRS), Universit´e de Paris-Sud, Universit´e Paris-Saclay, Orsay, France 62 Institute for Nuclear Research, Academy of Sciences, Moscow, Russia 63 Institute for Subatomic Physics, Utrecht University/Nikhef, Utrecht, Netherlands 64 Institute for Theoretical and Experimental Physics, Moscow, Russia 65 Institute of Experimental Physics, Slovak Academy of Sciences, Koˇsice, Slovakia 66 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 67 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 68 Institute of Space Science (ISS), Bucharest, Romania 69 Institut f¨ur Kernphysik, Johann Wolfgang Goethe-Universit¨at Frankfurt, Frankfurt, Germany 70 Instituto de Ciencias Nucleares, Universidad Nacional Aut´onoma de M´exico, Mexico City, Mexico 71 Instituto de F´ısica, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 72 Instituto de F´ısica, Universidad Nacional Aut´onoma de M´exico, Mexico City, Mexico 73 iThemba LABS, National Research Foundation, Somerset West, South Africa 74 Johann-Wolfgang-Goethe Universit¨at Frankfurt Institut f¨ur Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 75 Joint Institute for Nuclear Research (JINR), Dubna, Russia 76 Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 77 KTO Karatay University, Konya, Turkey 78 Laboratoire de Physique Subatomique et de Cosmologie, Universit´e Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 79 Lawrence Berkeley National Laboratory, Berkeley, California, United States 80 Lund University Department of Physics, Division of Particle Physics, Lund, Sweden 81 Nagasaki Institute of Applied Science, Nagasaki, Japan 82 Nara Women’s University (NWU), Nara, Japan 83 National and Kapodistrian University of Athens, School of Science, Department of Physics , Athens, Greece 84 National Centre for Nuclear Research, Warsaw, Poland 85 National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India 86 National Nuclear Research Center, Baku, Azerbaijan 87 National Research Centre Kurchatov Institute, Moscow, Russia 88 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 89 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands 90 NRC Kurchatov Institute IHEP, Protvino, Russia 91 NRNU Moscow Engineering Physics Institute, Moscow, Russia 92 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom 93 Nuclear Physics Institute of the Czech Academy of Sciences, ˇ Reˇz u Prahy, Czech Republic 94 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States 95 Ohio State University, Columbus, Ohio, United States 96 Petersburg Nuclear Physics Institute, Gatchina, Russia 97 Physics department, Faculty of science, University of Zagreb, Zagreb, Croatia 98 Physics Department, Panjab University, Chandigarh, India 99 Physics Department, University of Jammu, Jammu, India 100 Physics Department, University of Rajasthan, Jaipur, India – 22 – JHEP03(2019)169 101 Physikalisches Institut, Eberhard-Karls-Universit¨at T¨ubingen, T¨ubingen, Germany 102 Physikalisches Institut, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg, Germany 103 Physik Department, Technische Universit¨at M¨unchen, Munich, Germany 104 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum f¨ur Schwerionenforschung GmbH, Darmstadt, Germany 105 Rudjer Boˇskovi´c Institute, Zagreb, Croatia 106 Russian Federal Nuclear Center (VNIIEF), Sarov, Russia 107 Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India 108 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 109 Secci´on F´ısica, Departamento de Ciencias, Pontificia Universidad Cat´olica del Per´u, Lima, Peru 110 Shanghai Institute of Applied Physics, Shanghai, China 111 St. Petersburg State University, St. Petersburg, Russia 112 Stefan Meyer Institut f¨ur Subatomare Physik (SMI), Vienna, Austria 113 SUBATECH, IMT Atlantique, Universit´e de Nantes, CNRS-IN2P3, Nantes, France 114 Suranaree University of Technology, Nakhon Ratchasima, Thailand 115 Technical University of Koˇsice, Koˇsice, Slovakia 116 Technische Universit¨at M¨unchen, Excellence Cluster ‘Universe’, Munich, Germany 117 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 118 The University of Texas at Austin, Austin, Texas, United States 119 Universidad Aut´onoma de Sinaloa, Culiac´an, Mexico 120 Universidade de S˜ao Paulo (USP), S˜ao Paulo, Brazil 121 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 122 Universidade Federal do ABC, Santo Andre, Brazil 123 University College of Southeast Norway, Tonsberg, Norway 124 University of Cape Town, Cape Town, South Africa 125 University of Houston, Houston, Texas, United States 126 University of Jyv¨askyl¨a, Jyv¨askyl¨a, Finland 127 University of Liverpool, Liverpool, United Kingdom 128 University of Science and Techonology of China, Hefei, China 129 University of Tennessee, Knoxville, Tennessee, United States 130 University of the Witwatersrand, Johannesburg, South Africa 131 University of Tokyo, Tokyo, Japan 132 University of Tsukuba, Tsukuba, Japan 133 Universit´e Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 134 Universit´e de Lyon, Universit´e Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, Lyon, France 135 Universit´e de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France 136 Universit´e Paris-Saclay Centre d’ ´ Etudes de Saclay (CEA), IRFU, Department de Physique Nucl´eaire (DPhN), Saclay, France 137 Universit`a degli Studi di Foggia, Foggia, Italy 138 Universit`a degli Studi di Pavia, Pavia, Italy 139 Universit`a di Brescia, Brescia, Italy 140 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India 141 Warsaw University of Technology, Warsaw, Poland 142 Wayne State University, Detroit, Michigan, United States 143 Westf¨alische Wilhelms-Universit¨at M¨unster, Institut f¨ur Kernphysik, M¨unster, Germany 144 Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary 145 Yale University, New Haven, Connecticut, United States 146 Yonsei University, Seoul, Republic of Korea – 23 –