K∗(892)0 and φ(1020) production in p-Pb collisions at √sNN = 8.16 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/ K(892)0 and φ(1020) production in p-Pb collisions at √sNN = 8.16 TeV ©2023 CERN, for the ALICE Collaboration Published version ALICE Collaboration ALICE Collaboration. (2023). K(892)0 and φ(1020) production in p-Pb collisions at √sNN = 8.16 TeV. Physical Review C, 107(5), Article 055201. https://doi.org/10.1103/PhysRevC.107.055201 2023 PHYSICAL REVIEW C 107, 055201 (2023) K∗(892)0and φ(1020) production in p-Pb collisions at √sNN =8.16 TeV S. Acharya et al.∗ (ALICE Collaboration) (Received 28 October 2021; accepted 16 December 2022; published 9 May 2023) The production of K∗(892)0and φ(1020) resonances has been measured in p-Pb collisions at √sNN = 8.16 TeV using the ALICE detector. Resonances are reconstructed via their hadronic decay channels in the rapidity interval −0.5 <y<0 and the transverse momentum spectra are measured for various multiplicity classes up to pT=20 GeV/cforK ∗(892)0and pT=16 GeV/cforφ(1020). The pT-integrated yields and mean transverse momenta are reported and compared with previous results in pp, p-Pb and Pb-Pb collisions. The xT scaling for K∗(892)0and φ(1020) resonance production is newly tested in p-Pb collisions and found to hold in the high-pTregion at Large Hadron Collider energies. The nuclear modification factors (RpPb) as a function of pT for K∗0and φat √sNN =8.16 TeV are presented along with the new RpPb measurements of K∗0,φ,,andat √sNN =5.02 TeV. At intermediate pT(2–8 GeV/c), RpPb of ,show a Cronin-like enhancement, while K∗0and φshow no or little nuclear modification. At high pT(>8 GeV/c), the RpPb values of all hadrons are consistent with unity within uncertainties. The RpPb of K∗(892)0and φ(1020) at √sNN =8.16 and 5.02 TeV show no significant energy dependence. DOI: 10.1103/PhysRevC.107.055201 I. INTRODUCTION High-energy heavy-ion (A-A) collisions provide a unique opportunity to study the deconfined quark-gluon plasma (QGP) created in such collisions [1–3]. The hot and dense medium created in heavy-ion collisions evolves with time and cools down to form a phase where hadron resonance gas is studied. Evidence at Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) suggest that the QGP phase is followed by a hadronic phase where the hadrons interact via rescattering and regeneration processes, before the final freeze-out. Resonances are short-lived hadrons that decay via the strong interactions. They play an important role to understand the particle production mechanisms and for the characterization of the dynamic evolution of the system formed in heavy-ion collisions. They are used as a sensitive probe of the hadronic phase, where their mass, width and yield could be modified due to interaction of their decay products through re-scattering and regeneration processes [4–15]. ALICE has previously measured K∗(892)0and φ(1020) production in pp collisions at √s=5.02, 7, 8, and 13 TeV [16–22], in p-Pb collisions at √sNN =5.02 TeV [23] and Pb-Pb collisions at √sNN =2.76 and 5.02 TeV [13,15,18,19]. Proton-lead collisions are intermediate between pp and Pb-Pb collisions in terms of the size of the colliding system ∗Full author list given at the end of the article. 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. and the produced particle multiplicities. Recent measurements in high-multiplicity pp,p-Pb, and d-Au collisions at different energies have uncovered strong flowlike effects even in these small collision systems [14,22–25], whose origin is not fully understood. To investigate the mechanism of particle production and the origin of these effects, the ALICE Collaboration has studied the multiplicity dependence of light-flavor particle production for many species like π±,K ±,K 0 S,K ∗(892)0, φ(1020), ,(1520), ∗±,±,∗0,±in p-Pb collisions at √sNN =5.02 TeV [23,26–29] and in pp collisions at √s=7 and 13 TeV [17,20,22,30]. This paper reports on the multiplicity dependence of K∗(892)0and φ(1020) meson production at the highest center-of-mass energy, √sNN = 8.16 TeV, reached at the LHC in p-Pb collisions. This provides an opportunity to extend the previous studies of production of these particles in p-Pb collisions at √sNN =5.02 TeV [23] to a higher multiplicity reach and a larger pTcoverage. Hadron production is governed by the soft and hard scattering processes at LHC energies. The bulk of particles produced in high-energy collisions is dominated by low transverse momentum particles from soft interactions, which are nonperturbative in nature. The yield of particles at low pTis not well understood from the first principles of QCD and their description relies on phenomenological QCD-based models such as EPOS-LHC, DPMJET, and HIJING. The measurements in the low-momentum region of the spectra presented in this article provide input for the tuning of these event generators. In this paper, measurements of K∗(892)0and φ(1020) are compared with predictions from EPOS-LHC [31], DPMJET [32], and HIJING [33]. The transverse momentum spectra of light–flavor hadrons have shown a clear evolution with multiplicity in high-energy pp and p-Pb collisions [17,22,23,26,34], similar to that observed in Pb-Pb collisions [13,18,19,35,36], where in the latter 2469-9985/2023/107(5)/055201(21) 055201-1 ©2023 CERN, for the ALICE Collaboration S. ACHARYA et al. PHYSICAL REVIEW C 107, 055201 (2023) case the effect is usually attributed to a collective expansion of the system. The increase in slope of the pTspectra as a function of multiplicity attributed to the radial flow is related to the low-pTregion of the spectrum, where flow is relevant. This feature is also reflected in an increase of the average transverse momentum pTwith multiplicity. In contrast to the yields dN/dy, which evolve smoothly as a function of multiplicity for different collision systems, the pTof lightflavor hadrons as well as K∗(892)0and φ(1020), rises faster as a function of multiplicity in pp and p-Pb collisions than in Pb-Pb collisions, as discussed in Refs. [20,22,23]. The new measurements, with the highest multiplicity reach in p-Pb collisions, and comparison with the different model predictions can be used to further extend these studies. The high-pTparticle production is analyzed within the framework of perturbative Quantum Chromodynamics (pQCD) which features a nearly scale-invariant behavior of elementary parton-parton hard-scattering processes [37,38]. The convolution of hard scattering cross-sections with the parton distribution functions (PDFs) of incident hadrons and fragmentation functions (FFs) leads to the observed scaling of the inclusive invariant cross-section Ed3σ/dp 3as p−n Tat fixed transverse x,xT=2pT/√s[39,40]. The exponent n can be related to the scattering processes in which high-pT hadrons are produced. If hadrons are produced by leading twist (LT) 2 →2 hard subprocesses, then n ≈4, and for higher twist (HT) processes, n≈8. It has been observed that the exponent value decreases with increasing collision energy, which suggests that the contribution of higher twist processes on high-pThadron production is reduced as a function of energy. The transverse momentum distributions of different particle species at high pTare observed to satisfy a universal xTscaling over a wide energy range up to √s=13 TeV. This scaling behavior was observed by the CDF [41–43] and UA1 [44] Collaborations in p(p) collisions, and by the STAR [45], ALICE [46], and CMS [47] Collaborations in pp collisions. In this paper, the xTscaling of K∗(892)0and φ(1020) mesons are tested in p-Pb collisions at LHC energies. The transverse momentum distributions of the particles in p-Pb collisions are compared to those in pp collisions using the nuclear modification factor (RpPb). The measurement of RpPb acts as a control experiment observable in p-Pb collisions [48]in the context of the observed high-pThadron suppression in Pb-Pb collisions [15]. In this paper, RpPb measurements of K∗(892)0and φ(1020) in p-Pb collisions at √sNN =5.02 and 8.16 TeV, and that of and in p-Pb collisions at 5.02 TeV are reported. Similar measurements are also reported for strange and multistrange hadrons by CMS [49], and for π±, K±, and p(p) by ALICE [26]inp-Pb collisions at √sNN = 5.02 TeV. At high pT(>8GeV/c), the values of RpPb for all light hadrons are similar and found to be consistent with unity within the uncertainties. At intermediate pT(2 <pT< 8GeV/c), the values of RpPb for strange baryons (,)show an enhancement with a clear mass dependence [49]. In this pTregion, the hard scattering processes start to dominate over soft processes and the momentum range where this transition may happen depend on the mass and quark composition of the particle species. The measurements of strange particles produced in high multiplicity p-Pb collisions [27,50] suggested the presence of radial flow [51]. Due to the radial flow effect, hadrons of greater mass are pushed towards the higher transverse momentum and the effect increases with hadron mass as well as multiplicity [23,51]. However, it should be noted that some final state effects such as color reconnection in PYTHIA [52] which can mimic the radial flowlike effect and EPOS-LHC [31]which uses parameterized flow could describe the modification of transverse momentum spectra. The difference in the production mechanism of baryon and meson has been observed in particle ratios [23,51] and the nuclear modification factors [25,26,45,49,53]. The enhanced production of baryon (RpPb >1) may happen as a result of hadronization by parton recombination [54]. In addition, there are several initial-state effects such as isospin effect, Cronin effect, cold–nuclear matter energy loss and nuclear shadowing that can result in RpPb = 1[55]. The Cronin enhancement [56] in the intermediate pTare reported in the low-energy experiments [57,58]. Similar enhancement is observed for (anti)proton compared to pion and kaon in p-Pb collisions at √sNN =5.02 TeV [26]. In this paper, the particle species and collision energy dependence of RpPb is studied for p-Pb collisions at LHC energies. Throughout this paper, the results for K∗(892)0and K∗(892)0are averaged and denoted by the symbol K∗0, while φ(1020) is denoted by φ. The paper is organized as follows. In Sec. II, the dataset, event, and track selection criteria; the analysis techniques; the procedure for extraction of the yields; and the study of the systematic uncertainties are briefly discussed. In Sec. III, the results on the transverse momentum spectra, the dN/dy,pT,xTscaling, and RpPb in p-Pb collisions at √sNN =8.16 TeV are presented. Finally, the results are summarized in Sec. IV. II. DATA ANALYSIS The measurements of K∗0and φmeson production in p-Pb collisions at √sNN =8.16 TeV have been performed on data collected with the ALICE detector in the year 2016. The resonances are reconstructed via their hadronic decay channels with branching ratios (BR) of 66.6% for K∗0→π±K∓ and 49.2% for φ→K+K−in the rapidity interval −0.5 < y<0, where ystands for the rapidity in the nucleon-nucleon center-of-mass. For both K∗0and φ, the analysis is performed in various multiplicity classes and also using a multiplicityintegrated sample. A. Event selection The detailed description of the ALICE detector setup and its performance can be found in Refs. [59,60]. In p-Pb configurations, the 208Pb beam circulates towards the positive z direction in the ALICE laboratory frame, while the proton beam circulates in the opposite direction. Due to the asymmetric system, the center-of-mass frame is shifted in the rapidity by y=−0.465 in the direction of the proton beam with respect to the laboratory frame. The minimum bias trigger was configured to select events by requiring at least a coincidence signal in both the V0A and V0C detectors [61,62]. The V0 detector system consists of two arrays of 32 scintillator 055201-2 K∗(892)0AND φ(1020) PRODUCTION IN p-Pb COLLISIONS … PHYSICAL REVIEW C 107, 055201 (2023) TABLE I. Mean charged particle multiplicity densities (dNch/dη) measured in pseudorapidity range |ηlab|<0.5, corresponding to the various multiplicity classes defined using the V0A detector in p-Pb collisions at √sNN =8.16 TeV [62]. V0A percentile (%) dNch/dη|ηlab|<0.5 0–5 53.22 ±1.38 5–10 42.40 ±1.10 10–20 35.49 ±0.92 20–40 26.89 ±0.70 40–60 18.39 ±0.48 60–80 10.97 ±0.29 80–100 4.47 ±0.14 detectors, one on each side of the interaction point covering the full azimuthal angle in the pseudorapidity regions 2.8 < η<5.1 (V0A) and −3.7 <η<−1.7 (V0C). The background events due to beam-gas interaction and other machine-induced background collisions are rejected using the timing information from the V0 and the zero degree calorimeter (ZDC) [60]. The primary vertex of a collision is determined using charged tracks reconstructed in the inner tracking system (ITS) [63] and the time projection chamber (TPC) [64]. The events are selected whose primary vertex position along the beam axis (vz,zis the longitudinal direction) is within ±10 cm from the nominal interaction point. Pileup events from the triggered bunch crossing are rejected if multiple collision vertices are identified in the silicon pixel detector (SPD), which is the innermost detector of the ITS [60,64]. The total number of events analyzed after applying the event selection criteria is about 30 million. The minimum bias events are further divided into seven multiplicity classes, according to the total charge deposited in the forward V0A detector [61]. The yield of K∗0and φare measured in the rapidity interval −0.5 <y<0 for the following event multiplicity classes, 0–5%, 5–10%, 10–20%, 20–40%, 40–60%, 60–80%, and 80–100%. The pT spectra normalized to the fraction of non-single-diffractive (NSD) events are also obtained for both K∗0and φ. The mean charged-particle multiplicity (dNch/dη) corresponding to each multiplicity class, and measured in the pseudorapidity interval |ηlab|<0.5, is given in Table Itaken from Ref. [62]. B. Track selection and particle identification The charged tracks coming from the primary vertex are selected in the pseudorapidity interval |η|<0.8 with pT> 0.15 GeV/c. This ensures the uniform acceptance for the central barrel detectors. The high quality tracks are chosen based on selection criteria as done previously in Ref. [23]. The K∗0and φmesons are reconstructed from the charged tracks which have crossed at least 70 of a maximum 159 horizontal segments along the transverse readout plane of the TPC. The contamination from secondary particles originating from weak decays and beam background events are reduced by applying a selection on the distance of closest approach to the primary vertex in the transverse plane (DCAxy) and along the longitudinal direction (DCAz). A pT-dependent cut of DCAxy(pT)<(0.0105 +0.035p−1.1 T) cm, with pTin GeV/c, is used, which is less than 7 times its resolution. The track DCAzis required to be less than 2 cm [65]. The decay daughters (pions and kaons) of resonances are identified by measuring the specific ionization energy loss (dE/dx)inthe detector gas of the TPC and their time-of-flight information using the TOF [66]. The dE/dx resolution of the TPC is denoted as σTPC and the charged tracks are identified as pions and kaons if the mean specific energy loss measured by the TPC is within 6σTPC,3σTPC, and 2σTPC from the expected dE/dxvalues in the momentum range p<0.3 GeV/c, 0.3 <p<0.5 GeV/c, and p>0.5 GeV/c, respectively. In addition to the TPC, if the TOF information is available, then the charged tracks are identified by requiring the timeof-flight values within 3σTOF of the expected values for the full momentum range. C. Yield extraction The K∗0and φresonances are reconstructed from their decay products using the invariant mass technique. The invariant mass distributions are obtained from unlike charge πK(for K∗0) and KK (for φ) pairs in the same event. The distributions exhibit a signal peak and a large combinatorial background from the uncorrelated πK (KK) pairs. The combinatorial background is estimated using two methods, mixed-event and like-sign. In the mixed-event method, the tracks from one event are paired with oppositely charged tracks from other events. Each event is mixed with five other events to reduce the contribution of statistical uncertainty from the background distribution. The events which are mixed are selected to have similar characteristics like the longitudinal position of primary vertex (vz) must differ by less than 1 cm and the multiplicity percentiles computed using the V0A amplitude must differ by less than 5%. The mixed-event distributions for K∗0(φ) are normalized in the mass region 1.1 <minv <1.15 GeV/c2 (1.04 <minv <1.15 GeV/c2) that is approximately five σ away from the mass peak of each particle. In the like-sign method, tracks of identical charges from the same events are paired and the invariant mass distribution for the uncorrelated background is obtained as the geometric mean 2√n++ ×n−−, where n++ and n−− are the number of positive-positive and negative-negative pairs in each invariant mass bin, respectively. The mixed-event technique is the default method used for the extraction of yield both for K∗0and φ, whereas the like-sign background is used for the estimation of the systematic uncertainty. Figures 1(a) and 1(b) show the invariant mass distributions of π∓K±and K+K−pairs from the same events and the mixed-events in the transverse momentum interval 1.4 ⩽pT<1.6 and 0.6 ⩽pT<0.8 GeV/c for 0–100% in p-Pb collisions, respectively. The π∓K±and K+K−invariant mass distributions after mixed-event background subtraction are shown in Figs. 1(c) and 1(d), respectively, where the characteristic signal peak is observed on top of the residual background. The residual background arises due to correlated pairs from jets, misidentification of the decay daughters of resonances and decay of other particles [23]. The raw yields of resonances are extracted in each pTbin and multiplicity 055201-3 S. ACHARYA et al. PHYSICAL REVIEW C 107, 055201 (2023) FIG. 1. Invariant mass distributions for K∗0and φin the multiplicity class 0–100% and transverse momentum range 1.4 ⩽pT<1.6 GeV/c and 0.6 ⩽pT<0.8 GeV/c, respectively. In panels (a), (b), black markers show the unlike-sign invariant mass distributions and red markers show the normalized mixed event background. After the background subtraction the signals are shown in panels (c), (d). The K∗0peak is described by a Breit-Wigner function, whereas the φpeak is fitted with a Voigtian function. The residual background is described by the second-order polynomial function. class. The signal peak is fitted with a Breit-Wigner and a Voigtian function (convolution of Breit-Wigner and Gaussian functions) for K∗0and φ, respectively. A second-order polynomial function is used to describe the shape of the residual background for both resonances. The signal peak fit is performed in the range 0.75 <MKπ<1.15 GeV/c2(0.99 < MKK <1.07 GeV/c2)forK ∗0(φ). The widths of the K∗0and φare fixed to their PDG values (K∗0)=47.4 ±0.6 MeV/c2, (φ)=4.26 ±0.04 MeV/c2[67], whereas the resolution parameter of the Voigtian function for φis kept as a free parameter. The measured resolution of the φmass as a function of pT(σof Gaussian) varies between 1 and 3 MeV/c2.The sensitivity to the choice of the fitting range, the normalization interval, the shape of the background function, the width and resolution parameters have been studied by varying the default settings, as described in Sec. II D. In minimum bias collisions, K∗0(φ) production is measured in the pTrange from 0 to 20 GeV/c(0.4to16GeV/c). With the available data samples, K∗0production is measured up to pT=15 GeV/c in 0–5% and 5–10%, up to pT=20 GeV/c in 10–20%, 20–40%, and 40–60%, up to 10 GeV/c in 60–80% and up to 6 GeV/c in 80–100% multiplicity classes, while the φproduction is measured up to pT=16 GeV/c in 0–5%, 5–10%, 10–20%, 20–40%, pT=12 GeV/c in 40–60%, pT=10 GeV/cin 60–80%, and pT=6GeV/c in 80–100% multiplicity class. The raw transverse momentum distributions are normalized by the number of accepted events and corrected for the branching ratio, detector acceptance and reconstruction efficiency (A×rec) and, signal loss. The correction factor due to the vertex reconstruction efficiency is negligible in all multiplicity classes. The A×rec is obtained from the Monte Carlo simulation (MC) based on the DPMJET [32] event generator and the interaction of the generated particles passing through the ALICE detector geometry is modeled using GEANT3 [68]. It is defined as the ratio of the reconstructed K∗0(φ)to the generated K∗0(φ), both in the rapidity interval −0.5 <y< 0, and determined as a function of pT. The same track and particle identification (PID) selection criteria are applied to the decay daughter of resonances in MC as are used in the analysis. The shape of the generated pTdistributions are different from the measured pTdistributions, therefore a re-weighting procedure is used, in which the generated distributions are weighted to match the measured distributions. The effect of the reweighting procedure on A×rec is ≈2–5% at low pT (<1GeV/c) and negligible for pT>1GeV/c. The reweighted A×rec is used to correct the rawpTdistribution. No significant multiplicity dependence of A×rec is observed, therefore the raw pTspectra in the various multiplicity classes are corrected with the minimum bias A×rec values. The signal loss corrections that account for the loss in K∗0and φyields caused 055201-4 K∗(892)0AND φ(1020) PRODUCTION IN p-Pb COLLISIONS … PHYSICAL REVIEW C 107, 055201 (2023) TABLE II. The sources of systematic uncertainties for K∗0and φ yields in p-Pb collisions at √sNN =8.16 TeV. For each source, the average uncertainties are listed for the low and high-pTintervals. K∗0φ pT(GeV/c) Systematic variation 0.0–4.0 4.0–20.0 0.4–4.0 4.0–16.0 Yield extraction (%) 7.5 8.0 2.8 4.5 Track selection (%) 3.0 2.0 4.4 5.5 Particle identification (%) 4.3 5.0 1.9 3.5 Global tracking efficiency (%) 2.0 3.2 2.0 2.3 Material budget (%) 1.2 <0.5 2.2 <0.5 Hadronic Interaction (%) 1.9 <0.5 2.4 <1 Total (%) 9.6 10.2 6.7 8.3 by the event selection with minimum bias trigger, rather than all NSD events, are found to be negligible in the measured pTrange. The minimum bias pTspectra are normalized to the fraction of NSD events, which is 0.992. D. Systematic uncertainties The sources of systematic uncertainties of the measurement of K∗0and φproduction are signal extraction, track selection criteria, particle identification, global tracking efficiency, uncertainty in the material budget of the ALICE detector and the hadronic interaction cross-section in the detector material. A similar approach is adopted as used for the systematic uncertainty study of K∗0and φin p-Pb collisions at √sNN =5.02 TeV [23]. No multiplicity dependence of the systematic effects is observed, therefore the systematic uncertainties of minimum bias pTspectra are propagated for all multiplicity event classes studied. A summary of systematic uncertainties for K∗0(φ) in two transverse momentum intervals, 0 <pT<4GeV/c(0.4<pT<4GeV/c) and 4<pT<20 GeV/c(4<pT<16 GeV/c) are given in Table II. The uncertainties due to signal extraction include variations of the signal peak fitting range, variations of width and mass resolution, mixed-event background normalization region, choice of residual background function, and combinatorial background. The fitting range of the πK (KK) invariant mass distribution is varied by ≈50 (5) MeV/c2on each side of the signal peak. The normalization range of the πK (KK) invariant mass distributions differed by approximately 150 (50) MeV/c2with respect to the default value. The width of the resonances is fixed for the default fit whereas it is kept free for systematic studies. The residual background is fitted with a first-order and third-order polynomial function for the systematic studies of the signal extraction. For φresonance, the effect of the variation of the resolution parameter (σof the Gaussian) on the yield is also included in the systematic uncertainties. The combinatorial background from the likesign method is used for systematic studies. The contribution of systematic uncertainties due to the signal extraction is 7.5–8% for K∗0and 2.8–4.5% for φ. The systematic effects due to the charged track selection are studied by varying the criteria based on the number of crossed readout rows in the TPC and the distance of closest approach to the primary vertex of the collision [65]. The relative contribution of uncertainties due to the track selection are 2–3% for K∗0and about 4.4–5.5% for the φ. For the PID systematic uncertainty, the selections based on the TPC dE/dx and TOF time-of-flight are varied. Three variations are taken where one is a momentum dependent PID selection of 5σTPC (0 <p<0.3), 2.5σTPC (0.3 <p <0.5), 1.5σTPC (p>0.5) with 3σTOF, and two momentumindependent selection; 2σTPC with 3σTOF and 2σTPC only, for both K∗0and φ. This results in systematic uncertainties of 4.3–5% for K∗0and 1.9–3.5% for the φ. The uncertainty related to global tracking arises from the difference in the ITS-TPC track matching efficiency in data and MC. It is estimated from the single charged track uncertainty by taking the linear sum of the uncertainties of the two charged tracks which are used to reconstruct the resonances. It contributes to the systematic uncertainties with 2–3.2% and 2–2.3% for K∗0and φ, respectively. The material budget systematic effects account for the uncertainties in the estimation of the ALICE detector material budget and is estimated to be 1.2% for K∗0and 2.2% for φat low pT. It is negligible at pT >4GeV/c for both K∗0and φ. The systematic uncertainty due to the hadronic interaction cross-section in the detector material is estimated to be 1.9% for K∗0and 2.4% for φ at low pT, and negligible for pT>4GeV/c. The effects of material budget and hadronic interaction are evaluated by combining the uncertainties of the two charged tracks (π,K for K∗0and two K for φ) according to the kinematics of the decay. The systematic uncertainties of the material budget and the hadronic interaction cross-section were taken from Ref. [23]. The total systematic uncertainty is taken as the quadratic sum of all contributions and varies as 9.6–10.2% for K∗0and 6.7–8.3% for φ. The sources of systematic uncertainties that are multiplicity-dependent and uncorrelated across different multiplicity classes are also estimated. The systematic uncertainties due to signal extraction and PID are fully uncorrelated, whereas global tracking, track selection criteria, material budget and hadronic cross-section are correlated among event multiplicity classes. III. RESULTS AND DISCUSSION A. Transverse momentum spectra The measurement of K∗0(φ) production performed in the rapidity interval −0.5 <y<0uptopT=20 (16) GeV/c in p-Pb collisions at √sNN =8.16 TeV is reported. Figure 2 shows pTspectra of K∗0(left panel) and φ(right panel) for NSD events. These are compared with the predictions from EPOS-LHC [31,69], DPMJET [32], and HIJING [33] models. The bottom panels of Fig. 2show the ratios of pT spectra from these models to the data. The EPOS Monte Carlo event generator is a hadronic interaction parton model based on Gribov’s Reggeon field theory formalism which includes the feature of collective hadronization and the core-corona mechanism from pp to A-A collisions [70–72]. If the string segments of the final state parton have high-energy density, then that region is known as the “core,” whereas the region with strings of low-energy density surrounding the core 055201-5 S. ACHARYA et al. PHYSICAL REVIEW C 107, 055201 (2023) FIG. 2. Top panels: Transverse momentum spectrum of K∗0(left) and φ(right) as a function of pTfor the NSD events, measured in the rapidity interval −0.5 <y<0forp-Pb collisions at √sNN =8.16 TeV. The statistical and systematic uncertainties are shown as bars and boxes, respectively. The NSD spectrum is compared with the predictions from EPOS-LHC [31,69], DPMJET [32], and HIJING [33]. Bottom panels: The ratios of pTspectra from model to data. The shaded bands around unity describe the statistical and systematic uncertainties of the data point. is called the “corona.” The core evolves hydrodynamically and subsequently hadronizes to form the bulk of the system whereas the strings in the corona region break through the production of quark-antiquark pairs, which hadronize as fragmentation processes in vacuum. EPOS-LHC [31] is a tune of EPOS1.99 [73] that incorporates a parametrization of flow based on LHC data. The EPOS1.99 model is different from EPOS2.x [74] and EPOS3.x [69] as it does not use the complete 3D hydro calculation followed by the hadronic cascade but instead relies on the fast covariant approach. It describes various observables in minimum bias heavy-ion collisions as well as small collision systems up to a few GeV/cat LHC energies. DPMJET is a QCD-inspired dual parton model based on the Gribov-Glauber approach that treats the soft and hard scattering interaction processes differently. HIJING combines the perturbative QCD process with soft excitation, the production of multiple minijets, the interactions of jets in dense hadronic matter, and nuclear shadowing of parton distribution functions. For the K∗0resonance, at low pT(<1 GeV/c), DPMJET and HIJING models overestimate the data, whereas EPOS-LHC model gives a good description of the pTspectrum. At pT>1GeV/c, DPMJET and EPOS-LHC underestimate and closer to the data, however HIJING model underestimates (similar to the DPMJET and EPOS-LHC) for 1<pT<5GeV/c and overestimates for pT>5GeV/c. The EPOS-LHC model describes the φpTspectrum relatively better than the DPMJET and HIJING for all pT. However, HIJING model gives a good description of pTdistributon of φresonance for pT>6GeV/c. The EPOS-LHC model, where a different parametrization of flow is introduced in small collision systems like pp than the large volume produced in heavy-ion collisions, gives a better description of the transverse momentum distributions for both K∗0and φin p-Pb collisions. Figure 3shows the √sNN dependence of the transverse momentum spectra of K∗0and φfor NSD events in p-Pb collisions. The upper panels of Fig. 3show a comparison of the transverse momentum spectra of K∗0and φ at √sNN =5.02 and 8.16 TeV, whereas the lower panels show the ratio of the pT-differential yield at √sNN =8.16 to 5.02 TeV and its comparison with the results obtained from models [31–33,69]. The uncertainties of the ratios are obtained as the sum in quadrature of the uncertainties of the spectra at the two energies, which are largely uncorrelated. Up to pT1GeV/c, the differential yield ratio seems to independent of pTand collision energy. The values are consistent with unity within uncertainties. It suggests that the particle production in the soft scattering region is not strongly dependent on collision energy. The differential yield ratios increases as a function of pTfor pT1GeV/c. Similar behavior is also observed in pp collisions in Ref. [21]. The pTdifferential yield ratios 055201-6 K∗(892)0AND φ(1020) PRODUCTION IN p-Pb COLLISIONS … PHYSICAL REVIEW C 107, 055201 (2023) FIG. 3. Top panels: Energy dependence comparison of the transverse momentum spectra of K∗0(left) and φ(right) as a function of pTfor the NSD events, measured in the rapidity interval −0.5 <y<0forp-Pb collisions at √sNN =5.02 and 8.16 TeV. Bottom panels: The ratio of pTspectrum at √sNN =8.16 TeV to the pTspectrum at √sNN =5.02 TeV. The ratio is compared with the predictions from EPOS-LHC [31,69], DPMJET [32], and HIJING [33]. The statistical and systematic uncertainties are shown as bars and boxes, respectively. obtained from EPOS-LHC, DPMJET, and HIJING are consistent with the measurements within the systematic uncertainties and reproduce well the energy dependence trend for K∗0and φin p-Pb collisions. Figure 4shows the transverse momentum distributions of K∗0(left panel) and φ(right panel) in various multiplicity classes. The ratios of pTspectra in various multiplicity classes to the pTspectrum for NSD events are shown in the bottom panels of Fig. 4.ForpT4GeV/c, the slopes of the pTspectra increase from low to high multiplicity classes, whereas the spectral shapes are similar at high pT for all multiplicity classes. This indicates that processes like radial flow, which lead to a change in the shape of the pT spectra for various multiplicity classes, dominate mainly at low pT[36]. The increase in the slope of pTspectrum with multiplicity is reflected in Fig. 5for pTas a function of multiplicity. A similar behavior was also observed for K∗0and φin p-Pb collisions [23]at√sNN =5.02 TeV. The hardening of the pTspectra with charged particle multiplicity was also reported for inclusive charged hadron spectra, π,K,p,K 0 S, ,, and in pp collisions at LHC energies [20,22,34,75], where different models with multiparton interactions were shown to describe these effects. B. Integrated particle yield and mean transverse momentum The pT-integrated yields and mean transverse momentum are extracted from transverse momentum spectra in the measured range and using the fit function in the unmeasured region. The φyield is extrapolated in the unmeasured region (pT<0.4 GeV/c) by fitting a Lévy-Tsallis functions [76] to the measured pTspectra in all multiplicity classes. The difference in the yield contribution at low pTdue to different fitting functions (i.e., exponential, Boltzmann, mTexponential, Bose-Einstein and Boltzmann-Gibbs Blast-Wave function in Ref. [46]) from the Lévy-Tsallis function is included in the systematic uncertainties. The low-pTextrapolation accounts for 8.9% (14.1%) of the total yield in the 0–5% (80–100%) multiplicity class. The K∗0spectra are measured from pT=0, so low-pTextrapolation is not needed. The contribution of the extrapolated fraction of the yield is negligible for pT>20 GeV/c(16GeV/c) for K∗0(φ). The values of dN/dy and pTof K∗0and φfor various multiplicity classes are summarized in the Table III. The multiplicityscaled integrated yields [(dN/dy)/(dNch/dη|η|<0.5)] for K∗0 and φare shown in the upper panels of Fig. 5as a function of dNch/dη|η|<0.5. These results are compared with other ALICE measurements in pp collisions at √s=7 and 13 TeV [20,22], in p-Pb collisions at √sNN =5.02 TeV [23], and in Pb-Pb collisions at √sNN =2.76 and 5.02 TeV [15,18,19]. The scaled integrated yields evolve smoothly as a function of multiplicity from pp,p-Pb to Pb-Pb collisions. For similar dNch/dη|η|<0.5, these values are consistent within uncertainties for different colliding systems and at various LHC energies. This indicates that event multiplicity drives the resonance production, irrespective of the colliding systems and energies [20,22,23]. The scaled integrated yields of φshow a slight increase with multiplicity from pp collisions to mid-central Pb-Pb 055201-7 S. ACHARYA et al. PHYSICAL REVIEW C 107, 055201 (2023) FIG. 4. Top panels: The transverse momentum spectra of K∗0(left) and φ(right) for various multiplicity classes, measured in the rapidity interval −0.5<y<0forp-Pb collisions at √sNN =8.16 TeV. Bottom panels: The ratios of pTspectra of given event multiplicity classes to the NSD spectra are shown. The statistical and systematic uncertainties are shown as bars and boxes, respectively. collisions. The total increase is 12% with a 1.5σsignificance between the lowest multiplicity bin and the highest multiplicity bin in p-Pb collisions at √sNN =8.16 TeV. Similarly scaled integrated yields of K∗0show a slight decrease with multiplicity for all three collision systems and the total decrease is 12% with a 1.8σsignificance for p-Pb collisions at √sNN =8.16 TeV. The significance is calculated using statistical and multiplicity uncorrelated systematic uncertainties, added in quadrature. The integrated yield ratios of resonances relative to those of longer lived particles, π, K, and pare computed to study their production mechanism. The K∗0/K (φ/π) ratio measured in p-Pb collisions at √sNN =5.02 TeV [23] shows a decreasing (increasing) trend going from the lowest multiplicity to the highest multiplicity bin with a significance of 2.6σ(1.5σ) which is discussed in the context of a hint of a re-scattering (strangeness enhancement) effect. Future measurements of πand K yields in p-Pb collisions at √sNN =8.16 TeV will be useful to study these effects at higher center-of-mass energy and up to larger multiplicity. The model comparison with the p-Pb data shows that EPOS-LHC describes the scaled integrated yields for both K∗0and φwhereas HIJING overestimates the data for all multiplicities. The DPMJET model describes the scaled integrated yield of φat higher multiplicities but overestimates the K∗0at all multiplicities. The pTexhibits an increasing trend as a function of dNch/dη|η|<0.5for K∗0and φin various colliding systems and energies as shown in the bottom panels of Fig 5. The increase in pTis faster for pp and p-Pb than Pb-Pb and for a common multiplicity coverage the values of pTin pp and p-Pb are larger than Pb-Pb. At similar multiplicity (dNch/dη|η|<0.5≈40), the difference in pT values among Pb-Pb, p-Pb and pp collisions indicate that the geometry and dynamics of the collision systems are different, while the scaled integrated yields of K∗0and φare similar for all colliding systems and energies. This indicates that the high multiplicity event sample in small collision systems has a dominantly large fraction of harder events. Similar studies are reported in Refs. [23,77], where the moderate increase of pTin Pb-Pb collisions was related to collective flow. The strong increase of pTwith dNch/dη|η|<0.5in small collision systems can be further investigated by systematic studies of pTfrom different models in pp and p-Pb collisions that incorporate processes like color reconnection, between strings produced in multiparton interactions, different string fragmentation processes and the core-corona mechanism. It was observed in Ref. 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Zurlo141,58 (ALICE Collaboration) 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, USA 7Central China Normal University, Wuhan, China 8Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 9Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico 10Chicago State University, Chicago, Illinois, USA 11China Institute of Atomic Energy, Beijing, China 12Chungbuk National University, Cheongju, Republic of Korea 055201-18 K∗(892)0AND φ(1020) PRODUCTION IN p-Pb COLLISIONS … PHYSICAL REVIEW C 107, 055201 (2023) 13Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovakia 14COMSATS University Islamabad, Islamabad, Pakistan 15Creighton University, Omaha, Nebraska, USA 16Department of Physics, Aligarh Muslim University, Aligarh, India 17Department of Physics, Pusan National University, Pusan, Republic of Korea 18Department of Physics, Sejong University, Seoul, Republic of Korea 19Department of Physics, University of California, Berkeley, California, USA 20Department of Physics, University of Oslo, Oslo, Norway 21Department of Physics and Technology, University of Bergen, Bergen, Norway 22Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy 23Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy 24Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy 25Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy 26Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy 27Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy 28Dipartimento di Fisica e Nucleare e Teorica, Università di Pavia, Pavia, Italy 29Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy 30Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy 31Dipartimento di Scienze e Innovazione Tecnologica dell’Università del Piemonte Orientale and INFN Sezione di Torino, Alessandria, Italy 32Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy 33Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy 34European Organization for Nuclear Research (CERN), Geneva, Switzerland 35Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia 36Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway 37Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 38Faculty of Science, P.J. Šafárik University, Košice, Slovakia 39Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 40Fudan University, Shanghai, China 41Gangneung-Wonju National University, Gangneung, Republic of Korea 42Gauhati University, Department of Physics, Guwahati, India 43Helmholtz-Institut für Strahlenund Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany 44Helsinki Institute of Physics (HIP), Helsinki, Finland 45High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico 46Hiroshima University, Hiroshima, Japan 47Hochschule Worms, Zentrum für Technologietransfer und Telekommunikation (ZTT), Worms, Germany 48Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 49Indian Institute of Technology Bombay (IIT), Mumbai, India 50Indian Institute of Technology Indore, Indore, India 51Indonesian Institute of Sciences, Jakarta, Indonesia 52INFN, Laboratori Nazionali di Frascati, Frascati, Italy 53INFN, Sezione di Bari, Bari, Italy 54INFN, Sezione di Bologna, Bologna, Italy 55INFN, Sezione di Cagliari, Cagliari, Italy 56INFN, Sezione di Catania, Catania, Italy 57INFN, Sezione di Padova, Padova, Italy 58INFN, Sezione di Pavia, Pavia, Italy 59INFN, Sezione di Roma, Rome, Italy 60INFN, Sezione di Torino, Turin, Italy 61INFN, Sezione di Trieste, Trieste, Italy 62Inha University, Incheon, Republic of Korea 63Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University/Nikhef, Utrecht, Netherlands 64Institute for Nuclear Research, Academy of Sciences, Moscow, Russia 65Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovakia 66Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 67Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 68Institute of Space Science (ISS), Bucharest, Romania 69Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 70Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico 71Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 055201-19 S. ACHARYA et al. PHYSICAL REVIEW C 107, 055201 (2023) 72Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 73iThemba LABS, National Research Foundation, Somerset West, South Africa 74Jeonbuk National University, Jeonju, Republic of Korea 75Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 76Joint Institute for Nuclear Research (JINR), Dubna, Russia 77Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 78KTO Karatay University, Konya, Turkey 79Laboratoire de Physique des 2 Infinis, Irène Joliot-Curie, Orsay, France 80Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 81Lawrence Berkeley National Laboratory, Berkeley, California, USA 82Lund University Department of Physics, Division of Particle Physics, Lund, Sweden 83Moscow Institute for Physics and Technology, Moscow, Russia 84Nagasaki Institute of Applied Science, Nagasaki, Japan 85Nara Women’s University (NWU), Nara, Japan 86National and Kapodistrian University of Athens, School of Science, Department of Physics, Athens, Greece 87National Centre for Nuclear Research, Warsaw, Poland 88National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India 89National Nuclear Research Center, Baku, Azerbaijan 90National Research Centre Kurchatov Institute, Moscow, Russia 91Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 92Nikhef, National institute for subatomic physics, Amsterdam, Netherlands 93NRC Kurchatov Institute IHEP, Protvino, Russia 94NRC KurchatovInstitute - ITEP, Moscow, Russia 95NRNU Moscow Engineering Physics Institute, Moscow, Russia 96Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom 97Nuclear Physics Institute of the Czech Academy of Sciences, ˇ Rež u Prahy, Czech Republic 98Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA 99Ohio State University, Columbus, Ohio, USA 100Petersburg Nuclear Physics Institute, Gatchina, Russia 101Physics department, Faculty of science, University of Zagreb, Zagreb, Croatia 102Physics Department, Panjab University, Chandigarh, India 103Physics Department, University of Jammu, Jammu, India 104Physics Department, University of Rajasthan, Jaipur, India 105Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen, Germany 106Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 107Physik Department, Technische Universität München, Munich, Germany 108Politecnico di Bari and Sezione INFN, Bari, Italy 109Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany 110Russian Federal Nuclear Center (VNIIEF), Sarov, Russia 111Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India 112School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 113Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru 114St. Petersburg State University, St. Petersburg, Russia 115Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria 116SUBATECH, IMT Atlantique, Université de Nantes, CNRS-IN2P3, Nantes, France 117Suranaree University of Technology, Nakhon Ratchasima, Thailand 118Technical University of Košice, Košice, Slovakia 119The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 120The University of Texas at Austin, Austin, Texas, USA 121Universidad Autónoma de Sinaloa, Culiacán, Mexico 122Universidade de São Paulo (USP), São Paulo, Brazil 123Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 124Universidade Federal do ABC, Santo Andre, Brazil 125University of Cape Town, Cape Town, South Africa 126University of Houston, Houston, Texas, USA 127University of Jyväskylä, Jyväskylä, Finland 128University of Kansas, Lawrence, Kansas, USA 129University of Liverpool, Liverpool, United Kingdom 130University of Science and Technology of China, Hefei, China 055201-20 K∗(892)0AND φ(1020) PRODUCTION IN p-Pb COLLISIONS … PHYSICAL REVIEW C 107, 055201 (2023) 131University of South-Eastern Norway, Tonsberg, Norway 132University of Tennessee, Knoxville, Tennessee, USA 133University of the Witwatersrand, Johannesburg, South Africa 134University of Tokyo, Tokyo, Japan 135University of Tsukuba, Tsukuba, Japan 136Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 137Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de Lyon, Lyon, France 138Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France 139Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire (DPhN), Saclay, France 140Università degli Studi di Foggia, Foggia, Italy 141Università di Brescia, Brescia, Italy 142Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India 143Warsaw University of Technology, Warsaw, Poland 144Wayne State University, Detroit, Michigan, USA 145Westfälische Wilhelms-Universität Münster, Institut für Kernphysik, Münster, Germany 146Wigner Research Centre for Physics, Budapest, Hungary 147Yale University, New Haven, Connecticut, USA 148Yonsei University, Seoul, Republic of Korea aAlso at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy. bDeceased. cAlso at: Dipartimento DET del Politecnico di Torino, Turin, Italy. dAlso at: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear, Physics, Moscow, Russia. eAlso at: Department of Applied Physics, Aligarh Muslim University, Aligarh, India. fAlso at: Institute of Theoretical Physics, University of Wroclaw, Poland. gAlso at: University of Kansas, Lawrence, Kansas, USA. 055201-21