Centrality dependence of charged jet production 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. Centrality dependence of charged jet production in p–Pb collisions at √sNN = 5.02 TeV ALICE Collaboration ALICE Collaboration. (2016). Centrality dependence of charged jet production in p–Pb collisions at √sNN = 5.02 TeV. The European Physical Journal C, 76(5), Article 271. https://doi.org/10.1140/epjc/s10052-016-4107-8 2016 Eur. Phys. J. C (2016) 76:271 DOI 10.1140/epjc/s10052-016-4107-8 Regular Article - Theoretical Physics Centrality dependence of charged jet production in p–Pb collisions at √sNN =5.02TeV ALICE Collaboration CERN, 1211 Geneva 23, Switzerland Received: 14 March 2016 / Accepted: 24 April 2016 / Published online: 17 May 2016 © CERN for the benefit of the ALICE collaboration 2016. This article is published with open access at Springerlink.com Abstract Measurements of charged jet production as a function of centrality are presented for p–Pb collisions recordedat√sNN =5.02TeVwiththeALICEdetector.Centrality classes are determined via the energy deposit in neutron calorimeters at zero degree, close to the beam direction, to minimise dynamical biases of the selection. The corresponding number of participants or binary nucleon–nucleon collisions is determined based on the particle production in the Pb-going rapidity region. Jets have been reconstructed in the central rapidity region from charged particles with the anti-kTalgorithm for resolution parameters R=0.2 and R=0.4 in the transverse momentum range 20 to 120 GeV/c. The reconstructed jet momentum and yields have been corrected for detector effects and underlying-event background. In the five centrality bins considered, the charged jet production in p–Pb collisions is consistent with the production expected from binary scaling from pp collisions. The ratio of jet yields reconstructed with the two different resolution parameters is also independent of the centrality selection, demonstrating the absence of major modifications of the radial jet structure in the reported centrality classes. 1 Introduction Themeasurement ofbenchmark processesin proton–nucleus collisions plays a crucial role for the interpretation of nucleus–nucleus collision data, where one expects to create a system with high temperature in which the elementary constituents of hadronic matter, quarks and gluons, are deconfined for a short time: the quark-gluon plasma (QGP) [1]. Proton–lead collisions are important to investigate cold nuclear initial and final state effects, in particular to disentangle them from effects of the hot medium created in the final state of Pb–Pb collisions [2]. See Appendix A for the list of collaboration members. e-mail: [email protected] The study of hard parton scatterings and their subsequent fragmentation via reconstructed jets plays a crucial role in the characterisation of the hot and dense medium produced in Pb–Pb collisions while jet measurements in p–Pb and pp collisions provide allow to constrain the impact of cold nuclear matter effects in heavy-ion collisions. In the initial state, the nuclear parton distribution functions can be modified with respect to the quark and gluon distributions in free nucleons, e.g. via shadowing effects and gluon saturation [2,3]. In addition, jet production may be influenced, already in p–Pb collisions, by multiple scattering of partons and hadronic re-interaction in the initial and final state [4,5]. In the absence of any modification in the initial state, the partonic scattering rate in nuclear collisions compared to pp collisions is expected to increase linearly with the average number of binary nucleon–nucleon collisions Ncoll.This motivates the definition of the nuclear modification factor RpPb, as the ratio of particle or jet transverse momentum (pT) spectra in nuclear collisions to those in pp collisions scaled by Ncoll. In heavy-ion collisions at the LHC, binary (Ncoll) scaling is found to hold for probes that do not interact strongly, i.e. isolated prompt photons [6] and electroweak bosons [7,8]. On the contrary, the yields of hadrons and jets in central Pb– Pb collisions are strongly modified compared to the scaling assumptions. For hadrons, the yield is suppressed by up to a factor of seven at pT≈6GeV/c, approaching a factor of two at high pT(30 GeV/c)[9–11]. A similar suppression is observed for jets [12–16]. This observation, known as jet quenching, is attributed to the formation of a QGP in the collision, where the hard scattered partons radiate gluons due to strong interaction with the medium, as first predicted in [17,18]. In minimum bias p–Pb collisions at √sNN =5.02 TeV the production of unidentified charged particles [19–22] and jets [23–25] is consistent with the absence of a strong final state suppression. However, multiplicity dependent studies in p–Pb collisions on the production of low-pTidentified particles and long range correlations [26–29] show similar 123 271 Page 2 of 16 Eur. Phys. J. C (2016) 76 :271 features as measured in Pb–Pb collisions, where they are attributed to the collective behaviour following the creation of a QGP. These features in p–Pb collisions become more pronounced for higher multiplicity events, which in Pb– Pb are commonly associated with more central collisions or higher initial energy density. The measurement of jets, compared to single charged hadrons, tests the parton fragmentation beyond the leading particle with the inclusion of large-angle and low-pT fragments. Thus jets are potentially sensitive to centralitydependent modifications of low-pTfragments. This work extends the analysis of the charged jet production in minimum bias p–Pb collisions recorded with the ALICE detector at √sNN =5.02 TeV to a centralitydifferential study for jet resolution parameters R=0.2 and 0.4 in the pTrange from 20 to 120 GeV/c[25]. Section 2 describes the event and track selection, the centrality determination, as well as the jet reconstruction, the corrections for uncorrelated background contributing to the jet momentum [15,30,31] and the corrections for detector effects. The impact of different centrality selections on the nuclear modification factor has been studied in detail in [32]. We estimate the centrality using zero-degree neutral energy and the charged particle multiplicity measured by scintillator array detectors at rapidities along the direction of the Pb beam to determine Ncoll. The correction procedures specific to the centrality-dependent jet measurement are discussed in detail. Section 3introduces the three main observables: the centrality-dependent jet production cross section, the nuclear modification factor, and ratio of jet cross sections for two different resolution parameters. Systematic uncertainties are discussed in Sect. 4and results are presented in Sect. 5. 2 Data analysis 2.1 Event selection The data used for this analysis were collected with the ALICE detector [33] during the p–Pb run of the LHC at √sNN =5.02 TeV at the beginning of 2013. The ALICE experimental setup and its performance during the LHC Run 1 are described in detail in [33,34]. For the analysis presented in this paper, the main detectors used for event and centrality selection are two scintillator detectors (V0A and V0C), covering the pseudo-rapidity range of 2.8<η lab <5.1 and −3.7<η lab <−1.7, respectively [35], and the Zero Degree Calorimeters (ZDCs), composed of two sets of neutron (ZNA and ZNC) and proton calorimeters (ZPA and ZPC) located at a distance ±112.5m from the interaction point. Here and in the following ηlab denotes the pseudo-rapidity in the ALICE laboratory frame. The minimum bias trigger used in p–Pb collisions requires signal coincidence in the V0A and V0C scintillators. In addition, offline selections on timing and vertexquality are used to remove events with multiple interactions within the same bunch crossing and (pile-up) and backgroundevents,suchas beam-gasinteractions.Theeventsample used for the analysis presented in this manuscript was collected exclusively in the beam configuration where the proton travels towards negative ηlab (from V0A to V0C). The nucleon–nucleon center-of-mass system moves in the direction of the proton beam corresponding to a rapidity of yNN =−0.465. A van der Meer scan was performed to measure the visible cross section for the trigger and beam configuration used in this analysis: σV0 =2.09 ±0.07 b [36]. Studies with Monte Carlo simulations show that the sample collected in the configuration explained above consists mainly of nonsingle diffractive (NSD) interactions and a negligible contribution from single diffractive and electromagnetic interactions (see [37] for details). The trigger is not fully efficient for NSD events and the inefficiency is observed mainly for events without a reconstructed vertex, i.e. with no particles produced at central rapidity. Given the fraction of events without a reconstructed vertex in the data the corresponding inefficiency for NSD events is estimated to (2.2±3.1)%. Thisinefficiencyisexpectedto mainlyaffectthemostperipheral centrality class. Following the prescriptions of [32], centrality classes are defined as percentiles of the visible cross section and are not corrected for trigger efficiency. The further analysis requires a reconstructed vertex, in addition to the minimum bias trigger selection. The fraction of events with a reconstructed vertex is 98.3% for minimum bias events and depends on the centrality class. In the analysis events with a reconstructed vertex |z|>10 cm along the beam axis are rejected. In total, about 96 ·106events, corresponding to an integrated luminosity of 46 µb−1, are used for the analysis and classified into five centrality classes 2.2 Centrality determination Centrality classes can be defined by dividing the multiplicity distribution measured in a certain pseudo-rapidity interval into fractions of the cross section, with the highest multiplicities corresponding to the most central collisions (smallest impact parameter b). The corresponding number of participants, as well as Ncoll and b, can be estimated with a Glauber model [38], e.g. by fitting the measured multiplicity distribution with the Npart distribution from the model, convoluted with a Negative Binomial Distribution (NBD). Details on this procedure for Pb–Pb and p–Pb collisions in ALICE are found in [32,39], respectively. In p–Acollisions centrality selection is susceptible to a variety of biases. In general, relative fluctuations of Npart and 123 Eur. Phys. J. C (2016) 76 :271 Page 3 of 16 271 of event multiplicity are large, due to their small numerical value, in p–Pb collisions [32]Npart=Ncoll+1= 7.9±0.6 and dNch dη=16.81 ±0.71, respectively. Using either of these quantities to define centrality, in the Glauber model or the in experimental method, already introduces a bias compared to a purely geometrical selection based on the impact parameter b. In addition, a kinematic bias exists for events containing high-pTparticles, originating from parton fragmentation as discussed above. The contribution of these jet fragments to the overall multiplicity rises with the jet energy and thus can introduce a trivial correlation between the multiplicity and presence of a high-pTparticle, and a selection on multiplicity will bias the jet population. High multiplicity events are more likely created in collisions with multipleparton interactions, which can lead to a nuclear modification factor larger than unity. On the contrary, the selection of low multiplicity (peripheral) events can pose an effective veto on hard processes, which would lead to a nuclear modification factor smaller than unity. As shown in [32]the observed suppression and enhancement for charged particles in bins of multiplicity with respect to the binary scaling assumption can be explained by this selection bias alone. The bias can be fully reproduced by an independent superposition of simulated pp events and the farther the centrality estimator is separated in rapidity from the measurement region at mid-rapidity, the smaller the bias. We do not repeat the analysis for the centrality estimators with known biases here. In this work, centrality classification is based solely on the zero-degree energy measured in the lead-going neutron detector ZNA, since it is expected to have only a small dynamical selection bias. However, the ZNA signal cannot be related directly to the produced multiplicity for the Ncoll determination via NBD. As discussed in detail in [32] an alternative hybrid approach is used to connect the centrality selection based on the ZNA signal to another Ncoll determination via the charged particle multiplicity in the lead-going direction measured with the V0A (NcollPb−side c). This approach assumes that the V0 signal is proportional to the number of wounded lead (target) nucleons (Ntarget part = Npart −1=Ncoll). The average number of collisions for a given centrality, selected with the ZNA, is then given by scaling the minimum bias value NcollMB =6.9 with the ratio of the average raw signal Sof the innermost ring of the V0A: NPb−side coll c=NcollMB ·Sc SMB .(1) The values of Ncoll obtained with this method are shown in Table 1for different ZNA centrality classes [32]. 2.3 Jet reconstruction and event-by-event corrections The reported measurements are performed using charged jets, clustered starting from charged particles only, as described in [15,25,40] for different collision systems. Charged particles are reconstructed using information from theInnerTrackingSystem(ITS)[41]andtheTimeProjection Chamber(TPC)whichcoverthefullazimuthand|ηlab|<0.9 for tracks reconstructed with full length in the TPC [42]. The azimuthal distribution of high-quality tracks with reconstructed track points in the Silicon Pixel Detector (SPD), the two innermost layers of the ITS, is not completely uniform due to inefficient regions in the SPD. This canbecompensatedby consideringinaddition trackswithout reconstructed points in the SPD. The additional tracks constitute approximately 4.3% of the track sample used for analysis. For these tracks, the primary vertex is used as an additional constraint in the track fitting to improve the momentum resolution. This approach yields a uniform tracking efficiency within the acceptance, which is needed to avoid geometrical biases of the jet reconstruction algorithm caused by a non-uniform density of reconstructed tracks. The procedure is described first and in detail in the context of jet reconstruction with ALICE in Pb–Pb collisions [15]. The anti-kTalgorithm from the FastJet package [43]is employed to reconstruct jets from these tracks using the pT recombinationscheme. Theresolutionparametersusedin the presentanalysisare R=0.2and R=0.4.Reconstructedjets are further corrected for contributions from the underlying event to the jet momentum as pT,ch jet =praw T,ch jet −Ach jet ·ρch,(2) where Ach jet is the area of the jet and ρch the event-by-event background density [44]. The area is estimated by counting the so-called ghost particles in the jet. These are defined as particles with a finite area and vanishing momentum, which are distributed uniformly in the event and included in the jet reconstruction [45]. Their vanishing momentum ensures that the jet momentum is not influenced when they are included, while the number of ghost particles assigned to the jet provides a direct measure of its area. The background density ρch is estimated via the median of the individual momentum densities of jets reconstructed with the kTalgorithm in the event ρch =median pT,k Ak·C,(3) where kruns over all reconstructed kTjets with momentum pT,iandarea Ai.ReconstructedkTjetsarecommonlychosen for the estimate of the background density, since they provide a more robust sampling of low momentum particles. Cis the occupancy correction factor, defined as 123 271 Page 4 of 16 Eur. Phys. J. C (2016) 76 :271 Table 1 Average Ncoll values for centrality classes selected with the ZNA determined with the hybrid approach (NPb−side coll )[32], as well as moments of the background density and background fluctuation distributions shown in Fig. 1(negligible statistical uncertainty) ZNA centrality class (%) of visible cross section NPb−side coll ρ(GeV/c)σ(ρ)(GeV/c)σ(δpT,ch)(R=0.4)(GeV/c) 0–20 12.1 ±1.0 1.60 1.17 1.43 20–40 9.6 ±0.8 1.27 1.04 1.30 40–60 6.7 ±0.5 0.88 0.84 1.11 60–80 4.0 ±0.3 0.70 0.52 0.90 80–100 2.1 ±0.3 0.26 0.37 0.71 Minimum bias (0–100) 6.9 ±0.6 0.98 1.02 0.91 C=jAj Aacc ,(4) where Ajis the area of each kTjet with at least one real track, i.e. excluding ghosts, and Aacc is the area of the chargedparticleacceptance,namely(2×0.9)×2π.Thetypicalvalues for Crange from 0.72 for most central collisions (0–20%) to 0.15 for most peripheral collisions (80–100%). This procedure takes into account the more sparse environment in p– Pb collisions compared to Pb–Pb and is described in more detail in [25]. The probability distribution for ρch for the five centrality classes and minimum bias is shown in Fig. 1 (left) and the mean and width of the distributions are given in Table 1. The event activity and thus the background density increases for more central collisions, though on average the background density is still two orders of magnitude smaller thanin Pb–Pbcollisionswhere ρch is≈140GeV/cforcentral collisions [31]. 2.4 Jet spectrum unfolding Residual background fluctuations and instrumental effects can smear the jet pT. Their impact on the jet spectrum needs to be corrected on a statistical basis using unfolding, which is performed using the approach of Singular-Value- Decomposition (SVD) [46]. The response matrix employed in the unfolding is the combination of the (centralitydependent) jet response to background fluctuations and the detector response. The general correction techniques are discussed in detail in the context of the minimum bias charged jet measurement in p–Pb [25]. Region-to-region fluctuations of the background density compared to the event median, contain purely statistical fluctuations of particle number and momentum and in addition also intra-event correlations, e.g. those characterised by the azimuthal anisotropy v2and higher harmonics, which induce additional variations of the local background density. The impact of these fluctuations on the jet momentum is determined by probing the transverse momentum density in randomly distributed cones in (η, φ) and comparing it to the average background via [31]: δpT,ch = i pT,i−ρch ·A,A=πR2(5) where pT,iis the transverse momentum of each trackiinside a cone of radius R, where Rcorresponds to the resolution parameter in the jet reconstruction. ρch is the background )c (GeV/ ch ρ 02468101214 Probability density -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 1 10 2 10 = 5.02 TeV NN sALICE p-Pb c > 0.15 GeV/ T, track p| < 0.9, lab η | = 0.4R Centrality classes (ZNA) Minimum bias 0-20% 20-40% 40-60% 60-80% 80-100% )c (GeV/ T, ch p δ -10-5 0 5 10152025303540 Probability density -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 1 10 2 10 = 5.02 TeV NN sALICE p-Pb c > 0.15 GeV/ T, track p| < 0.9, lab η | = 0.4R Centrality classes (ZNA) Minimum bias 0-20% 20-40% 40-60% 60-80% 80-100% Fig. 1 Left Centrality dependence of the background momentum density ρch determined with kTjets and R=0.4. Right δpT,ch distributions for different centralities obtained with random cones and R=0.4 123 Eur. Phys. J. C (2016) 76 :271 Page 5 of 16 271 density, and Athe area of the cone. The distribution of residuals, as defined by Eq. 5, is shown for different centralities in Fig. 1(right). The corresponding widths are given in Table 1. The background fluctuations increase for more central events, which is expected from the general increase of statistical fluctuations (∝√N) with the particle multiplicity. The δpT,ch distributions measured for R=0.2 and 0.4 are used in the unfolding procedure. In addition to the background fluctuations the unfolding procedure takes into account the instrumental response. The dominatinginstrumentaleffectsonthereconstructedjetspectrum are the single-particle tracking efficiency and momentum resolution. These effects are encoded in a response matrix, which is determined with a full detector simulation using PYTHIA6 [47] to generate jets and GEANT3 [48]for thetransportthroughtheALICEsetup. Thedetector response matrix links the jet momentum at the charged particle level to the one reconstructed from tracks after particle transport through the detector. No correction for the missing energy of neutral jet constituents is applied. 3 Observables 3.1 Jet production cross sections The jet production cross sections dσc dpT, for different centralities c, are provided as fractions of the visible cross section σV0. The fraction of the cross section is determined with the number of selected events in each centrality bin Nc ev and takes into account the vertex reconstruction efficiency εc vtx determined for each centrality dσc dpT=εc vtx Nc ev dN dpT·σV0 ·Nc ev NMB ev =εc vtx NMB ev dN dpT·σV0,(6) where εc vtx decreases from 99.9% for the most central selection (0–20%) to 95.4% in peripheral. 3.2 Quantifying nuclear modification The nuclear modification factor compares the pT-differential per-event yield, e.g. in p–Pb or Pb–Pb collisions, to the differential yield in pp collisions at the same center-of-mass energyinordertoquantifynucleareffects.Undertheassumption that the jet or particle production at high pTscales with the number of binary collisions, the nuclear modification factor is unity in the absence of nuclear effects. In p–Pb collisions the jet population can be biased, depending on the centrality selection and Ncoll determination, hence the nuclear modification factor may vary from unity even in the absence of nuclear effects as described in detail in Sect.2.2 (see also [32]). To reflect this ambiguity the centrality-differential nuclear modification factor in p– Pb collisions is called QpPb, instead of RpPb as in the minimum bias case. QpPb is defined as QpPb = d2Nc pPb/dηdpT Nc coll·d2Npp/dηdpT.(7) Here, Nc collis number of binary collisions for centrality c, shown in Table 1. For the construction of QpPb,weusethesameppreference as for the study of charged jet production in minimum bias p–Pb collisions [25]. This reference has been determined from the ALICE charged jet measurement at 7 TeV [40] via scaling to the p–Pb center-of-mass energy and taking into account the rapidity shift of the colliding nucleons. The scaling behaviour of the charged jet spectra is determined based on pQCD calculations using the POWHEG framework [49] and PYTHIA parton shower (see [25]for details). This procedure fixes the normalisation based on the measured data at 7 TeV, while the evolution of the cross section with beam energy is calculated, taking into account all dependences implemented in POWHEG and PYTHIA, e.g. the larger fraction of quark initiated jets at lower collision energy. 3.3 Jet production cross section ratio The angular broadening or narrowing of the parton shower with respect to the original parton direction can have an impact on the jet production cross section determined with different resolution parameters. This can be tested via the ratio of cross sections or yields reconstructed with different radii, e.g. R=0.2 and 0.4, in a common rapidity interval, here |ηlab|<0.5: R(0.2,0.4)=dσpPb,R=0.2/dpT dσpPb,R=0.4/dpT.(8) Consider for illustration the extreme scenario where all fragments are already contained within R=0.2. In this case the ratio would be unity. In addition, the statistical uncertainties between R=0.2 and R=0.4 would be fully correlated and they would cancel completely in the ratio, when the jets are reconstructed from the same data set. If the jets are less collimated, the ratio decreases and the statistical uncertainties cancel only partially. For the analysis presented in this paper,theconditionalprobability variesbetween25 and50% for reconstructing a R=0.2 jet in the same pT-bin as a geometrically close R=0.4 jet. This leads to a reduction of the statistical uncertainty on the ratio of about 5–10% compared to the case of no correlation. The measurement and comparison of fully corrected jet cross sections for different radii provides an observable sen- 123 271 Page 6 of 16 Eur. Phys. J. C (2016) 76 :271 sitive to the radial redistribution of momentum that is also theoretically well defined [50]. Other observables that test the structure of jets, such as the fractional transverse momentum distribution of jet constituents in radial and longitudinal direction or jet-hadron correlations [10,51–54], are potentially more sensitive to modified jet fragmentation in p– Pb and Pb–Pb . However, in these cases the specific choices of jet reconstruction parameters, particle pTthresholds and the treatment of background particles often limit the quantitative comparison between experimental observables and to theory calculations. 4 Systematic uncertainties The different sources of systematic uncertainties for the three observables presented in this paper are listed in Table 2for 0–20% and 60–80% most central collisions. The dominant source of uncertainty for the pT-differential jet production cross section is the uncertainty of the singleparticletrackingefficiencythat hasa directimpacton thecorrection of the jet momentum in the unfolding, as discussed in Sect.2.4. In p–Pb collisions, the single-particle efficiency is known with a relative uncertainty of 4%, which is equivalent to a 4% uncertainty on the jet momentum scale. To estimate the effect of the tracking efficiency uncertainty on the jet yield, the tracking efficiency is artificially lowered by randomly discarding the corresponding fraction of tracks (4%) used as input for the jet finder. Depending on the shape of the spectrum, the uncertainty on the single-particle efficiency (jet momentum scale) translates into an uncertainty on the jet yield ranging from 8 to 15%. To estimate the effect of the single-particle efficiency on the p–Pb nuclear modification factor for jets, one has to consider that the uncertainty on the efficiency is partially correlated between the pp and p–Pb data set. The correction is determined with the same description of the ALICE detector in the Monte Carlo and for similar track quality cuts, but changes of detector conditions between run periods reduce the degree of correlation between the data sets. The uncorrelated uncertainty on the single-particle efficiency has been estimated to 2% by varying the track quality cuts in data and simulations. Consequently, the resulting uncertainty for the nuclear modification factor is basically half the uncertainty due to the single particle efficiency in the jet spectrum (cf. Table2). It was determined by discarding 2% of the tracks in one of the two collision systems, as also described in [25]. Uncertainties introduced by the unfolding procedure, e.g. choice of unfolding method, prior, regularisation strength, and minimum pTcut-off, are determined by varying those methods and parameters within reasonable boundaries. Bayesian [55,56] and χ2[57] unfolding have been tested and compared to the default SVD unfolding to estimate the systematic uncertainty of the chosen method. The quality of the unfolded result is evaluated by inspecting the Pearson coefficients,where alarge(anti-)correlation betweenneighbouring bins indicates that the regularisation is not optimal. The overall uncertainty on the jet yield due to the background subtraction is estimated by comparing various back- Table 2 Summary of systematic uncertainties on the fully corrected jet spectrum, the corresponding nuclear modification factor, and the jet production cross section ratio in 0–20% central and 60–80% peripheral events for the resolution parameter R=0.4. The range of percentages provides the variation from the minimum to the maximum momentum in each centrality. For R=0.2 only the combined uncertainty is provided for, the difference to R=0.4 is mainly due to the smaller impact of the single particle efficiency for smaller radii Observable Jet cross section (R=0.4) QpPb (R=0.4) R ZNA centrality class (%) 0–20 60–80 0–20 60–80 0–20 60–80 Single-particle efficiency (%) 10.2–14.0 10.0–12.7 4.9–6.3 4.9–6.4 2.0–2.0 1.8–4.7 Unfolding (%) 4.3 4.6 4.5 4.8 1.4 −3.1 Unfolding prior steepness (%) 0.9–7.0 0.3–3.6 1.1–7.2 0.8–4.0 0.7–1.4 0.3–2.2 Regularisation strength (%) 2.8–6.4 0.4–3.7 2.8–7.3 0.5–3.9 1.8–7.0 0.3–3.7 Minimum pTcut-off (%) 3.7–9.2 0.6–2.9 4.1–9.8 1.7–3.8 2.2–0.8 0.5–1.8 Background estimate (%) 3.5–1.8 3.8–3.0 3.5–1.8 3.8–3.0 1.7–1.8 2.6–1.2 δpT,ch estimate (%) 0.1–0.0 0.2–2.3 0.1–0.0 0.2–2.3 0.1–0.0 0.2–1.1 Combined uncertainty (%) 12.5–19.8 11.6–15.2 9.0–16.3 8.1–11.1 4.2–7.8 4.4–7.5 Combined uncertainty (R = 0.2) (%) 10.4–19.5 8.2–12.5 8.6–18.0 5.8–9.4 – – NPb−side coll (%) – – 8.0 8.0 – – Visible cross section (%) 3.3 3.3 – – – – Reference scaling pp 7 TeV (%) – – 9.0 9.0 – – NSD selection efficiency p–Pb (%) – – 3.1 3.1 – – Combined scaling uncertainty (%) – – 12.4 12.4 – – 123 Eur. Phys. J. C (2016) 76 :271 Page 7 of 16 271 ground estimates: track-based and jet-based density estimates, as well as pseudo-rapidity-dependent corrections. The estimated uncertainty amounts to 3.8% at low pTand decreases for higher reconstructed jet momenta. Themainuncertaintyrelatedtothebackgroundfluctuation estimateisgiven bythechoiceofexcludingreconstructedjets in the random cone sampling. While the probability of a jet to overlap with another jet in the event scales with Ncoll −1, it scales in the case of the random cone sampling with Ncoll. This can be emulated by rejecting a given fraction of cones overlapping with signal jets, which introduces an additional dependence on the definition of a signal jet. The resulting uncertainty due to the treatment of jet overlaps is of the order of 0.1% and can be considered negligible. In addition, several normalisation uncertainties need to be considered: the uncertainty on Ncoll (8% in the hybrid approach), on the visible cross section σV0 (3.3%) and from the assumptions made to obtain the scaled pp reference from 7to5TeV(9%). Further details on the evaluation of the centrality-indepe- ndent systematic uncertainties can be found in [25]. 5 Results The pT-differential cross sections for jets reconstructed from charged particles for five centrality classes in p–Pb collisions at √sNN =5.02 TeV are shown in Fig. 2. For both resolution parameters, the measured yields are higher for more central collisions, as expected from the increase of the binary interactions (cf. Table 1). The pp reference at √s=5.02 TeV is also shown. In addition to the increase in binary collisions the larger total cross section in p–Pb compared to pp further separates the data from the two collision systems; by an additional factor of 20 % ·σpPb V0 /σpp inel ≈6. The scaling behaviour of the p–Pb spectra with respect to the pp reference is quantified by the nuclear modification factor QpPb (Eq. 7). The nuclear modification factor with the hybrid approach, shown in Fig. 3, is compatible with unity for all centrality classes, indicating the absence of centralitydependent nuclear effects on the jet yield in the kinematic regime probed by our measurement. This result is consistent withthemeasurementofsinglechargedparticlesin p–Pbcollisions presented in [32], where the same hybrid approach is used. For other centrality selections, closer to mid-rapidity, a separation of QpPb for jets is observed for the different centralities that is caused by dynamical biases of the selection, similar to the QpPb for charged particles. If we use e.g. the centrality selection based on the multiplicity in the V0A, QpPb decreases from about 1.2 in central to approximately 0.5 in peripheral collisions [58]. )c (GeV/ T,ch jet p 20 40 60 80 100 120 /GeV)c (mb T p/d σ d -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 = 5.02 TeV NN sALICE p-Pb | < 0.5 lab η jets, | T kFastJet anti- Centrality classes (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100% pp reference (scaled pp jets 7 TeV) = 0.2RResolution parameter /GeV)c (mb T p/d σ d -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 = 5.02 TeV NN sALICE p-Pb | < 0.5 lab η jets, | T kFastJet anti- Centrality classes (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100% pp reference (scaled pp jets 7 TeV) = 0.4RResolution parameter Fig. 2 pT-differential production cross sections of charged jet production in p–Pb collisions at 5.02 TeV for several centrality classes. Top and bottom panels show the result for R=0.4andR=0.2, respectively. In these and the following plots, the coloured boxes represent systematic uncertainties, the error bars represent statistical uncertainties. The overall normalisation uncertainty on the visible cross section is 3.3 % in p–Pb . The corresponding reference pp spectrum is shown for both radii, it was obtained by scaling down the measured charged jets at 7 TeV to the reference energy The centrality dependence of full jet production in p– Pb collisions, i.e. using charged and neutral jet fragments, has been reported by the ATLAS collaboration in [23] over a broadrangeofthecenter-of-massrapidity(y∗)andtransverse momentum. Centrality-dependent deviations of jet production have been found for large rapidities in the proton-going direction and pT,jet 100 GeV/c. In the nucleon–nucleon center-of-mass system as defined by ATLAS, our measurement in |ηlab|<0.5 corresponds to −0.96 <y∗<−0.04. As shown in Fig. 4, the measurement of the nuclear modification factor of charged jets in central and peripheral collisions is consistent with the full jet measurement of ATLAS, where the kinematical selection of jet momentum and rapidity overlap, note however that the underlying parton pTat a given reconstructed pTis higher for charged jets. The centrality evolution for QpPb as measured by ALICE is shown for three pT-regions and R=0.4inFig.5.No significant variation is observed with centrality for a fixed pTinterval. The same holds for R=0.2 (not shown). 123 271 Page 8 of 16 Eur. Phys. J. C (2016) 76 :271 )c (GeV/ T, ch jet p 20 40 60 80 100 120 pPb Q 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 = 5.02 TeV NN sALICE p-Pb | < 0.5 lab η jets, | T kFastJet anti- Reference: scaled pp jets 7 TeV Centrality classes (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100% = 0.2RResolution parameter pPb Q 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 = 5.02 TeV NN sALICE p-Pb | < 0.5 lab η jets, | T kFastJet anti- Reference: scaled pp jets 7 TeV = 0.4RResolution parameter Centrality classes (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100% Fig. 3 Nuclear modification factors QpPb of charged jets for several centrality classes. Ncoll has been determined with the hybrid model. Top and bottom panels show the result for R=0.4andR=0.2, respectively.Thecombinedglobalnormalisationuncertaintyfrom Ncoll, the measured pp cross section, and the reference scaling is indicated by the box around unity Recently,thePHENIXcollaborationreportedonacentrality dependent modification of the jet yield in d–Au collisions at √sNN =200 GeV in the range of 20 <pT<50 GeV/c [59]: a suppression of 20% in central events and correspondingenhancement inperipheral eventsis observed.Evenwhen neglecting the impact of any possible biases in the centrality selection, the measurement of the nuclear modification at lower √sNN cannot be directly compared to the measurements at LHC for two reasons. First, in case of a possible final state energy loss the scattered parton momentum is the relevant scale. Here, the nuclear modification factor at lower energies is more sensitive to energy loss, due to the steeperspectrumof scatteredpartons. Second,for initialstate effects the nuclear modification should be compared in the probed Bjorken-x, which can be estimated at mid-rapidity to xT≈2pT/√sNN, and is at a given pTapproximately a factor of 25 smaller in p–Pb collisions at the LHC. The ratio of jet production cross sections reconstructed with R=0.2 and 0.4 is shown in Fig. 6. For all centrality classes, the ratio shows the expected stronger jet collimation )c (GeV/ T, jet p, T, ch jet p 20 40 60 80 100 120 pPb Q 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 = 5.02 TeV NN sp-Pb = 0.4R jets, T kFastJet anti- | < 0.5 (ALICE) lab η | -0.8 < y* < -0.3 (ATLAS) Centrality classes ALICE 0-20% ALICE 60-80% ATLAS 0-10% ATLAS 60-90% Fig. 4 Nuclear modification factor of charged jets compared to the nuclear modification factor for full jets as measured by the ATLAS collaboration [23]. Note that the underlying parton pTfor fixed reconstructed jet pTis higher in the case of charged jets Centrality (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100% pPb Q 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 )c interval (GeV/ T p < 30 T p ≤20 < 50 T p ≤40 < 80 T p ≤70 = 5.02 TeV NN sALICE p-Pb | < 0.5 lab η jets, | T kFastJet anti- Reference: Scaled pp jets 7 TeV = 0.4RResolution parameter Fig. 5 Centrality evolution of QpPb for selected pT,ch jet-bins and R= 0.4 towards higher pT. Moreover, the ratio is for all centralities consistent with the result obtained in minimum bias p– Pb collisions, which agrees with the jet cross section ratio in pp collisions as shown in [25]. The result is fully compatible with the expectation, since even in central Pb–Pb collisions, whereasignificantjetsuppressioninthenuclearmodification factor is measured, the cross section ratio remains unaffected [15]. 6 Summary Centrality-dependent results on charged jet production in p– Pb collisions at √sNN =5.02 TeV have been shown for transverse momentum range 20 <pT,ch jet <120 GeV/c and for resolution parameters R=0.2 and R=0.4. The 123 Eur. Phys. J. C (2016) 76 :271 Page 15 of 16 271 54 Institut für Kernphysik, Westfälische Wilhelms-Universität Münster, Münster, Germany 55 Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg, CNRS-IN2P3, Strasbourg, France 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, Daejeon, 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 123 271 Page 16 of 16 Eur. Phys. J. C (2016) 76 :271 105 Sezione INFN, Cagliari, Italy 106 Sezione INFN, Catania, Italy 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, Cracow, 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 Università di Brescia, Brescia, Italy 132 V. Fock Institute for Physics, St. Petersburg State University, St. Petersburg, Russia 133 Variable Energy Cyclotron Centre, Kolkata, India 134 Warsaw University of Technology, Warsaw, Poland 135 Wayne State University, Detroit, MI, USA 136 Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary 137 Yale University, New Haven, CT, USA 138 Yonsei University, Seoul, South Korea 139 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