Measurements of inclusive jet spectra in pp and central Pb-Pb collisions at √sNN = 5.02 TeV

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Measurements of inclusive jet spectra in pp and central Pb-Pb collisions at √sNN = 5.02 TeV ©2020 CERN, for the ALICE Collaboration Published version ALICE Collaboration ALICE Collaboration. (2020). Measurements of inclusive jet spectra in pp and central Pb-Pb collisions at √sNN = 5.02 TeV. Physical Review C, 101(3), Article 034911. https://doi.org/10.1103/PhysRevC.101.034911 2020 PHYSICAL REVIEW C 101, 034911 (2020) Measurements of inclusive jet spectra in pp and central Pb-Pb collisions at √sNN =5.02 TeV S. Acharya et al.∗ (ALICE Collaboration) (Received 4 October 2019; accepted 12 February 2020; published 16 March 2020) This article reports measurements of the pT-differential inclusive jet cross section in pp collisions at √s= 5.02 TeV and the pT-differential inclusive jet yield in Pb-Pb 0–10% central collisions at √sNN =5.02 TeV. Jets were reconstructed at midrapidity with the ALICE tracking detectors and electromagnetic calorimeter using the anti-kTalgorithm. For pp collisions, we report jet cross sections for jet resolution parameters R=0.1–0.6 over the range 20 <pT,jet <140 GeV/c, as well as the jet cross-section ratios of different Rand comparisons to two next-to-leading-order (NLO)–based theoretical predictions. For Pb-Pb collisions, we report the R=0.2and R=0.4 jet spectra for 40 <pT,jet <140 GeV/cand 60 <pT,jet <140 GeV/c, respectively. The scaled ratio of jet yields observed in Pb-Pb to pp collisions, RAA, is constructed, and exhibits strong jet quenching and a clear pTdependence for R=0.2. No significant Rdependence of the jet RAA is observed within the uncertainties of the measurement. These results are compared to several theoretical predictions. DOI: 10.1103/PhysRevC.101.034911 I. INTRODUCTION A deconfined state of strongly interacting matter described by quantum chromodynamics (QCD) is produced in ultrarelativistic heavy-ion collisions at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) [1–8]. Numerous observables including high-pThadron suppression, anisotropic flow, and J/ψ suppression and recombination provide evidence that the hot QCD state produced in these collisions consists of subnucleonic degrees of freedom. One of the major strategies to investigate this hot QCD state is the study of jet modification in heavy-ion collisions. Partons often traverse a significant path length of the hot QCD medium, and the effect that the medium has on the resulting jets can be deduced by comparing jet properties in heavy-ion collisions to those in pp collisions. Since the jet production cross section can be computed in perturbative QCD, and since jets are sensitive to a wide range of momentum exchanges with the medium, jet physics is an appealing tool to investigate the medium at a wide range of resolution scales. Previous measurements demonstrate suppression of the jet transverse momentum (pT) spectrum in heavy-ion collisions relative to pp collisions scaled by the number of incoherent binary nucleon-nucleon collisions, indicating that jets transfer energy to the hot QCD medium [9–15]. Furthermore, jet substructure measurements indicate that in heavy-ion collisions, the jet core is more collimated and fragments are harder [16], ∗Full author list given at the end of the article. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. while at wide angles from the jet axis there is an excess of soft particles [17,18]. Jet modification in heavy-ion collisions is described by several different theoretical approaches typically based on energy loss via medium-induced gluon radiation and elastic scattering [19–22, and references therein]; however, there remains no clear consensus of the precise nature of the interaction of jets with the medium. New measurements of the absolute level of jet suppression and its pTdependence will directly test models and serve as a key constraint for global analyses of high-pTobservables. Additionally, the evolution of jet suppression with the jet resolution parameter, R, can constrain competing effects between the recovery of out-ofcone radiation and the changing selection of the jet population (such as reduction of the quark/gluon fraction) as Rincreases [23–25]. The inclusive jet cross section in pp collisions contains important QCD physics itself. In recent years, the inclusive jet cross section in pp collisions was computed at NLO with resummation of logarithms of the jet resolution parameter [26–29] and threshold logarithms [30,31], and also to NNLO both with and without the leading color approximation [32,33]. Measurements of the inclusive pp jet cross section have been made at the SPS [34,35], the Tevatron [36,37], RHIC [38], and the LHC [39–47], and the latest comparisons of these measurements with theoretical predictions demonstrate the importance of contributions beyond NLO fixedorder calculations, namely resummations or matched parton showers. However, the precise contributions of the perturbative aspect of the jet, as well as the hadronization and underlying event (UE) effects, remain under investigation. Inclusive jet measurements at low-pTas a function of R (including ratios of jet cross sections, which allow partial cancellation of experimental and theoretical uncertainties) will help clarify these contributions and provide tests for both the perturbative and nonperturbative contributions to the 2469-9985/2020/101(3)/034911(21) 034911-1 ©2020 CERN, for the ALICE Collaboration S. ACHARYA et al. PHYSICAL REVIEW C 101, 034911 (2020) inclusive jet cross section. Moreover, these measurements can be used to constrain parton distribution functions (PDFs) and the strong coupling constant αs[43,45,48–50]. This article reports measurements of inclusive jet pTspectra in pp and central Pb-Pb collisions at √sNN =5.02 TeV with the ALICE detector. Jets were reconstructed in the pseudorapidity range |ηjet|<0.7−Rfor jet resolution parameters R=0.1–0.6inpp collisions and R=0.2 and R=0.4 in Pb-Pb collisions. In Pb-Pb collisions, we required jets to contain at least one charged track with pT>5–7 GeV/c (depending on the jet R) in order to identify hard jet candidates (arising from large momentum-transfer scatterings) in the large background from combinatorial jets. In pp collisions, we report the cross section both with and without this bias. The relative jet yields observed in Pb-Pb and pp collisions are reported using their scaled ratio, RAA, and compared to several theoretical predictions. II. EXPERIMENTAL SETUP AND DATASETS The ALICE detector [51,52] is a dedicated heavy-ion experiment located at the Large Hadron Collider [53]. The analysis relied on the central tracking system and the electromagnetic calorimeter (EMCal), as well as detectors for event triggering and centrality determination. The tracking system consists of a six-layer silicon inner tracking system (ITS) with radial distance 3.9–43 cm from the beamline, and a gas time projection chamber (TPC) with radial distance 85–247 cm from the beamline. The combined tracking system spans |η|< 0.9 and full azimuth, and tracks were measured in the range 150 MeV/c<pT,track <100 GeV/c. The EMCal consists of a Pb-scintillator sampling calorimeter spanning |η|<0.7 and 1.4<ϕ<3.3 in azimuth, located a radial distance 4.36 m from the beamline [54]. It contains 12 288 cells organized in an approximately projective geometry relative to the interaction point. The Moliere radius of the EMCal is rM=3.2cm, and its cells have a transverse size of approximately 6.0× 6.0cm(η ×ϕ ≈0.014 ×0.014). Each cell has a depth of 24.6 cm, corresponding to approximately 20 electromagnetic radiation lengths and one hadronic interaction length. The reported Pb-Pb (pp) data were recorded in 2015 (2017) at √sNN =5.02 TeV. The events were collected using a minimum bias (MB) trigger requiring a coincidence hit in both of the V0 scintillators, located at 2.8<η<5.1(V0-A) and −3.7<η<−1.7(V0-C)[55]. An accepted event was required to have a primary vertex successfully reconstructed within −10 cm <z<10 cm of the interaction point and to satisfy several vertex quality criteria. In Pb-Pb collisions, the centrality was determined using the V0 multiplicities [56–58]. Additionally, out-of-bunch pileup was rejected using timing cuts as well as correlating track multiplicities between several subdetectors. We utilized a sample of approximately 4.6M 0–10% most central Pb-Pb accepted events (6.0μb−1) and 760M pp accepted events (15.7nb−1). Reconstructed tracks were generally required to include at least one hit in the silicon pixel detector (SPD) comprising the first two layers of the ITS and to have at least 70 TPC space points and at least 80% of the geometrically findable space points in the TPC. Tracks without any hits in the SPD, but otherwise satisfying the tracking criteria, were refit with a constraint to the primary vertex of the event. Including this second class of tracks ensured approximately uniform acceptance in ϕ, while preserving similar pTresolution to tracks with SPD hits. Tracks with pT,track >150 MeV/cwere accepted over −0.9<η<0.9,0<ϕ<2π. The performance of the detector was estimated with a model of the ALICE detector and its response to particles using GEANT3. The tracking efficiency in pp collisions, as estimated by PYTHIA8 MONASH 2013 [59] and the ALICE GEANT3 detector simulation, is approximately 67% at pT,track =150 MeV/c, rises to approximately 84% at pT,track =1GeV/c, and remains above 75% at higher pT. Studies of the centrality dependence of the tracking efficiency in a HIJING [60] simulation demonstrated that the tracking efficiency is approximately 2% lower in 0–10% central Pb-Pb collisions compared to pp collisions, independent of pT,track. The momentum resolution δpT/pT was estimated from the covariance matrix of the track fit [52] using PYTHIA8MONASH 2013, and was approximately 1% at pT,track =1GeV/cand 4% at pT,track =50 GeV/c. Reconstructed EMCal clusters were built by clustering EMCal cells with Ecell >100 MeV around a seed cell with Eseed >300 MeV, using a clustering algorithm that allows each cluster to have only a single local maximum. The highest-energy cell in a cluster was required to satisfy a timing cut. Clusters with large apparent energy but anomalously small number of contributing cells were removed from the analysis, since they are believed to be due to interactions of slow neutrons or highly ionizing particles in the avalanche photodiodes [9]. The linearity of the energy response of the EMCal was determined from electron test beam data, and a correction of about 7% at Ecluster =0.5 GeV but negligible above Ecluster =3 GeV was applied to the cluster energies. A study using the photon conversion method demonstrated that with this nonlinearity correction, the π0mass in Monte Carlo (MC) simulations matches that in pp data within 1%. For pp collisions, an additional correction obtained from a photon conversion analysis was used to reduce the small remaining offset of the energy scale in data and MC simulations [61]. The energy resolution obtained from electron test beam data was about 15% at Ecluster =0.5GeV and better than 5% above Ecluster =3GeV. Since the jet energy is reconstructed by combining tracks and clusters, one needs to account for the fact that charged particles deposit energy in both the tracking system and the EMCal, as in Ref. [40]. In particular, all accepted tracks were propagated to the average shower depth of the EMCal, r=440 cm, and allowed to match geometrically to at most one cluster; clusters were allowed to have multiple matching tracks. If a track was matched within pT-dependent thresholds ranging from (η, ϕ)≈(0.037,0.084) at pT=0.15 GeV/cto (η, ϕ)≈(0.010,0.015) at pT=100 GeV/c, then a hadronic correction was applied to the cluster: Ehadcorr cluster =Enonlincorr cluster −E, where Enonlincorr cluster is the nonlinearity corrected cluster energy, and E=ciptrack i, where ispans all tracks matched to the cluster, ptrack iis the track three-momentum, and cis the speed of light. After the above cuts and corrections were performed, clusters with Ehadcorr cluster >300 MeV were accepted. 034911-2 MEASUREMENTS OF INCLUSIVE JET SPECTRA IN pp … PHYSICAL REVIEW C 101, 034911 (2020) TABLE I. Approximate values characterizing the jet reconstruction performance for R=0.2andR=0.4inpp and Pb-Pb collisions. For cases with a leading track requirement, plead,ch T=5 GeV/cis used for R=0.2andplead,ch T=7 GeV/cfor R=0.4. pp (plead,ch T>0 GeV/c)pp (plead,ch T>5/7 GeV/c)Pb-Pb(plead,ch T>5/7 GeV/c) pT,jet 20 GeV/c100 GeV/c20 GeV/c100 GeV/c20 GeV/c100 GeV/c R=0.2 JES −29% −30% −18% −28% −23% −35% JER 27% 21% 19% 19% 35% 23% εreco 98% 100% 86% 96% 86% 96% R=0.4 JES −30% −31% −14% −27% −6% −33% JER 23% 18% 15% 16% 77% 25% εreco 99% 100% 82% 92% 82% 92% III. JET RECONSTRUCTION Jets were reconstructed with R=0.1–0.6inpp collisions and R=0.2,0.4 in Pb-Pb collisions using the anti-kT sequential recombination algorithm implemented in FASTJET 3.2.1 [62,63] from the combination of charged particle tracks and hadronically corrected EMCal clusters. We used the pTrecombination scheme, assuming EMCal clusters are massless: praw T,jet =ipi T,track +jpj T,cluster, where pT,cluster =Ehadcorr cluster /c. In Pb-Pb collisions, we subtracted the average combinatorial background following the approach in Ref. [9]. The background density ρwas determined in each event and used to subtract the average background from each jet in that event: preco T,jet =praw T,jet −ρA,where Ais the jet area. The average background density in 0–10% central events is typically ρ≈220–280 GeV/c, corresponding to ≈110–140 GeV/c for a R=0.4jet.Inpp collisions, we did not subtract the background due to the underlying event, in order to minimize the model dependence of the measurement. Jets selected for the measurement were required to satisfy several criteria in order to be accepted: (i) the center of the jet must be within the fiducial volume of the EMCal, i.e., a distance R≡(η)2+(ϕ)2from any edge of the EMCal, (ii) the jet must not contain any tracks with pT,track > 100 GeV/c, (iii) in Pb-Pb and applicable pp results, the jet must contain a track with pT,track >5–7 GeV/c, depending on R, and (iv) in Pb-Pb collisions, the area of the jet must be A>0.6πR2.ThepT,track <100 GeV/c requirement removed only a small number of jets at large preco T,jet and has negligible bias for the preco,max T,jet selected in this analysis. The leading track requirement introduces a small fragmentation bias in the jet sample, which may lead to a bias in the measured jet suppression. This effect is discussed in Sec. VI and is estimated to have only a small effect on the reported RAA.A larger leading track requirement is needed for larger Rsince the magnitude of background fluctuations increases with R. The area cut in Pb-Pb collisions was negligible except at very low preco T,jet, where it rejects combinatorial jets. In Pb-Pb collisions, local fluctuations in the background smear the reconstructed jet momentum. To study jet-byjet fluctuations in the background, we generated a random (η, ϕ) within the fiducial calorimeter acceptance in each event and compared the sum of constituents in a cone of radius Rto the expected average background in that cone: δpT=cone (pT,track +pT,cluster)−ρπR2. The width of the δpTdistribution is a measure of the size of the background fluctuations [64]. For R=0.2, the standard deviation of the δpTdistribution is σδpT=6.5GeV/c, which grows to σδpT= 16.1GeV/cfor R=0.4. In the present analysis, the δpT distributions were not explicitly used except to determine the preco T,jet range to utilize in the analysis, which is discussed in Sec. IV. We evaluated the performance of our jet reconstruction strategy by estimating the mean jet energy scale shift, JES =(preco T,jet −ptrue T,jet)/ptrue T,jet, the jet energy resolution, JER =σ(preco T,jet)/ptrue T,jet, and the jet reconstruction efficiency, εreco,fromPYTHIA8MONASH 2013 and the ALICE detector simulation. Table Ishows approximate values of JES,JER,and εreco for R=0.2 and R=0.4inpp and Pb-Pb collisions. The jet energy scale shift is a long-tailed asymmetric distribution due to reconstruction inefficiency (such as tracking inefficiency) [10], and JES should be understood only as a rough characterization of this distribution. When a leading track requirement is imposed, the jet reconstruction efficiency and jet energy scale shift are primarily due to this requirement in combination with the tracking efficiency. Note that the pp response approximately, but not exactly, describes the detector effects in jet reconstruction relevant for Pb-Pb collisions. In Pb-Pb collisions, the jet reconstruction performance (including the effect of background fluctuations) was determined by embedding pp MC events into Pb-Pb data, as described in detail in Sec. IV.TheJER is approximately constant at ≈23% above ptrue T,jet =60 GeV/c for R=0.2, and deteriorates at lower ptrue T,jet due to background fluctuations. As Rincreases, the JER deteriorates due to the increased influence of background fluctuations. IV. CORRECTIONS The reconstructed preco T,jet spectrum includes fluctuations in the underlying background (in Pb-Pb collisions) and a variety of detector effects, including tracking inefficiency, missing long-lived neutral particles (n,K 0 L), and particle-material 034911-3 S. ACHARYA et al. PHYSICAL REVIEW C 101, 034911 (2020) interactions. We therefore deconvoluted the reconstructed jet spectrum with a response matrix (RM) describing the correlation between preco T,jet and ptrue T,jet in order to recover the “truth”- level jet spectrum at the hadron level. In pp collisions, we generated a RM using PYTHIA8 MONASH 2013 with the full GEANT3 ALICE detector simulation, based on the detector performance in the relevant 2017 pp data-collection period. In Pb-Pb collisions, we generated a RM by embedding PYTHIA events (with detector simulation based on the detector performance in the 2015 Pb-Pb datacollection period) into Pb-Pb data after the detector-level reconstruction was run individually on both. The set of tracks in the “hybrid” event was taken as the sum of all tracks in both events individually, while the set of EMCal clusters were reclustered from a combined pool of cells from both events. This embedding-based approach, which uses real background, ensures that the detector response accurately reflects the Pb-Pb response of the calorimeter, including particle overlaps in the calorimeter as well as the Pb-Pb particle composition, and ensures the effect of the hadronic correction is equivalent in data and in the response. Moreover, it ensures that the correlation between the local background and the reconstructed jet due to local detector inefficiencies is accounted for. The truth-level jet was constructed from the primary particles of the PYTHIA event, defined as all particles with a proper decay length longer than 1 cm, excluding daughters of these particles [65]. We correct the jet pTto include the “missing” long-lived neutral particles. The detector-level jet in ppcollisions was constructed from the PYTHIA tracks and clusters at detector level. In Pb-Pb collisions, the detector-level jet was constructed from the “hybrid” event consisting of both PYTHIA and Pb-Pb tracks and clusters at detector level. To account for the decreased tracking efficiency in Pb-Pb collisions, we randomly rejected 2% of the PYTHIA tracks in the Pb-Pb case, independent of pT. The average combinatorial background was subtracted as in 0–10% central Pb-Pb data: We computed the event-by-event ρcharged using only Pb-Pb tracks, and we applied the background scale factor obtained in Pb-Pb data; we assume that the combinatorial background from the ppevent is negligible. In order to fill the RM, we matched truth-level jets to detector-level jets by a geometrical matching procedure. In ppcollisions, if an accepted detector-level jet and an accepted PYTHIA jet were within R<0.6R, and they were both the closest jets to each other, then the jets were matched, and they contribute to the RM. In Pb-Pb collisions, if an accepted hybrid jet and an accepted PYTHIA jet were within R<1.5R, and they were both the closest jets to each other, then the jets were matched, and they contribute to the RM. The leading track requirement nullifies the need in Pb-Pb collisions for further criteria such as a shared momentum fraction requirement in order to generate accurate matches. The RM was generated with5GeV/cbin widths for preco T,jet and 10 GeV/cwidths for ptrue T,jet and was normalized so as to preserve the number of jets upon unfolding. To perform the deconvolution, we employed the SVD unfolding algorithm [66]usingtheROOUNFOLD package [67]. The regularization parameter ksuppresses high-frequency variations in the unfolded result and was TABLE II. Minimum and maximum reconstructed jet pTused in the analysis as input to the deconvolution procedure. pp (GeV/c) Pb-Pb (GeV/c) preco,min T,jet preco,max T,jet preco,min T,jet preco,max T,jet R=0.2 7 130 20 120 R=0.4 10 130 35 120 selected by examining the so-called d-vector distribution. Statistical uncertainties were computed according to MC pseudoexperiments within ROOUNFOLD. The reconstructed spectrum was input to the unfolding procedure over a fixed window of preco T,jet ∈[preco,min T,jet ,preco,max T,jet ], as illustrated in Table II. In Pb-Pb collisions, each of these preco,min T,jet corresponds to ≈2–3 ×σδpT, which, in combination with the leading charged hadron requirement, results in a sample largely free of combinatorial jets. A larger value of preco,min T,jet was used in Pb-Pb collisions in order to minimize the impact of the combinatorial background, which can destabilize the unfolding process. Any residual combinatorial jets will still be unfolded to low pTby the RM. Since truncating the RM in preco T,jet loses the information of the fraction of truth-level jets that migrate outside of the measured detector-level window, we corrected for this kinematic efficiency. The unfolded result is then reported in a range over which the input data provides meaningful constraints, that is, a region unaffected by combinatorial jets and where the kinematic efficiency is larger than approximately 80%. We corrected the unfolded spectrum for the fact that the jet finding procedure failed to reconstruct a certain fraction of jets. We computed the jet reconstruction efficiency as εrecoptrue T,jet=Nmatchedptrue T,jetNtruthptrue T,jet, where Nmatched is the number of accepted detector-level jets matched to PYTHIA truth-level jets out of Ntruth accepted truthlevel jets. In order that εreco also includes the false positive rate of accepted detector-level jets that have no matching truth-level jet (which can occur if the truth-level jet was generated slightly outside of our geometrical acceptance), the numerator also contains matches to truth-level jets outside of the EMCal fiducial acceptance. Note that εreco does not explicitly include the bias of the leading charged hadron requirement, but only the probability to reconstruct an accepted jet given a truth-level jet satisfying the leading charged hadron requirement (when applicable). In order for εreco to be the jet reconstruction efficiency, the jet matching efficiency must be 100%. However, in the Pb-Pb embedding environment, this is difficult to achieve, since some criteria need to be imposed to suppress combinatorial jets (in our case, the leading track requirement). Therefore, in the Pb-Pb case we used the jet reconstruction efficiency as determined from a pp simulation alone (with 2% reduced tracking efficiency). The unfolded solution was verified to be mathematically robust by performing a refolding test and a “self-closure” test. The refolding test consisted of generating a RM (from half of the MC data sample runs) and unfolding the measured 034911-4 MEASUREMENTS OF INCLUSIVE JET SPECTRA IN pp … PHYSICAL REVIEW C 101, 034911 (2020) distribution, then applying a RM (from the other half of the MC data sample) to the unfolded result, and comparing the refolded solution to the measured distribution. The selfclosure test consisted of taking the matched detector-level jet spectrum in the full embedded sample and smearing each data point with a Gaussian according to the statistical uncertainties of the measured data. This spectrum was then unfolded using the RM and compared the result to the truth-level PYTHIA jet spectrum. In both cases, consistency was achieved within statistical uncertainties. In Pb-Pb collisions, the unfolded solution is verified to be physically correct by a thermal model closure test similar to that in Ref. [9]. The closure test consisted of performing the entire analysis on “hybrid” events containing a PYTHIA event and a thermal background, in which “hybrid” jets were clustered from the combination of PYTHIA detector-level particles and thermal background particles. The background was modeled by generating Nparticles from a Gaussian, with pT taken from a distribution, f(pT;β)∼pTe−pT/β , where the free parameters N,σ N,β were fixed to roughly fit the δpT distribution in 0–10% Pb-Pb data. The test consisted of constructing the hybrid detector-level jet spectrum, building the RM, unfolding the hybrid jets—and comparing the spectrum to the truth-level PYTHIA spectrum. Since the background does not have any jet component, this test is able to verify whether the analysis procedure indeed recovers the jet spectrum and is not contaminated by combinatorial jets. These tests validated the analysis procedure within approximately 5% for R=0.2 with plead,ch T=5GeV/cand R=0.4 with plead,ch T=7GeV/c. V. SYSTEMATIC UNCERTAINTIES Following Ref. [9], we categorized two classes of systematic uncertainties: correlated uncertainties and shape uncertainties. Correlated uncertainties encompass detector effects such as uncertainty on the tracking efficiency and uncertainty on the EMCal response, which are approximately fully positively correlated among all pT,jet bins. Shape uncertainties refer to systematic unfolding uncertainties, which alter the shape of the final pT,jet spectrum. The dominant systematic uncertainties in this analysis are the uncertainty in the tracking efficiency and the systematic uncertainty in the unfolding procedure. Note that in general the following uncertainties describe uncertainties on the jet yield, not on the jet pTscale. A. Correlated uncertainties The dominant correlated uncertainty is the uncertainty on the modeling of the tracking efficiency, since correcting for unmeasured tracks has a major effect on the unfolding procedure. For the track selection described in Sec. II,the uncertainty on the tracking efficiency is approximately 4%, as estimated from variation in the track selection parameters and variation in the ITS-TPC matching requirements. In order to assign a systematic uncertainty to the final result, we constructed a RM using the same techniques as for the final result except with an additional 4% of PYTHIA tracks randomly rejected in jet finding (for Pb-Pb, this is in addition to the 2% rejection used for the main result). The jet reconstruction efficiency was also computed with this extra 4% suppression applied. This modified RM was then used to unfold the same measured spectrum as used for the main result. This varied result was corrected for the jet reconstruction efficiency and compared to the main result, with the differences in each bin taken as the uncertainty. Additionally, the uncertainty due to the tracking pTresolution was approximately 1%. Systematic uncertainties due to the modeling of the EMCal response were included in several ways. In order to describe the uncertainty in the MC description of the EMCal hadronic response, the subtracted energy in the hadronic correction was varied from 100% to 70% of the matched track momentum. Moreover, a systematic uncertainty associated with the track-matching criteria was included by changing the pTdependent track-matching criteria to pT-independent criteria η < 0.015,ϕ<0.03. These two uncertainties were combined in quadrature to form the uncertainty on the EMCal hadronic correction procedure. In order to describe the uncertainty in the MC description of the EMCal electromagnetic response, in the pp case the photon conversion based nonlinearity correction was switched off. These variations were individually performed both in the RM and the data, and the systematic uncertainty was evaluated by comparing the modified unfolded result to the main result. In the Pb-Pb case, there is an additional uncertainty due to the fact that the MC does not exactly describe the cluster energy nonlinearity. To account for this, different cluster nonlinearity corrections are typically applied to data and MC; however, in the Pb-Pb embedding procedure, the clusters are mixtures of data and MC cells. The main result was computed by applying the data nonlinearity parametrization to the mixed data and MC cells in the embedding procedure. Therefore, we applied the MC nonlinearity parametrization as a systematic variation. In Pb-Pb collisions for R=0.4, the uncertainties on the EMCal nonlinearity correction and track matching procedure are large, primarily due to unfolding effects, which we do not decouple in the evaluation of the correlated uncertainties. We included also a systematic uncertainty associated with the choice of jet matching procedure. For pp, the geometrical matching distance was varied from 0.4Rto 0.8R(except for R=0.1from0.2Rto 0.9R), which resulted in an uncertainty of less than 1% (1.5%). For Pb-Pb, we varied from a pure geometrical matching to an MC-fraction based approach, in which a shared momentum fraction requirement ensures that the matched jet contains more than 50% of the pTof the MC jet. This gave an uncertainty of 2–6%. We included also a systematic uncertainty associated with the model-dependent reliance on PYTHIA to unfold the spectra. In pp collisions, we reweighted the response matrix according to the jet angularity (g=ipT,iri/pT,jet, where ri= √η2+ϕ2is the distance of the ith constituent from the jet axis) at truth level. Specifically, we re-weighted the response matrix such that the 50% largest angularity jets were weighted an additional ±30% relative to the 50% lowest angularity jets. This contributed an uncertainty ranging from ≈2% to 7% depending on the jet R, and roughly independent of pT.The same uncertainties were taken for Pb-Pb collisions. Tables III and IV illustrate the contributions of the various correlated uncertainties for pp and Pb-Pb collisions. These 034911-5 S. ACHARYA et al. PHYSICAL REVIEW C 101, 034911 (2020) TABLE III. Summary of correlated systematic uncertainties on the pp jet spectra without a leading track bias, for select R. The columns pmin T,jet and pmax T,jet are the uncertainties at the minimum and maximum pT,jet bin. Relative uncertainty (%) R=0.2R=0.6 pp pmin T,jet pmax T,jet Avg. pmin T,jet pmax T,jet Avg. Tracking efficiency 5.9 9.1 7.7 9.4 8.9 9.0 Track pTresolution 1.0 1.0 1.0 1.0 1.0 1.0 EMCal nonlinearity 0.5 0.5 0.5 1.0 0.9 1.0 Hadronic correction 0.2 1.2 0.5 1.2 2.1 0.9 Jet matching 0.1 0.0 0.0 0.2 0.2 0.2 PYTHIA fragmentation 0.5 1.0 0.4 3.1 5.6 5.8 Total corr. uncertainty 6.0 9.3 7.8 10.1 10.8 10.8 uncertainties are expected to be largely independent, so we summed their uncertainties in quadrature. B. Shape uncertainties In order to assign a shape uncertainty arising from the unfolding regularization procedure, we performed several systematic variations: (1) Variation of the unfolding algorithm: We unfolded with a Bayes-inspired iterative unfolding algorithm [68]. (2) Variation of the regularization parameter: In the SVD unfolding, we varied the regularization parameter k one unit above and below the nominal solution. (3) Variation of the prior: The SVD algorithm requires a prior distribution as input, which for the main result is the projection of the RM onto the truth axis (before normalization). We varied this input prior either by scaling the main prior by p±0.5 Tor replacing it with a jet cross section produced by POWHEG or the unfolded main result itself. (4) Variation of the input range: For Pb-Pb (pp) collisions, we varied the measured input range ±5( +5 −3)GeV/c around the nominal value for each R. The total shape uncertainty is then the standard deviation of the variations, 3 i=1σ2 i/4, where σiis the systematic due to a single variation, since they each comprise independent measurements of the same underlying systematic uncertainty in the regularization. Tables Vand VI illustrate the contributions of the various shape uncertainties for ppand Pb-Pb collisions. C. Uncertainties on the jet cross-section ratio We computed the correlated systematic uncertainties on the pp jet cross-section ratio by making the same variations as in Sec. VA on both spectra simultaneously and compared the varied jet cross-section ratio to the main result. This resulted in significant cancellation of the correlated uncertainties between the numerator and denominator, as can be seen in Sec. VI. We computed the shape systematic uncertainties by adding the single spectra shape uncertainties in quadrature. It is important to note that the statistical uncertainties of the numerator and denominator are partially correlated, due to error propagation through the unfolding procedure. We did not, however, take this into account. This may result in a slightly conservative statistical uncertainty estimation, since there may be significant cancellation between the two radii. Additionally, we did not use statistically independent samples to form the ratio, and so the numerator and denominator are statistically correlated with each other, which may lead to further slight overestimation of the statistical uncertainties. TABLE IV. Summary of correlated systematic uncertainties on the Pb-Pb jet spectra, for select Rand plead,ch Tthresholds. The columns pmin T,jet and pmax T,jet are the uncertainties at the minimum and maximum pT,jet bin. Relative uncertainty (%) R=0.2,5 GeV/cR=0.4,7 GeV/c Pb-Pb pmin T,jet pmax T,jet Avg. pmin T,jet pmax T,jet Avg. Tracking efficiency 5.8 8.9 8.0 9.9 9.8 9.8 Track pTresolution 1.0 1.0 1.0 1.0 1.0 1.0 EMCal nonlinearity 2.1 1.1 1.6 11.4 7.9 9.5 Hadronic correction 0.8 5.9 2.0 12.8 9.9 12.4 Jet matching 2.0 2.0 2.0 6.0 2.0 2.8 PYTHIA fragmentation 0.8 3.6 2.0 2.8 5.1 3.8 Total corr. uncertainty 6.7 11.6 9.2 20.9 16.9 19.5 034911-6 MEASUREMENTS OF INCLUSIVE JET SPECTRA IN pp … PHYSICAL REVIEW C 101, 034911 (2020) TABLE V. Summary of shape systematic uncertainties on the pp jet spectra without a leading track bias, for select R. The columns pmin T,jet and pmax T,jet are the uncertainties at the minimum and maximum pT,jet bin. Relative uncertainty (%) R=0.2,0 GeV/cR=0.6,0 GeV/c pp pmin T,jet pmax T,jet Avg. pmin T,jet pmax T,jet Avg. Unfolding method 0.0 16.0 3.4 2.6 16.0 4.5 Reg. parameter 0.7 2.4 1.3 1.0 3.5 2.1 Prior 1.3 0.8 0.9 0.9 3.7 2.0 Input pTrange 0.8 3.3 1.3 0.4 3.2 1.4 Total shape uncertainty 0.8 8.3 2.2 1.5 8.5 3.0 VI. RESULTS A. Inclusive jet spectra 1. pp We report the pp full jet cross section for R= 0.1,0.2,0.3,0.4,0.5,0.6inFig.1(left). The cross sections are reported differentially in pT,jet and ηjet as d2σjet dpT,jetdηjet = 1 L d2N dpT,jetdηjet ,where we experimentally measured the yield d2N dpT,jetdηjet and the integrated luminosity L[55]. The uncertainty on the luminosity is 2.1%. The measured jet cross sections were unfolded for detector and background effects and are reported at the hadron-level. The cross sections were corrected for the kinematic efficiency and jet reconstruction efficiency, as well as the partial azimuthal acceptance of the EMCal and the vertex efficiency. Note that a leading track requirement was not imposed for the results in Fig. 1. We compare the pp inclusive jet cross section to two theoretical calculations in Fig. 1(right). The predictions denoted NLO+NLL+NP are analytical predictions at NLO with resummation of jet Rlogarithms and threshold logarithms to NLL accuracy, performed in a rigorous QCD factorization scheme [28,30,31]. The effect of unaccounted higher order corrections was evaluated by various scale variations and is included as a systematic uncertainty. A correction for hadronization and multiparton interaction (MPI) effects is applied to this prediction, based on PYTHIA8 tune A14 and is shown in Fig. 2. These nonperturbative (NP) effects become large for low pT,jet at both small and large R, where systematic uncertainties in this correction (beyond the scope of this article) are likely critical. The predictions use PDF set CT14nlo. These predictions are seen to be generally consistent with the data, except at low pTand small R. This tension may be due to the model-dependent NP correction, which is large in this region. The experimental data presented in Fig. 1, which cover a large range of Rdown to low pTand therefore span a wide range of NP effects (from hadronization dominated at small Rto MPI dominated at large R, as seen in Fig. 2), can be used to further constrain NP effects in pp collisions. This is of relevance both for pp QCD physics and for interpreting modifications in heavy-ion collisions, which are typically strongest at low pT. The predictions denoted POWHEG+PYTHIA8 consist of a MC parton-shower-based model using NLO calculations from POWHEG [69] matched to a parton shower and hadronization from PYTHIA8 tune A14.1Two theoretical uncertainties were computed for these predictions, both in regard to the POWHEG event generation: PDF uncertainty, computed as in Ref. [73], and scale uncertainty, which was computed by varying the 1The POWHEG reference was produced by POWHEG-BOX-V2at √s=5.02 TeV via the jet pair production process [69–71]. PDF set CT14nlo was used, along with the settings bornktmin =1and bornsuppfact =70. PYTHIA 8.2 tune A14 NNPDF2.3LO was used for the parton shower, which is tuned with ATLAS pp collisions at √sNN =7 TeV using underlying event observables, jet substructure observables, and several other observables, not including the inclusive jet cross section [72]. Merging with PYTHIA was done as in Ref. [73]. The same set of primary particles was used as described earlier [65]. TABLE VI. Summary of shape systematic uncertainties on the Pb-Pb jet spectra, for select Rand plead,ch Tthresholds. The columns pmin T,jet and pmax T,jet are the uncertainties at the minimum and maximum pT,jet bin. Relative uncertainty (%) R=0.2,5 GeV/cR=0.4,7 GeV/c Pb-Pb pmin T,jet pmax T,jet Avg. pmin T,jet pmax T,jet Avg. Unfolding method 7.7 10.0 5.4 30.3 2.5 18.2 Reg. parameter 4.2 8.7 4.4 24.9 20.6 23.1 Prior 1.5 6.7 2.4 2.3 8.3 4.2 Input pTrange 0.4 0.9 0.6 1.5 1.7 1.4 Total shape uncertainty 4.4 7.4 3.8 19.6 11.2 15.5 034911-7 S. ACHARYA et al. PHYSICAL REVIEW C 101, 034911 (2020) FIG. 1. Left: Unfolded pp full jet cross section at √s=5.02 TeV for R=0.1–0.6. No leading track requirement is imposed. Right: Ratio of NLO+NLL+NP and POWHEG+PYTHIA8 tune A14 predictions to the measured data. The systematic uncertainties in the ratio are denoted by boxes and are the quadratic sum of the systematic uncertainties in data and the predictions. Note that no systematic uncertainties for the nonperturbative correction in the NLO+NLL+NP prediction were included. renormalization and factorization scales. The total theoretical uncertainty on the cross section was obtained by adding these two contributions in quadrature. Note that large nonperturbative effects, similar to Fig. 2, are implicitly present in this FIG. 2. Nonperturbative correction factor applied to parton-level NLO+NLL predictions, obtained from PYTHIA8 tune A14 as the ratio of the inclusive jet spectrum at hadron-level with MPI compared to parton-level without MPI. prediction as well. The POWHEG+PYTHIA8 predictions are consistent with the measured data for all Rand pT,jet. Figure 1 does not include predictions by PYTHIA alone, since it is well established that NLO contributions are necessary to obtain the pp inclusive jet cross section [32,39]. Figure 3shows the pp jet cross section ratio for various R, built from the spectra in Fig. 1. The top two panels show the ratios of R=0.2 to other radii, and the bottom two panels show the ratios of R=0.1 to other radii. The left panels also include comparisons to POWHEG+PYTHIA8, and the right panels include comparisons to NLO+NLL+NP. Correlated uncertainties largely cancel [40,74], which allows this observable to elucidate higher precision effects compared to the inclusive jet cross section. The systematic uncertainties on the POWHEG+PYTHIA8 prediction largely cancel as well, and the resulting high-precision comparisons show that the cross-section ratios are generally well-described by POWHEG+PYTHIA8. The systematic uncertainties in the NLO+NLL+NP prediction, however, do not substantially cancel, because the scale variations include variation of softer scales which are sensitive to nonperturbative effects; the NLO+NLL+NP predictions are consistent with the measured data within the size of these large theoretical uncertainties. 2. 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Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine 3Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India 4Budker Institute for Nuclear Physics, Novosibirsk, Russia 5California Polytechnic State University, San Luis Obispo, California, USA 6Central China Normal University, Wuhan, China 7Centre de Calcul de l’IN2P3, Villeurbanne, Lyon, France 8Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 9Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico 10Centro Fermi, Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi,” Rome, Italy 11Chicago State University, Chicago, Illinois, USA 12China Institute of Atomic Energy, Beijing, China 13Chonbuk National University, Jeonju, Republic of Korea 14Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovakia 15COMSATS University Islamabad, Islamabad, Pakistan 16Creighton University, Omaha, Nebraska, USA 17Department of Physics, Aligarh Muslim University, Aligarh, India 18Department of Physics, Pusan National University, Pusan, Republic of Korea 19Department of Physics, Sejong University, Seoul, Republic of Korea 20Department of Physics, University of California, Berkeley, California, USA 21Department of Physics, University of Oslo, Oslo, Norway 22Department of Physics and Technology, University of Bergen, Bergen, Norway 23Dipartimento di Fisica dell’Università ’La Sapienza’ and Sezione INFN, Rome, Italy 24Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy 25Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy 26Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy 27Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy 28Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy 29Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy 30Dipartimento di Fisica “E. R. Caianiello” dell’Università and Gruppo Collegato INFN, Salerno, Italy 31Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy 32Dipartimento di Scienze e Innovazione Tecnologica dell’Università del Piemonte Orientale and INFN Sezione di Torino, Alessandria, Italy 33Dipartimento Interateneo di Fisica “M. Merlin” and Sezione INFN, Bari, Italy 34European Organization for Nuclear Research (CERN), Geneva, Switzerland 35Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia 36Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway 37Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 38Faculty of Science, P. J. Šafárik University, Košice, Slovakia 39Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 40Gangneung-Wonju National University, Gangneung, Republic of Korea 41Gauhati University, Department of Physics, Guwahati, India 42Helmholtz-Institut für Strahlenund Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany 43Helsinki Institute of Physics (HIP), Helsinki, Finland 44High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico 45Hiroshima University, Hiroshima, Japan 46Hochschule Worms, Zentrum für Technologietransfer und Telekommunikation (ZTT), Worms, Germany 47Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 48Indian Institute of Technology Bombay (IIT), Mumbai, India 49Indian Institute of Technology Indore, Indore, India 034911-19 S. ACHARYA et al. PHYSICAL REVIEW C 101, 034911 (2020) 50Indonesian Institute of Sciences, Jakarta, Indonesia 51INFN, Laboratori Nazionali di Frascati, Frascati, Italy 52INFN, Sezione di Bari, Bari, Italy 53INFN, Sezione di Bologna, Bologna, Italy 54INFN, Sezione di Cagliari, Cagliari, Italy 55INFN, Sezione di Catania, Catania, Italy 56INFN, Sezione di Padova, Padova, Italy 57INFN, Sezione di Roma, Rome, Italy 58INFN, Sezione di Torino, Turin, Italy 59INFN, Sezione di Trieste, Trieste, Italy 60Inha University, Incheon, Republic of Korea 61Institut de Physique Nucléaire d’Orsay (IPNO), Institut National de Physique Nucléaire et de Physique des Particules (IN2P3/CNRS), Université de Paris-Sud, Université Paris-Saclay, Orsay, France 62Institute for Nuclear Research, Academy of Sciences, Moscow, Russia 63Institute for Subatomic Physics, Utrecht University/Nikhef, Utrecht, Netherlands 64Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovakia 65Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 66Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 67Institute of Space Science (ISS), Bucharest, Romania 68Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 69Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico 70Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 71Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 72iThemba LABS, National Research Foundation, Somerset West, South Africa 73Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 74Joint Institute for Nuclear Research (JINR), Dubna, Russia 75Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 76KTO Karatay University, Konya, Turkey 77Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 78Lawrence Berkeley National Laboratory, Berkeley, California, USA 79Lund University Department of Physics, Division of Particle Physics, Lund, Sweden 80Nagasaki Institute of Applied Science, Nagasaki, Japan 81Nara Women’s University (NWU), Nara, Japan 82National and Kapodistrian University of Athens, School of Science, Department of Physics, Athens, Greece 83National Centre for Nuclear Research, Warsaw, Poland 84National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India 85National Nuclear Research Center, Baku, Azerbaijan 86National Research Centre Kurchatov Institute, Moscow, Russia 87Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 88Nikhef, National Institute for Subatomic Physics, Amsterdam, Netherlands 89NRC Kurchatov Institute IHEP, Protvino, Russia 90NRC Kurchatov Institute ITEP, Moscow, Russia 91NRNU Moscow Engineering Physics Institute, Moscow, Russia 92Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom 93Nuclear Physics Institute of the Czech Academy of Sciences, ˇ Rež u Prahy, Czech Republic 94Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA 95Ohio State University, Columbus, Ohio, USA 96Petersburg Nuclear Physics Institute, Gatchina, Russia 97Physics Department, Faculty of Science, University of Zagreb, Zagreb, Croatia 98Physics Department, Panjab University, Chandigarh, India 99Physics Department, University of Jammu, Jammu, India 100Physics Department, University of Rajasthan, Jaipur, India 101Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen, Germany 102Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 103Physik Department, Technische Universität München, Munich, Germany 104Politecnico di Bari, Bari, Italy 105Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany 106Rudjer Boškovi´c Institute, Zagreb, Croatia 107Russian Federal Nuclear Center (VNIIEF), Sarov, Russia 034911-20 MEASUREMENTS OF INCLUSIVE JET SPECTRA IN pp … PHYSICAL REVIEW C 101, 034911 (2020) 108Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India 109School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 110Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru 111Shanghai Institute of Applied Physics, Shanghai, China 112St. Petersburg State University, St. Petersburg, Russia 113Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria 114SUBATECH, IMT Atlantique, Université de Nantes, CNRS-IN2P3, Nantes, France 115Suranaree University of Technology, Nakhon Ratchasima, Thailand 116Technical University of Košice, Košice, Slovakia 117Technische Universität München, Excellence Cluster “Universe,” Munich, Germany 118The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 119The University of Texas at Austin, Austin, Texas, USA 120Universidad Autónoma de Sinaloa, Culiacán, Mexico 121Universidade de São Paulo (USP), São Paulo, Brazil 122Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 123Universidade Federal do ABC, Santo Andre, Brazil 124University of Cape Town, Cape Town, South Africa 125University of Houston, Houston, Texas, USA 126University of Jyväskylä, Jyväskylä, Finland 127University of Liverpool, Liverpool, United Kingdom 128University of Science and Techonology of China, Hefei, China 129University of South-Eastern Norway, Tonsberg, Norway 130University of Tennessee, Knoxville, Tennessee, USA 131University of the Witwatersrand, Johannesburg, South Africa 132University of Tokyo, Tokyo, Japan 133University of Tsukuba, Tsukuba, Japan 134Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 135Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, Lyon, France 136Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France 137Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire (DPhN), Saclay, France 138Università degli Studi di Foggia, Foggia, Italy 139Università degli Studi di Pavia, Pavia, Italy 140Università di Brescia, Brescia, Italy 141Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India 142Warsaw University of Technology, Warsaw, Poland 143Wayne State University, Detroit, Michigan, USA 144Westfälische Wilhelms-Universität Münster, Institut für Kernphysik, Münster, Germany 145Wigner Research Centre for Physics, Budapest, Hungary 146Yale University, New Haven, Connecticut, USA 147Yonsei University, Seoul, Republic of Korea aPresent address: Dipartimento DET del Politecnico di Torino, Turin, Italy. bPresent address: M. V. Lomonosov Moscow State University, D. V. Skobeltsyn Institute of Nuclear, Physics, Moscow, Russia. cDeceased. dPresent address: Department of Applied Physics, Aligarh Muslim University, Aligarh, India. ePresent address: Institute of Theoretical Physics, University of Wroclaw, Poland. 034911-21