Production of K∗ (892) 0 and ϕ(1020) in p–Pb collisions at √sNN = 5.02 TeV

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Production of K (892) 0 and ϕ(1020) in p–Pb collisions at √sNN = 5.02 TeV ALICE Collaboration ALICE Collaboration. (2016). Production of K (892) 0 and ϕ(1020) in p–Pb collisions at √sNN = 5.02 TeV. European Physical Journal C, 76(5), Article 245. https://doi.org/10.1140/epjc/s10052-016-4088-7 2016 Eur. Phys. J. C (2016) 76:245 DOI 10.1140/epjc/s10052-016-4088-7 Regular Article - Experimental Physics Production of K∗(892)0and φ(1020) in p–Pb collisions at √sNN =5.02TeV ALICE Collaboration CERN, 1211 Geneva 23, Switzerland Received: 1 February 2016 / Accepted: 14 April 2016 © CERN for the benefit of the ALICE collaboration 2016. This article is published with open access at Springerlink.com Abstract TheproductionofK∗(892)0andφ(1020)mesons has been measured in p–Pb collisions at √sNN =5.02 TeV. K∗0and φare reconstructed via their decay into charged hadrons with the ALICE detector in the rapidity range −0.5<y<0. The transverse momentum spectra, measured as a function of the multiplicity, have a pTrange from 0to15GeV/cfor K∗0and from 0.3 to 21 GeV/cfor φ. Integrated yields, mean transverse momenta and particle ratios are reported and compared with results in pp collisions at √s =7 TeV and Pb–Pb collisions at √sNN =2.76 TeV. In Pb–Pb and p–Pb collisions, K∗0and φprobe the hadronic phase of the system and contribute to the study of particle formation mechanisms by comparison with other identified hadrons. For this purpose, the mean transverse momenta and the differential proton-to-φratio are discussed as a function of the multiplicity of the event. The short-lived K∗0is measured to investigate re-scattering effects, believed to be related to the size of the system and to the lifetime of the hadronic phase. 1 Introduction The phase transition predicted by QCD from ordinary matter to a deconfined quark–gluon plasma (QGP) has been studied in high-energy heavy-ion collision (AA) experiments at the super proton synchrotron (SPS) [1–11], the relativistic heavy-ion collider (RHIC) [12–15] and the large hadron collider (LHC) [16–22]. In this context, hadronic resonances provide an important contribution to the study of particle production mechanisms and the characterisation of the dynamic evolution of the system formed in heavy-ion collisions, during the late hadronic phase. Results on resonance production in different collision systems at RHIC have been reported in See Acknowledgments section for the list of collaboration members. e-mail: [email protected] [23–29]. At the LHC, K∗(892)0andφ(1020) production have been measured in pp collisions at √s=7 TeV by ALICE [30], ATLAS [31] and LHCb [32], and in pp and Pb–Pb collisions at √sNN =2.76 TeV by ALICE [33,34]. Results obtained in p–Pb collisions at √sNN =5.02 TeV with the ALICE detector are presented in this paper. Measurements in smaller collision systems such as proton–proton (pp) and proton–nucleus (pA) constitute a reference for the interpretation of the heavy-ion results. In addition, proton–nucleus collisions have proven to be interesting in their own right, as several measurements [35–39] indicate that they cannot be explained by an incoherent superposition of pp collisions, but suggest instead the presence of collective effects [40,41]. In heavy-ion collisions, the presence of a strong collective radial flow reveals itself in the evolution with centrality of the transverse momentum spectra of identified hadrons [42]. The spectral shapes of K∗0and φfollow the common behaviour found for all the other particles and exhibit an increase of the mean transverse momentum, dominated by the low pTregion of the spectra where particle production is more abundant, with centrality [33]. In central Pb–Pb events, particles with similar mass such as the φmeson and the proton have similar pTand, in addition, the φ/p ratio as a function of pTis flat for pT<4GeV/c. Both observations are consistent with expectations from hydrodynamic models, where the mass of the particle drives the particle spectral shapes at low momenta [43]. On the other hand, in most peripheral Pb–Pb collisions, as well as in pp, the φ/p ratio exhibits a strong pTdependence,suggesting thatthe productionof lowand intermediate-momentum baryons and mesons occurs by means of other mechanisms such as fragmentation or recombination [44,45]. Similarly to Pb–Pb, one is interested in searching for collective effects in p–Pb collisions and in studying particle production as a function of the hadron multiplicity, which strongly depends on the geometry of the collision. In this respect, p–Pb collisions provide us with a system whose size in terms of average charged-particle density and num123 245 Page 2 of 21 Eur. Phys. J. C (2016) 76:245 ber of participating nucleons is intermediate between pp and peripheral Pb–Pb collisions [18,46–49]. Measurements in an intermediate-size system as p–Pb can provide information on the onset of the collective behaviour leading to the presence of radial flow. The φmeson, with similar mass to that of the proton and rather long lifetime (τφ=46.3 ±0.4 fm/c[50]) compared to that of the fireball, is an ideal candidate for such study. The yields of short-lived resonances such as the K∗0(τK∗0= 4.16 ±0.05 fm/c[50]) instead may be influenced by interactionsduring thehadronic phase:the re-scatteringofthe decay products in the fireball may prevent the detection of a fraction of the resonances, whereas pseudo-elastic hadron scattering can regenerate them. The effects of re-scattering and regeneration depend on the scattering cross section, the particle density, the particle lifetime and the timespan between chemical and kinetic freeze-out, namely the lifetime of the hadronic phase. Therefore, the observation of re-scattering effects would imply the presence of an extended hadronic phase. The latter can be studied by comparing particles with different lifetimes, such as the K∗0resonance and the φmeson, which has a ten times longer lifetime. ALICE has observed [33] that in most central Pb–Pb collisions at the LHC the K∗0/Kratioissignificantlysuppressedwithrespecttoperipheral Pb–Pb collisions, pp collisions and the value predicted by a statistical hadronisation model [51]. This is interpreted as a scenario where re-scattering during the hadronic phase, dominating for low-momentum resonances (pT<2GeV/c) [52,53], reduces the measurable yield of K∗0. No suppression is observed instead for the ten times longer-lived φ, sinceit decaysmainlyafterkinetic freeze-out.Basedon these observations, a lower limit of 2 fm/con the lifetime of the hadronic phase in 0–20 % most central Pb–Pb events could be estimated [33]. The K∗0suppression exhibits a monotonic trend with centrality, suggesting a dependence on the volume of the particle source at the kinetic freeze-out. A similar measurement of resonance production as a function of the system size in p–Pb can provide information as regards the lifetime of the hadronic fireball produced in such a smaller system. The K∗0and φmesons are reconstructed using the ALICE detector in p–Pb collisions at √sNN =5.02 TeV. Their yields, mean transverse momenta and ratios to identified long-livedhadronsinp–Pbcollisionsarestudiedasafunction of the system size or the multiplicity of the event, and compared with pp and Pb–Pb. The experimental conditions are briefly presented in Sect. 2. Section 3illustrates the analysis procedure, including event and track selection, signal extraction, efficiency correction and systematic uncertainties. The results are presented in Sect. 4and in Sect. 5the conclusions are summarised. 2 Experimental setup A complete description of the ALICE detector and its performance during the LHC Run I are reported in [54,55], respectively. The analyses presented in this paper have been carried out on a sample of p–Pb collision events at √sNN =5.02 TeV collected in 2013. The LHC configuration was such that the lead beam, with energy of 1.58 TeV per nucleon, was circulating in the counter-clockwise direction, namely towards the ALICE “A” side (positive rapidity direction), while the 4 TeV proton beam was circulating in the clockwise direction, towards the ALICE muon spectrometer, or “C” side. According to this convention for the sign of the coordinates, the nucleon–nucleon center-of-mass system was moving in the laboratory frame with a rapidity of yNN =−0.465 in the direction of the proton beam. In the following, ylab (ηlab) are used to indicate the (pseudo) rapidity in the laboratory reference frame, whereas y(η) denotes the (pseudo) rapidity in the nucleon–nucleon center-of-mass reference system. For the results presented in this paper, a low-luminosity data sample has been analysed, consisting of events collected at an hadronic interaction rate of about 10 kHz. The interaction region had a root mean square of 6.3 cm along the beam direction and of about 60 µm in the direction transverse to the beam. The event pile-up rate has been estimated to have negligible effects on the results of this analysis. In particular, pile-up of collisions from different bunch crossings is negligible due to the 200 ns bunch-crossing spacing, larger than the integration time of the zero-degree calorimeter (ZDC), while a small fraction of in-bunch pile-up events is removed by the offline analysis, as described in the next section. Small acceptance forward detectors (V0, T0, and ZDC) are used for triggering, event characterisation, and multiplicity studies. The trigger is provided by two arrays of 32 scintillator detectors, V0A and V0C, that cover the full azimuthal angle in the pseudo-rapidity regions 2.8 <η lab <5.1 (Pbgoing direction) and −3.7 <η lab <−1.7 (p-going direction), respectively. V0 information is also used to classify events in multiplicity classes (see Sect. 2.1). The two quartz Cherenkov detectors T0A (4.6 <η lab <4.9) and T0C (−3.3 <η lab <−3) deliver the time and the longitudinal position of the interaction. The zero-degree calorimeters (ZDC), consisting of two tungsten–quartz neutron and two brass–quartz protoncalorimetersplacedsymmetricallyat adistanceof113 m from the interaction point, on both sides, are used to reject background and to count spectator nucleons. The reconstruction of the primary vertex of the collision and the tracking of particles in the ALICE central barrel is provided by the inner tracking system (ITS) and the time-projection chamber (TPC), in the pseudo-rapidity range 123 Eur. Phys. J. C (2016) 76:245 Page 3 of 21 245 |ηlab|<0.9 and the full azimuthal angle. The ITS is a siliconbased detector, constituted by two innermost pixels layers (SPD), two intermediate drift (SDD) and two outer strip layers (SSD), with radii between 3.9 and 43 cm from the beam axis. The ALICE main tracker, the TPC, is a 90 m3cylindrical drift chamber filled with Ne-CO2gas and divided in two parts by a central cathode. The end plates are equipped with multi-wire proportional chambers whose readout cathode pads allow one to sample particle tracks up to 159 points (clusters). In addition to tracking, the TPC allows particle identificationvia thespecific ionisationenergylossdE/dxin the gas. The time-of-flight (TOF) detector, a large Multigap resistive plate chamber (MRPC) array covering |η|<0.9 and the full azimuthal angle, allows for particle identification at intermediate momenta and has been exploited together with the TPC for the analysis presented in this paper (see Sect. 3.1). 2.1 Event selection The minimum bias trigger during p–Pb data taking was configured to select hadronic events with high efficiency, by requiring a signal in either V0A or V0C. The resulting sample contains single-diffractive (SD), non-single-diffractive (NSD) and electromagnetic (EM) events. Diffractive interactions are described in Regge theory by the exchange of a colour singlet object with the quantum numbers of the vacuum (pomeron). In SD events one of the two nucleons breaks up producing particles in a limited rapidity interval. NSD events include double-diffractive interactions, where both nucleons break up by producing particles separated by a largerapidity gap,and other inelasticinteractions.Theoffline analysisselectseventshavingacoincidenceofsignals in both V0A and V0C in order to reduce the contamination from SD and EM events to a negligible amount. The trigger and eventselection efficiency for NSD events is estimated as NSD = 99.2 % using a combination of Monte-Carlo event generators, as described in [48,49]. The arrival time of signals on the V0 and the ZDC is required to be compatible with a nominal p–Pb collision occurring close to the nominal interaction point, to ensure the rejection of beam-gas and other machine-induced background collisions. The primary vertex of the collision is determined using tracks reconstructed in the TPC and ITS. In case of low multiplicity events only the information from the SPD is used to reconstructthe vertex,as describedindetail in[55].98.5 %of all events have a primary vertex. Minimum bias events with the primary vertex positioned along the beam axis within 10 cm from the center of the ALICE detector are selected offline. A small fraction (0.2 %) of pile-up events from the same bunch crossing has been removed from the sample by rejecting events with multiple vertices. Events are accepted if the vertices separately measured by the SPD and using tracks are within 0.5 cm, and if the SPD vertex is determined by at least five track segments defined by one hit in each one of the two layers of the detector. After the trigger and offline event-selection criteria, the sample used for this analysis counts about 108events, corresponding to an integrated luminosity of about 50 µb−1. The minimum bias sample has been further divided in several event classes based on the charged-particle multiplicity, estimated using the total charge deposited in the V0A detector positioned along the direction of the Pb beam. The yield of K∗0is measured in five multiplicity classes, namely 0– 20, 20–40, 40–60, 60–80 and 80–100 %. In case of φseven classes, namely 0–5, 5–10, 10–20, 20–40, 40–60, 60–80 and 80–100 % are used. In addition, minimum bias spectra normalised to the fraction of NSD events are measured for both particles. In order to study the dependence of particle production on the geometry of the collision, the V0A estimator for the charged particle multiplicity has been used to determine centrality, by following the approach based on the Glauber Monte Carlo model combined with a simple model for particle production [56,57], a strategy customarily employed in heavy-ion collisions [58]. The average number of binary collisions Ncoll(related to the number of participant nucleons Npart by the simple relation Ncoll =Npart −1), obtained with this method for each centrality class, are listed in Table 1for future reference, together with the mean chargedparticle multiplicity density, dNch/dηlab|η|<0.5[47,48], here corrected for trigger and vertex-reconstruction inefficiency, which is about 5.5 % in the lowest multiplicity event class. In addition, the average Ncoll has been determined with an hybrid method that uses the ZDC to classify the events Table 1 Average charged-particle pseudo-rapidity density, dNch/dηlab|η|<0.5, measured at mid-rapidity in visible cross section event classes and average number of colliding nucleons, Ncoll. Multiplicity classes are defined using the V0A estimator [48,49], as described in the text. Total systematic uncertainties are reported, see [49] for details, which do not include the difference with respect to the other methods used to estimate the average Ncoll. For minimum bias collisions, dNch/dηlab=16.81 ±0.71 and Ncoll=6.87 ±0.5 Multiplicity class (%) dNch/dηlab|η|<0.5Ncoll 0–5 45 ±1 14.8 ±1.5 5–10 36.2 ±0.8 13.0 ±1.3 10–20 30.5 ±0.7 11.7 ±1.2 0–20 35.6 ±0.8 12.8 ±1.3 20–40 23.2 ±0.5 9.36 ±0.84 40–60 16.1 ±0.4 6.42 ±0.46 60–80 9.8 ±0.2 3.81 ±0.76 80–100 4.16 ±0.09 1.94 ±0.45 123 245 Page 4 of 21 Eur. Phys. J. C (2016) 76:245 according to the energy deposited by the neutrons emitted in the Pb-going direction (by evaporation or fragmentation) or the energy measured with the ZDC in the Pb-going direction and the assumption that the charged-particle multiplicity measured at mid-rapidity is proportional to the number of participant nucleons. This method was shown [49] to avoid possible bias in the event sample related to the fact that the rangeofmultiplicities usedtoselectagivenclassin p–Pbcollisions is of similar magnitude to the fluctuations on the same quantity. The variations of the average Ncoll for a given multiplicity class, obtained with different methods are found not to exceed 6 % in any of the used classes. 3 Resonance signal reconstruction K∗(892)0and φ(1020) mesons are reconstructed through their decay into charged hadrons, K∗0→K+π−and K∗0→K−π+,B.R.=0.666, and φ→K+K−,B.R.= 0.489 [50]. Since K∗(892)0and K∗(892)0are expected to be produced in equal amounts, as measured in lower energy experiments [59], for this measurement the yields of particle and anti-particle are combined in order to improve statistics. The average (K∗(892)0+ K∗(892)0)/2 is indicated as K∗0in the following. The φ(1020) meson is indicated as φ. For these measurements, the reconstructed K∗0and φare selected in the rapidity range −0.5 <y<0, in order to ensure the best detector acceptance as the center of mass of the nucleon–nucleon system was moving with respect to the beam interaction point. 3.1 Track selection and particle identification The charged tracks coming from the primary vertex of the collision (“primary” tracks) with pT>0.15 GeV/cand |ηlab| <0.8 are considered for the invariant-mass reconstruction of K∗0and φin this analysis. The selection of primary tracks imposestherequirementthattheysatisfygoodreconstruction quality criteria. It is required that tracks have left a signal in at least one of the layers of the SPD and that the distance of closest approach to the primary vertex of the collisions is lower than 7σxy in the transverse plane and within 2 cm along the longitudinal direction. The resolution on the distance of closest approach in the transverse plane, σxy, is strongly pTdependent and lower than 100 µmforpT>0.5 GeV/c[55]. In addition tracks are required to cross at least 70 out of maximum 159 horizontal segments (or “rows”) along the transverse readout plane of the TPC. Primary tracks have been identified as πor K based on the information of the TPC and TOF detectors. In the TPC, charged hadrons are identified by measuring the specific ionisation energy loss (dE/dx) in the detector gas. With a resolution (σTPC)ondE/dxof 6 %, the TPC allows a 2σTPC separation between πand K up to pT∼0.8 GeV/cand above 3GeV/c, in the relativistic rise region of the dE/dx.The TOF contributes to particle identification with the measurement of the time-of-flight of the particle, with the start time of the event measured by the T0 detector or using an algorithm which combines the particle arrival times at the TOF surface. In p–Pb collisions, when the event time is determined by the TOF algorithm (available for 100 % of the events which have more than three tracks) the resolution is 80 <σ TOF <100 ps. TOF allows a 2σTOF separation between identified πand K in the momentum range 0.7–3 GeV/c, and between K and protons up to 5 GeV/c[60]. For the combined “TPC-TOF PID” approach, particles with a signal in the TOF are identified by requiring that the measured time-of-flight and energy loss do not deviate from the expected values for each given mass hypothesis by more than 2σTOF and 5σTPC, respectively. For tracks which do not hit the TOF active region, a 2σTPC selection on the dE/dxis applied. Variations of these cuts have been used for systematic studies, as described in Sect. 3.4. Besides the TPC-TOF, the measurement of φhas been performed following two alternative strategies, one which exploits a 2σTPC separation on the particle energy loss in the TPC for the K identification, and the second for which no PID cuts are applied. In the noPID scheme all positively charged hadrons are considered as K+whereas all negatively charged hadrons are considered as K−. The no-PID approach extends the measurement of the yields from pT=10 GeV/c, the upper limit reached by the PID analysis, to 16 GeV/c(multiplicity dependent) or 21 GeV/c(minimum bias). At low pT, the TPC-TOF selection leads to a better separation between signal and background withrespecttoTPC-onlyandno-PID,thereforeitisuseduntil pT(φ)cutoff =3GeV/c. At high momentum, K and πcannot be efficiently separated by TPC-TOF, therefore no-PID is used for pT(φ)>3GeV/cto maximise the total reconstruction efficiency. The multiplicity-integrated yields of φ(see Sect. 4) obtained with the no-PID, TPC only, and TPC-TOF approaches are compared in Fig. 1a in the common transverse momentum interval. Details of the signal extraction procedure and efficiency correction are given, respectively, in Sects. 3.2 and 3.3. The ratio of the data to the Lévy–Tsallis function (see Sect. 4.1) used to fit the TPC-TOF spectrum in the 0.3 <pT<10 GeV/crange (Fig. 1b) further shows good agreement among the three analyses, within uncertainties. In the case of K∗0, which is a wide resonance, PID is necessary also at high momentum to reduce the background and therefore the TPC-TOF strategy has been applied in the full kinematic range. 3.2 Signal extraction K∗0and φsignals are reconstructed in each multiplicity class and transverse momentum interval, as described in [30,33]. 123 Eur. Phys. J. C (2016) 76:245 Page 5 of 21 245 ] -1 )c) [(GeV/yd T p/(dN 2 d evt 1/N 4− 10 3− 10 2− 10 1− 10 (a) PID strategy TPC TPC-TOF No PID vy-TsalliseL = 5.02 TeV, NSD NN sALICE, p-Pb < 0y-0.5 < 2 +sys. 2 stat.Uncertainties: (1020)φ )c (GeV/ T p 012345678910 Data/Fit 0.5 1 1.5 (b) Fig. 1 a Comparison of the transverse momentum spectrum d2N/(dpTdy)ofφ-mesoninnon-single-diffractive(NSD)p–Pbevents, reconstructed via the decay channel into K+K−by exploiting three different strategies for K identification: TPC only, TPC-TOF and no-PID. The reader can refer to Sect. 3.1 for details on the PID selection and to Sect. 3.2 for a description of the signal extraction procedure. The uncertainties are the sum in quadrature of statistical and systematic. A Lévy–Tsallis function (see Eq. 1) is used to fit the TPC-TOF spectrum in 0.3 <pT<10 GeV/c.bRatio of each spectrum to the fit function, showing good agreement of the three PID strategies within uncertainties For each event, the invariant-mass distribution of the K∗0(φ) is constructed using all unlike-sign combinations of charged K candidates with π(K) candidates. For K∗0in the full momentum range and for φup to 3 GeV/cthe TPC-TOF approach has been used for particle identification. φmesons with pT>3GeV/chave been reconstructed by applying no PID. In the following the K+and π+candidates are labelled by h+,theK −and π−are labelled by h−. The combinatorial background due to the uncorrelated pairs has been estimated in two ways, by the mixed-event technique and from the invariant-mass distribution of like-sign pairs from the same event. In the event-mixing method the shape of the uncorrelated background is estimated from the invariant-mass distribution of h+h−combinations from five different events. Effectsfrommultiplicityfluctuationsareminimisedbydividingthesampleinto tenmultiplicityclassesandbyperforming event mixing within the same multiplicity class. In order to minimise distortions due to acceptance effects within each multiplicity class, the events are further sub-divided into 20 bins according to the relative vertex position along the zaxis (zv=1 cm). The final mixed-event distribution for each multiplicity class is found by adding up the Minv distributions from each vertex zvinterval. For the K∗0analysis, the mixed-event distribution for each pTbin is normalised by the smallest factor that leads to a positive-defined unlike-sign distribution after subtraction, within the statistical error in all invariant-mass bins. The mixed-event distribution for φis normalised in the mass region 1.04 <MKK <1.06 GeV/c2. The normalisation range for K∗0and φis varied for systematicstudies.Inthelike-signtechnique,theinvariant-massdistributionfortheuncorrelatedbackgroundisobtainedbycombiningthe h+h+andh−h−pairs fromthesameeventaccording to a geometric mean (2(h+h+)·(h−h−)), in order to reduce statistical fluctuations in the resulting distribution. The like-sign background is subtracted without normalisation from the unlike-sign pairs distribution. The mixed-event methodhas beenpreferredfor K∗0(φ)signal extractionin the range 0.4 <pT<15 GeV/c(0.3 <pT<16 GeV/c), given the smaller statistical uncertainties on the invariant-mass distribution. At very low momentum, pT<0.4 GeV/c, the likesign distribution is found to reproduce better the background of the K∗0and not to be affected by the choice of the normalisation range, therefore it has been preferred over the mixed event. Figure 2shows the MKπand MKK invariantmass distributions before and after background subtraction in the transverse momentum interval 1.2 ≤pT<1.4 GeV/c, for the 0–20 and 0–5 % V0A multiplicity classes, for K∗0and φ, respectively. After background subtraction, the resulting distributions exhibit a characteristic peak on top of a residual background (lower panels of Fig. 2). The latter is only partly due to imperfections in the description of the combinatorial background and mainly due to correlated pairs from jets, multi-body decay of heavier particles or correlated pairs contribution to the background from real resonance decays where the daughterparticles aremisidentified asK orπby theTPC-TOFPID. A dedicated study in Monte Carlo simulations has been performed to ensure that the shape of the correlated background is a smooth function of mass and to verify that a second-order polynomial provides a good description of it. Asin[30],thesignalpeaks for K∗0andφarefitted,respectively, with a (non-relativistic) Breit–Wigner and a Voigtian function (convolution of Breit–Wigner and Gaussian) superimposed to a second-order polynomial function to shape the residualbackground.Examplesare reportedinthelower panelsofFig.2, where fits are performed in the intervals 0.76 <MKπ<1.04 GeV/c2and 1.0 <MKK <1.05 GeV/c2.The fitting range is optimised for each pTbin across all multiplicity event classes. The mass and width of K∗0and φare found to be compatible with the measurements in Pb–Pb collisions [33]. For the measurement of the yields, the width of K∗0and φhave been fixed to their natural values, (K∗0)=47.4 ± 0.6 MeV/c2,(φ) = 4.26 ±0.04 MeV/c2[50], whereas the resolution parameter of the Voigtian function for φhas been kept as a free parameter. The measured pT-dependent resolution on the φmass (sigma of Gaussian) varies between 0.9 and 1.5 MeV/c2, and it is consistent with the values extracted from Monte Carlo simulation. The sensitivity to the choice 123 245 Page 6 of 21 Eur. Phys. J. C (2016) 76:245 2 c Counts / 10 MeV/ 80 90 100 110 120 130 140 150 Unlike-sign pairs Mixed event background (MEB) = 5.02 TeV (0-20%) NN sALICE, p-Pb c < 1.4 GeV/ T p≤ < 0, 1.2 y-0.5 < 4 10× (a) ) 2 c (GeV/ πK M 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 2 c Counts / 10 MeV/ 0 10 20 30 40 50 60 70 80 90 Data (MEB subtracted) Breit-Wigner peak fit Residual background 3 10× 0 K*(892) (b) 2 cCounts / 1 MeV/ 0 1 2 3 4 5 6 Unlike-sign pairs Mixed event background (MEB) = 5.02 TeV (0-5%) NN sALICE, p-Pb c < 1.4 GeV/ T p≤ < 0, 1.2 y-0.5 < 3 10× (c) ) 2 c (GeV/ KK M 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 2 cCounts / 1 MeV/ 0 0.5 1 1.5 2 2.5 Data (MEB subtracted) Voigtian peak fit Residual background 3 10× (1020)φ (d) Fig. 2 Invariant-mass distributions for K∗0(a,b)andφ(c,d)inthe transverse momentum range 1.2 ≤pT<1.4 GeV/cand multiplicity classes 0–20 and 0–5 %, respectively. Upper panels a,c, report the unlike-sign invariant-mass distribution and the mixed-event background (MEB) normalised as described in the text. In lower panels b,c, the distributions after background subtraction are shown. The K∗0peak is fitted with a Breit–Wigner function whereas the φmeson peak is described by a Voigtian function. A second-order polynomial function is used to describe the residual background of the normalisation interval, the fitting range, the shape of the background function, the fitting range and the constraints on mass, width and resolution parameters has been studied by varying the default settings, as described in Sect. 3.4. In minimum bias collisions the sample of reconstructed particles includes about 3.4×106K∗0and 8.6×105φin the transverse momentum range 0 <pT(K∗0)<15 GeV/cand 0.3 <pT(φ)<21 GeV/c, respectively. With the available statistics, the K∗0production in the 80–100% V0A multiplicity event class has been measured up to pT=6GeV/c, while the φspectra extend up to 16 GeV/cin the 0–60 % multiplicity percentile interval and up to 13 GeV/cin 60–80 and 80–100 %. 3.3 Detector acceptance and efficiency In order to evaluate the detector acceptance and reconstruction efficiency, a sample of about 108Monte Carlo simulated p–Pb events, based on the DPMJET 3.05 event generator [61], with the detector geometry and material budget modelled by GEANT 3.21 [62], has been analysed. The acceptance and efficiency correction is determined as the fraction of generated resonances in the rapidity interval −0.5 <y<0 that have been reconstructed. The reconstructed signal pairs are obtained upon combination of primary πand K selected by applying the same kinematics cuts and track cuts as in the data (see Sect. 3.1), including TPC-TOF PID cuts for K∗0, and φwith pT<3GeV/c.Forφwith pT>3GeV/cno PID cuts are applied. The acceptance and efficiency corrections, Acc ×,forK ∗0and φare reported in Fig. 3as a function of pTfor minimum bias events. Since only events with reconstructed primary vertex have been considered in the computation of (Acc ×)(pT), a correction factor has to beapplied tothe totalnumberof acceptedeventsin eachV0A multiplicity event class, to account for vertex reconstruction inefficiency. The correction is about 0.995 for 60–80 % class and 0.945 for the lowest multiplicity events 80–100 %, and it is applied as discussed in Sect. 4.1. 123 Eur. Phys. J. C (2016) 76:245 Page 7 of 21 245 )c (GeV/ T p 0 2 4 6 8 101214161820 Efficiency×Acceptance 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 + π - + K - π + K→ 0 K* + 0 K* c > 3 GeV/ T p, No-PID - +K + K→φ = 5.02 TeV (0-100%) NN sALICE, simulated p-Pb TPC-TOF PID Fig. 3 Detector acceptance and signal reconstruction efficiency for K∗0and φmesons, which includes reconstruction, track selection and particle identification efficiency. For K∗0and φproduction below pT<3GeV/c, the PID efficiency is relative to the TPC-TOF approach, whereas for φproduction with pT>3GeV/cno PID contribution is included, as no particle identification is applied in the analysis 3.4 Systematic uncertainties The measurement of K∗0and φproduction in p–Pb collisions have been tested for systematic effects due to global tracking efficiency, track selection cuts, PID, signal extraction, knowledge of the material budget and of the hadronic interaction cross section in the detector material, as summarised in Table 2. The approach is similar to the one adopted for the study of K∗0and φin Pb–Pb collisions [33], but the total averageuncertaintyevaluatedinthep–Pb caseissignificantly lower (about half of the relative uncertainty in the Pb–Pb), mainly due to lower contributions from global tracking efficiency and the signal extraction procedure. No multiplicity dependence of systematic effects has been observed, therefore the uncertainties presented in Sect. 2have been averaged among all multiplicity event classes. For each particle, they arequotedfortwoseparatemomentumintervals:forK∗0,one can distinguish a low-pTrange (0 <pT(K∗0)<4GeV/c) where the knowledge of the material budget and hadronic interaction cross section in the detector material enter in the systematic uncertainty, as opposite to the high-pTrange (4 <pT(K∗0)<15 GeV/c) where these contributions are negligible (<0.5 %). In the φcase, two pTintervals are considered, according to the particle identification approach used to identify the decay products, namely the “TPC-TOF” and “No PID” strategies described in Sect. 3.1.ThepTregion where the TPC-TOF PID is applied (pT<3GeV/c), coincides also with the range where effects of material budget and hadronic interaction cross section are relevant for the measurement of φproduction. The main source of uncertainty, common to K∗0and φ, comes from the determination of the global tracking efficiency. In p–Pb collisions this contribution has been estimatedtobeapT-independent effect of 3 % for charged particles [48], which results in a 6 % effect when any two tracks are combined in the invariant-mass analysis of K∗0and φ. The track selection was varied to study systematic effects: the analyses are sensitive to variations of the cuts on the number of crossed rows in the TPC and the distance of closest approach to the primary vertex of the collision. Track selection enters in the total uncertainty with a relative contribution of 2.5 % for K∗0and about 1.9–2.2 % for the φcase. At high transverse momentum, namely for pT(K∗0)>8 GeV/cand pT(φ) > 12 GeV/c, the systematic uncertainties are dominated by the raw yield extraction procedure. This Table 2 Sources of systematic uncertainties for K∗0and φyields (d2N/(dpTdy)). For each source and transverse momentum range (see text for details), the average relative uncertainty over all multiplicity classes is listed. For each pTrange, the particle identification (“PID technique”) used for the analysis is also indicated. The contributions have been summed in quadrature to estimate the total relative systematic uncertainty K∗0φ pT(GeV/c) 0–4.0 4.0–15.0 0.3–3.0 3.0–21.0 PID technique TPC-TOF TPC-TOF No PID Global tracking efficiency 6% 6% Track selection cuts 2.5% 1.9% 2.2% Material budget 1.2% <0.5% 2.2% <0.5% Hadronic interaction cross section 1.9% <0.5% 2.4% <1% Particle identification 1.1% 2.7% 0.9% – Signal extraction 3.8% 4.6% 1.8% 4.3% Total 7.9% 8.4% 7.4% 7.7% 123 245 Page 8 of 21 Eur. Phys. J. C (2016) 76:245 contribution is labelled as “Signal extraction” in Table 2and it includes the background normalisation region, the choice of the fitting range, the residual background shape and variations of the constraints on the fit parameters. In addition to the default strategy described in Sect. 3.2, the mixedevent background distributions for K∗0and φhave been normalised in different invariant-mass regions that surround, but exclude the signal peaks. The sensitivity of the K∗0(φ) yield extraction to the fit range has been studied by varying each interval boundary within ±50 MeV/c2(±5MeV/c2). As alternative to the second-order polynomial, thirdand first-order polynomial functions have been used to fit the residual background. The measurements for both K∗0and φturned out to be independent on the mass parameters, but not on the constraints on the K∗0width and φmass resolution. Therefore, the K∗0width has been varied by ±50 % for systematic studies, while the φresolution has been varied within the range of values observed in the simulation. Due to the lower particle multiplicity and the improved PID strategy that has led to a lower residual background after mixed-event background subtraction, the contribution of signal extraction for K∗0is reduced by half in p–Pb with respect to the Pb–Pb case, where the uncertainty associated to the choice of the fitting range was larger than 9 % [33]. In order to study the effect of the PID selection on signal extraction, the cuts on TOF and TPC have been varied to 3σand 4σwith respect to the default settings described in Sect. 3.1, resulting in the average contribution to the systematic uncertainty reported in Table 2as “particle identification”. For K∗0the average contribution from PID is 1.1% in the low-pTrange, and 2.7 % at high transverse momenta. The contribution to the φuncertainty is 0.9 % on average in the transverse momentum range where TPC-TOF PID is applied. The knowledge of the material budget contributes for K∗0(φ) with an average of 1.2 % (2.2 %) at low transverse momentum, and a maximum of 3.5 % (5.4 %), reached for 0 <pT<0.2 GeV/c(0.8 <pT<0.9 GeV/c). In both cases, it is negligible for pT>3GeV/c. The contribution from the estimate of the hadronic interaction cross section in the detector material is 1.9 % (2.4%) for K∗0(φ)atlowpT,negligible for pT>4GeV/c(pT>3GeV/c). These effects were evaluated by combining the uncertainties for a πand a K (for K∗0), and for two K (in the case of φ), determined as in [42,47], according to the kinematics of the decay. The systematics were studied independently for all event classes,inordertoseparatethesourceswhicharemultiplicitydependent and uncorrelated across multiplicity bins. In particular, signal extraction and PID are fully uncorrelated sources, whereas global tracking, track cuts, material budget and hadronic cross section are correlated among different event classes. 4 Results and discussion 4.1 Transverse momentum spectra The multiplicity-dependent transverse momentum spectra of K∗0and φmesons measured in the rapidity range −0.5 <y <0 are reported in Fig. 4. Measured yields are corrected for acceptance, efficiency and branching ratio, and normalised to the visible cross section in each V0A multiplicity event class, as discussed in Sect. 3.3. The minimum bias spectra for K∗0and φare also reported in Fig. 4and have been )c (GeV/ T p 0246810121416 ] -1 )c) [(GeV/yd T p/(dN 2 d evt 1/N 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1NSD x 1 0-20% x 4 20-40% x 2 40-60% x 1/2 60-80% x 1/4 80-100% x 1/8 = 5.02 TeV NN sALICE, p-Pb < 0y-0.5 < ) 0 K*+ 0 (K* 2 1 (a) )c (GeV/ T p 0 5 10 15 20 ] -1 ) c ) [(GeV/yd T p/(dN 2 d evt 1/N 8− 10 7− 10 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 NSD x 1 0-5% x 16 5-10% x 8 10-20% x 4 20-40% x 2 40-60% x 1/2 60-80% x 1/4 80-100% x 1/8 = 5.02 TeV NN sALICE, p-Pb < 0y-0.5 < φ (b) Fig. 4 Transverse momentum spectra d2N/(dpTdy)of K∗0(a)and φ(b) for different multiplicity classes (V0A estimator), measured in the rapidity range −0.5 <y<0. K∗0and K∗0are averaged. The multiplicity-dependent spectra are normalised to the visible cross section, whereas the minimum bias spectrum is normalised to the fraction ofNSD events (seetext).Statistical(bars)andsystematic (boxes)uncertainties are indicated. 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C (2016) 76:245 Page 19 of 21 245 5Budker Institute for Nuclear Physics, Novosibirsk, Russia 6California Polytechnic State University, San Luis Obispo, CA, USA 7Central China Normal University, Wuhan, China 8Centre de Calcul de l’IN2P3, Villeurbanne, France 9Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 10 Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain 11 Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City, Mérida, Mexico 12 Centro Fermi-Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Rome, Italy 13 Chicago State University, Chicago, IL, USA 14 China Institute of Atomic Energy, Beijing, China 15 Commissariat à l’Energie Atomique, IRFU, Saclay, France 16 COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan 17 Departamento de Física de Partículas and IGFAE, Universidad de Santiago de Compostela, Santiago de Compostela, Spain 18 Department of Physics and Technology, University of Bergen, Mons, Norway 19 Department of Physics, Aligarh Muslim University, Aligarh, India 20 Department of Physics, Ohio State University, Columbus, OH, USA 21 Department of Physics, Sejong University, Seoul, South Korea 22 Department of Physics, University of Oslo, Oslo, Norway 23 Dipartimento di Elettrotecnica ed Elettronica del Politecnico, Bari, Italy 24 Dipartimento di Fisica dell’Università ‘La Sapienza’ and Sezione INFN, Rome, Italy 25 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy 26 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy 27 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy 28 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy 29 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy 30 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padua, Italy 31 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy 32 Dipartimento di Scienze e Innovazione Tecnologica dell’Università del Piemonte Orientale and Gruppo Collegato INFN, Alessandria, Italy 33 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy 34 Division of Experimental High Energy Physics, University of Lund, Lund, Sweden 35 Eberhard Karls Universität Tübingen, Tübingen, Germany 36 European Organization for Nuclear Research (CERN), Geneva, Switzerland 37 Excellence Cluster Universe, Technische Universität München, Munich, Germany 38 Faculty of Engineering, Bergen University College, Mons, Norway 39 Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia 40 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 41 Faculty of Science, P.J. Šafárik University, Kosice, Slovakia 42 Faculty of Technology, Buskerud and Vestfold University College, Vestfold, Norway 43 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 44 Gangneung-Wonju National University, Gangneung, South Korea 45 Department of Physics, Gauhati University, Guwahati, India 46 Helsinki Institute of Physics (HIP), Helsinki, Finland 47 Hiroshima University, Hiroshima, Japan 48 Indian Institute of Technology Bombay (IIT), Mumbai, India 49 Indian Institute of Technology Indore (IITI), Indore, India 50 Inha University, Inchon, South Korea 51 Institut de Physique Nucléaire d’Orsay (IPNO), Université Paris-Sud, CNRS-IN2P3, Orsay, France 52 Institut für Informatik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 53 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 54 Institut für Kernphysik, Westfälische Wilhelms-Universität Münster, Münster, Germany 55 Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg, CNRS-IN2P3, Strasbourg, France 123 245 Page 20 of 21 Eur. 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C (2016) 76:245 56 Institute for Nuclear Research, Academy of Sciences, Moscow, Russia 57 Institute for Subatomic Physics of Utrecht University, Utrecht, The Netherlands 58 Institute for Theoretical and Experimental Physics, Moscow, Russia 59 Institute of Experimental Physics, Slovak Academy of Sciences, Kosice, Slovakia 60 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic 61 Institute of Physics, Bhubaneswar, India 62 Institute of Space Science (ISS), Bucharest, Romania 63 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico 64 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 65 iThemba LABS, National Research Foundation, Somerset West, South Africa 66 Joint Institute for Nuclear Research (JINR), Dubna, Russia 67 Konkuk University, Seoul, South Korea 68 Korea Institute of Science and Technology Information, Taejon, South Korea 69 KTO Karatay University, Konya, Turkey 70 Laboratoire de Physique Corpusculaire (LPC), Clermont Université, Université Blaise Pascal, CNRS-IN2P3, Clermont-Ferrand, France 71 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 72 Laboratori Nazionali di Frascati, INFN, Frascati, Italy 73 Laboratori Nazionali di Legnaro, INFN, Legnaro, Italy 74 Lawrence Berkeley National Laboratory, Berkeley, CA, USA 75 Moscow Engineering Physics Institute, Moscow, Russia 76 Nagasaki Institute of Applied Science, Nagasaki, Japan 77 National Centre for Nuclear Studies, Warsaw, Poland 78 National Institute for Physics and Nuclear Engineering, Bucharest, Romania 79 National Institute of Science Education and Research, Bhubaneswar, India 80 National Research Centre Kurchatov Institute, Moscow, Russia 81 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 82 Nikhef, Nationaal instituut voor subatomaire fysica, Amsterdam, The Netherlands 83 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, UK 84 Nuclear Physics Institute, Academy of Sciences of the Czech Republic, ˇ Rež u Prahy, Czech Republic 85 Oak Ridge National Laboratory, Oak Ridge, TN, USA 86 Petersburg Nuclear Physics Institute, Gatchina, Russia 87 Physics Department, Creighton University, Omaha, NE, USA 88 Physics Department, Panjab University, Chandigarh, India 89 Physics Department, University of Athens, Athens, Greece 90 Physics Department, University of Cape Town, Cape Town, South Africa 91 Physics Department, University of Jammu, Jammu, India 92 Physics Department, University of Rajasthan, Jaipur, India 93 Physik Department, Technische Universität München, Munich, Germany 94 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 95 Purdue University, West Lafayette, IN, USA 96 Pusan National University, Pusan, South Korea 97 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany 98 Rudjer Boškovi´c Institute, Zagreb, Croatia 99 Russian Federal Nuclear Center (VNIIEF), Sarov, Russia 100 Saha Institute of Nuclear Physics, Kolkata, India 101 School of Physics and Astronomy, University of Birmingham, Birmingham, UK 102 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru 103 Sezione INFN, Bari, Italy 104 Sezione INFN, Bologna, Italy 105 Sezione INFN, Cagliari, Italy 106 Sezione INFN, Catania, Italy 123 Eur. 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C (2016) 76:245 Page 21 of 21 245 107 Sezione INFN, Padua, Italy 108 Sezione INFN, Rome, Italy 109 Sezione INFN, Trieste, Italy 110 Sezione INFN, Turin, Italy 111 SSC IHEP of NRC Kurchatov institute, Protvino, Russia 112 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria 113 SUBATECH, Ecole des Mines de Nantes, Université de Nantes, CNRS-IN2P3, Nantes, France 114 Suranaree University of Technology, Nakhon Ratchasima, Thailand 115 Technical University of Košice, Kosice, Slovakia 116 Technical University of Split FESB, Split, Croatia 117 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Kraków, Poland 118 Physics Department, The University of Texas at Austin, Austin, TX, USA 119 Universidad Autónoma de Sinaloa, Culiacán, Mexico 120 Universidade de São Paulo (USP), São Paulo, Brazil 121 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 122 University of Houston, Houston, TX, USA 123 University of Jyväskylä, Jyväskylä, Finland 124 University of Liverpool, Liverpool, UK 125 University of Tennessee, Knoxville, TN, USA 126 University of the Witwatersrand, Johannesburg, South Africa 127 University of Tokyo, Tokyo, Japan 128 University of Tsukuba, Tsukuba, Japan 129 University of Zagreb, Zagreb, Croatia 130 Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, France 131 V. Fock Institute for Physics, St. Petersburg State University, St. Petersburg, Russia 132 Variable Energy Cyclotron Centre, Kolkata, India 133 Warsaw University of Technology, Warsaw, Poland 134 Wayne State University, Detroit, MI, USA 135 Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary 136 Yale University, New Haven, CT, USA 137 Yonsei University, Seoul, South Korea 138 Zentrum für Technologietransfer und Telekommunikation (ZTT), Fachhochschule Worms, Worms, Germany aDeceased bAlso at: Georgia State University, Atlanta, GA, USA cAlso at Department of Applied Physics, Aligarh Muslim University, Aligarh, India dAlso at: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear Physics, Moscow, Russia 123