Measurement of charged jet cross section in pp collisions at √s = 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/ Measurement of charged jet cross section in pp collisions at √s = 5.02 TeV © 2019 CERN, for the ALICE Collaboration Published version ALICE Collaboration ALICE Collaboration. (2019). Measurement of charged jet cross section in pp collisions at √s = 5.02 TeV. Physical Review D, 100(9), Article 092004. https://doi.org/10.1103/PhysRevD.100.092004 2019 Measurement of charged jet cross section in pp collisions at ffiffi s p=5.02 TeV S. Acharya et al.* (A Large Ion Collider Experiment Collaboration) (Received 3 July 2019; published 13 November 2019) The cross section of jets reconstructed from charged particles is measured in the transverse momentum range of 5<p T<100 GeV=c in pp collisions at the center-of-mass energy of ffiffiffi s p¼5.02 TeV with the ALICE detector. The jets are reconstructed using the anti-kTalgorithm with resolution parameters R¼0.2, 0.3, 0.4, and 0.6 in the pseudorapidity range jηj<0.9−R. The charged jet cross sections are compared with the leading-order (LO) and to next-to-leading-order (NLO) perturbative quantum chromodynamics (pQCD) calculations. It is found that the NLO calculations agree better with the measurements. The cross section ratios for different resolution parameters are also measured. These ratios increase from low pTto high pTand saturate at high pT, indicating that jet collimation is larger at high pTthan at low pT. These results provide a precision test of pQCD predictions and serve as a baseline for the measurement in Pb-Pb collisions at the same energy to quantify the effects of the hot and dense medium created in heavy-ion collisions at the LHC. DOI: 10.1103/PhysRevD.100.092004 I. INTRODUCTION In quantum chromodynamics (QCD), jets are defined as collimated showers of particles resulting from the fragmentation of hard (high momentum transfer Q) partons (quarks and gluons) produced in short-distance scattering processes. Jet cross section measurements provide valuable information about the strong coupling constant, αs, and the structure of the proton [1,2]. In addition, inclusive jet production represents a background to many other processes at hadron colliders. Therefore, the predictive power of fixed-order perturbative QCD (pQCD) calculations of jet production is relevant in many studies in high-energy collisions, and the inclusive jet cross section measurements in proton-proton collisions provide a clean test of pQCD. Jet production in eþe−,ep,p¯ p, and pp collisions is quantitatively described by pQCD calculations [3–5]. Jets also constitute an important probe for the study of the hot and dense QCD matter created in high-energy collisions of heavy nuclei. In nucleus-nucleus (A-A) collisions, high-pTpartons penetrate the colored medium and lose energy via induced gluon radiation and elastic scattering (see Ref. [6] and references therein), while in protonnucleus (p-A) collisions, jet production may be modified by cold nuclear matter (CNM) effects [7–10]. Furthermore, in high-multiplicity pp and p-Acollisions, jet production could be modified even if the collision system is small. The measurements of inclusive jets in pp collisions thus provide a baseline for similar measurements in A-A, p-A, and high-multiplicity pp and p-Acollisions. The measured jet properties are typically well reproduced by many general-purpose Monte Carlo (MC) event generators [11]. The unprecedented beam energy achieved at the Large Hadron Collider (LHC) [12] in pp collisions enables an extension of the energy range of the jet production cross section and property measurements carried out at lower energies. Such measurements enable further tests of QCD and help in the tuning of MC event generators. Inclusive jet production cross sections have been measured in collisions of hadrons at the Sp¯ pS and Tevatron colliders at various center-of-mass energies. The latest and most precise results at ffiffiffi s p¼1.96 TeV are detailed in Refs. [13,14]. At the LHC at CERN, the ALICE, ATLAS and CMS Collaborations have measured inclusive jet cross sections in proton-proton collisions at center-ofmass energies of ffiffiffi s p¼2.76 TeV [15–17],7TeV[18,19], and 8 TeV [20,21]. Recently, the ATLAS and CMS Collaborations have measured the inclusive jet cross sections at ffiffiffi s p¼13 TeV [22,23]. This paper presents the measurements of the inclusive charged jet cross sections in proton-proton collisions at a center-of-mass energy of ffiffiffi s p¼5.02 TeV by the ALICE Collaboration at the LHC. The inclusive charged jet cross sections are measured double-differentially as a function of the jet transverse momentum, pT, and the absolute jet pseudorapidity, jηj. The jets are reconstructed using the anti-kTjet clustering algorithm [24] with resolution *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. PHYSICAL REVIEW D 100, 092004 (2019) 2470-0010=2019=100(9)=092004(22) 092004-1 © 2019 CERN, for the ALICE Collaboration parameter values of R¼0.2, 0.3, 0.4, and 0.6. The inclusive charged jet cross sections are measured in the kinematic region of 5<p T<100 GeV=c and a pseudorapidity of jηj<0.9−R. The analysis is restricted to jets reconstructed solely from charged particles, hereafter called “charged jets.”Charged particles with momenta down to pT>0.15 GeV=c are used in the jet reconstruction of different Rvalues, thereby allowing us to test perturbative and nonperturbative aspects of jet production and fragmentation as implemented in MC event generators [25,26]. ALICE reported similar measurements of charged jet production in pp [27,28],p-Pb [29,30], and Pb-Pb collisions [31] using data from the first LHC run. A brief description of the ALICE detector and the selected data sample are introduced in Sec. II.MC simulations and theoretical calculations used for comparison to data are presented in Sec. III. The cross section definition is given in Sec. IV, and the unfolding procedure is described in Sec. V. Systematic uncertainties on the cross section measurements are addressed in Sec. VI. Finally, the results without and with underlying event (UE) subtraction are presented and discussed in Sec. VII and in the Appendix, respectively. II. EXPERIMENTAL SETUP AND DATA SAMPLE ALICE (A Large Ion Collider Experiment) is a dedicated heavy-ion experiment at the LHC, CERN. A detailed description of the detectors can be found in Ref. [32]. The detector components used in the data analysis presented in this publication are outlined here. The ALICE detector comprises a central barrel (pseudorapidity jηj<0.9coverage over full-azimuth) immersed in a uniform 0.5 T magnetic field along the beam axis (z) supplied by the large solenoid magnet. The forwardrapidity plastic scintillator counters are positioned on each side of the interaction point, covering pseudorapidity ranges 2.8<η<5.1(V0A) and −3.7<η<−1.7(V0C), and they are used for determination of the interaction trigger. The central barrel contains a set of tracking detectors: a six-layer high-resolution silicon inner tracking system (ITS) surrounding the beam pipe [from inside outward: the silicon pixel (SPD), drift (SDD), and strip (SSD) detectors], and a large-volume (5 m length, 5.6 m diameter) time-projection chamber (TPC). The ITS and TPC space points are combined to reconstruct tracks from charged particles over a wide transverse momentum range (0.15 <p T<100 GeV=c). The selected tracks are required to have at least 70 TPC space points out of a maximum of 159 possible and more than 60% of the findable TPC space points based on the track parameters. For the best momentum resolution, at least three track hits are required to be located in the ITS. The primary vertex position is reconstructed from charged particle tracks as described in Ref. [33]. Only tracks originating from the primary vertex, called primary tracks, are used for jet reconstruction. These tracks are selected based on their distance of closest approach to the primary vertex of the interaction (smaller than 2.4 cm and 3.2 cm in the transverse plane and along the beam axis, respectively). To fully compensate the loss of tracking efficiency with the SPD dead areas and recover good momentum resolution, tracks without any hit in either of the two SPD layers, referred to as “hybrid tracks,”are also retained but constrained to the primary vertex [34]. The tracking efficiency estimated from a full simulation of the detector (see Sec. III) is 80% for pT>0.4GeV=c, decreasing to 60% at 0.15 GeV=c. The momentum resolution is better than 3% for hybrid tracks below 1GeV=c, and increases linearly up to 10% at pT¼100 GeV=c. The measurement presented here uses data from pp collisions at a center-of-mass energy of ffiffiffi s p¼5.02 TeV collected in 2015. During this period, minimum-bias (MB) events are selected using the high-purity V0-based MB trigger [35], which required a charged particle signal coincidence in the V0A and V0C arrays. The corresponding visible pp cross section was measured with the van der Meer technique to be 51.21.2mb [36]. During the intensity ramp up, the instantaneous luminosity delivered by the LHC was successively leveled to 2×1029 cm−2s−1 and 1030 cm−2s−1, resulting in interaction rates of 10 kHz and 50 kHz, respectively [37]. The track quality was checked, and it was found to be independent of interaction rates. Further selection of MB events for offline analysis is made by requiring a primary vertex position within 10 cm around the nominal interaction point to ensure full geometrical acceptance in the ITS for jηj<0.9. Pileup interactions are maintained at an average number of pp interactions per bunch, crossing below 0.06 through beam separation in the horizontal plane. Residual pileup events are rejected based on a multiple vertex finding algorithm using SPD information [34]. After event selection, a dataset of 103 ×106minimum-bias pp collisions corresponding to an integrated luminosity Lint ¼2nb−1is used. III. MONTE CARLO SIMULATION Monte Carlo (MC) event generators are used both for predictions of jet production to compare with data, and for simulations of detector performance for particle detection and reconstruction used to correct the measured distributions for instrumental effects. For the latter case, primary simulated events are generated with the PYTHIA 8[38] ( PYTHIA 8.125, M onash2013 tune [39]) MC generator. Then particles are transported through the simulated detector apparatus with GEANT 3.21 [40]. The simulated and real data are analyzed with the same reconstruction algorithms. The MC generators HERWIG [41,42] ( HERWIG 6.510) and PYTHIA 6( PYTHIA 6.425 and several UE tunes defined as everything accompanying an event but the hard scattering) [43] are used for variations of the detector response and S. ACHARYA et al. PHYS. REV. D 100, 092004 (2019) 092004-2 systematic investigations of the MC correction factors as well as jet fragmentation and hadronization patterns (as described in Sec. VI). For comparison with data, MCsimulated samples with different tunes from PYTHIA 6, PYTHIA 8, and POWHEG merged with PYTHIA 8for the parton shower and hadronization [44–47] are used. PYTHIA and HERWIG are both event generators based on leading-order (LO) pQCD calculations of matrix elements for 2→2reactions of parton-level hard scattering. However, each generator utilizes different approaches to describe the parton shower and hadronization processes. HERWIG makes angular ordering a direct part of the evolution process and thereby takes coherence effects into account in the emission of soft gluons. PYTHIA 6is based on transverse-momentum-ordered showers [48] in which angular ordering is imposed by an additional veto. In PYTHIA 6, the initial-state evolution and multiple partonparton interactions are interleaved into one common decreasing pTsequence. In PYTHIA 8, the final-state evolution is also interleaved with initial-state radiation and multiparton interactions. Hadronization in PYTHIA proceeds via string breaking as described by the Lund model [49], whereas HERWIG uses cluster fragmentation [50]. The PYTHIA Perugia tune variations, beginning with the central tune P erugia-0 [51], are based on LEP, Tevatron, and SPS data. The PYTHIA 6 P erugia-2011 family of tunes [51] belongs to the first generation of tunes that use LHC pp data at ffiffiffi s p¼0.9and 7 TeV. For the PYTHIA 8 M onash-2013 tune [39], data at ffiffiffi s p¼8and 13 TeV are also used. The PYTHIA 8 CUETP 8 M 1tune uses the parameters of the Monash tune and fits to the UE measurements performed by CMS [52]. The HERWIG generator and PYTHIA 6tunes used in this work utilize the CTEQ 5 L parton distribution functions (PDFs) [53]. The PYTHIA 8Monash tune uses the NNPDF 2.3 LO set [54]. The uncertainty on the PDFs has been taken into account by the variation of the final results for the respective uncertainty sets of the PDFs. The POWHEG framework, an event-by-event MC, is used for next-to-leading-order (NLO) pQCD calculations of 2→2and 2→3parton scattering at Oðα3 sÞ. The outgoing partons from POWHEG are passed to PYTHIA 8event by event where the subsequent parton shower is performed. Doublecounting of partonic configurations is inhibited by a matching scheme based on shower emission vetoing [55]. Contrary to fixed-order NLO calculations, the POWHEG MC approach has the advantage that the same selection criteria and jet finding algorithm can be used on the final-state particle level as are used in the analysis of the real data; in particular, only charged particles can be selected. For the comparison with the measured differential jet cross sections, the CT 14nlo PDF set is used [56]. The dominant uncertainty in the parton-level calculation is given by the choice of renormalization, μR, and factorization scale, μF. The default value is chosen to be μR¼μF¼ pTof the underlying Born configuration, here a 2→2 QCD scattering [44]. Independent variations by a factor of 2 around the central value are considered as the systematic uncertainty. For the POWHEG calculations, the PYTHIA 8 A 14 tune is used [57]. IV. INCLUSIVE CHARGED JET CROSS SECTION Jets are reconstructed from charged particles using the anti-kTjet clustering algorithm [58,59] with resolution parameters R¼0.2, 0.3, 0.4, and 0.6. The jet transverse momenta are calculated using a boost-invariant pTrecombination scheme as the scalar sum of their constituent transverse momenta. The bin-averaged differential inclusive charged jet cross section measured as a function of charged jet transverse momentum pch jet Tin bins of pseudorapidity is defined as d2σch jet dpTdηðpch jet TÞ¼ 1 Lint dNjets dpTdηðpch jet TÞ;ð1Þ where Lint is the integrated luminosity given in Sec. II and Njets is the number of jets reconstructed in bins of width dpTin transverse momentum and dηin pseudorapidity. One single bin of pseudorapidity jηj<0.9−Ris considered in this analysis because of the limited coverage of the ALICE central barrel. The measurements are performed in the kinematic range of 5<p ch jet T<100 GeV=c. Jets observed in pp collisions are inevitably affected by the underlying event (UE) activity originating from multiple parton interactions (MPIs), fragmentation of beam remnants, and initialand final-state radiation [60]. The UE can be characterized on an event-by-event basis by the amount of transverse momentum density ρUE in a “control region”cone of the same radius as the jet resolution parameter placed perpendicular to the leading jet axis, at the same pseudorapidity as the leading jet but offset by an azimuthal angle of π=2relative to the jet axis [27].To obtain the ρUE, we calculate the sum of the track pTin a perpendicular cone which is defined with respect to a leading jet axis and divided by jet area as ρUE ¼X n i¼0 pperp T;i =πR2;ð2Þ where Ris the jet resolution parameter and pperp T;i is the transverse momentum of the ith track in a perpendicular cone. The average ρUE as a function of the event scale defined by the leading jet pTis shown in Fig. 1for resolution parameters R¼0.2, 0.3, 0.4, and 0.6. The relative UE contribution increases with increasing jet transverse momentum. A steep rise of the UE activity in the transverse region is observed with increasing leading jet pTfollowed by a slower rise above 10 GeV=c, which suggests a weaker correlation with the hard process [61]. The average UE also MEASUREMENT OF CHARGED JET CROSS SECTION IN PP …PHYS. REV. D 100, 092004 (2019) 092004-3 has a weak dependence on jet finding resolution parameters. While the asymptotic value of hρUEiis located close to 1GeV=c for resolution parameters from 0.2 up to 0.4, it increases by 20% for R¼0.6, probably due to the contamination from jet regions which might arise for such a large cone size. Figure 1compares the data to the recent tunes of the PYTHIA MC event generators as a function of detector level jet pT. The measured transverse momentum density can be reproduced by different PYTHIA tunes within 5%, i.e., a slight underestimation from the M onash-2013 tune when approaching the slowly rising region. A similar observation was reported by an earlier publication of UE measurements using leading particles instead of jets [61]. All the observables studied in this paper are measured both with and without UE corrections, with the former presented in the Appendix, and the latter in the body of the paper. The impact of the UE subtraction on the inclusive jet spectrum can be seen in Fig. 11. A systematic uncertainty on the ρUE measurement was estimated to be 5% [27], resulting in a 2% uncertainty on the UE subtracted jet cross section at pch jet T¼5GeV=c and decreasing for higher jet transverse momentum. Furthermore, as a reference for constructing jet nuclear modification factors in Pb-Pb collisions [31,62], leading-track biased jet spectra are made available in the Appendix in Fig. 12. Finally, the differential inclusive charged jet cross sections are corrected for detector resolution and unfolded to the charged particle level (Sec. V) to allow for a direct comparison to theoretical predictions (Sec. VII). V. UNFOLDING OF DETECTOR EFFECTS The measurement of the steeply falling jet transverse momentum spectrum is affected by the imperfect track reconstruction efficiency and finite track momentum resolution of the detector. The inference of the true spectrum from the smeared one, a process usually called unfolding, requires construction of a detector response matrix. The jet production yields are corrected by the unfolding method [63], as implemented in the RooUnfold package [64]. A two-dimensional detector response matrix maps the transverse momentum of particle-level charged jets clustered from stable charged particles produced by a MC event generator (pjet;particle T) to the detector-level jets reconstructed from tracks after full GEANT 3-based detector simulation (pjet;detector T). The entries of the response matrix are computed by matching particleand detector-level jets geometrically, according to the distance d¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Δη2þΔϕ2 p between the jet axes. The anti-kTjet finding algorithm is used for both particle-level and detector-level jet reconstruction. )c (GeV/ ch jet, det T,Leading p 0 1020304050 )c (GeV/〉 UE ρ 〈 0 0.2 0.4 0.6 0.8 1 1.2 1.4 ALICE PYTHIA8(Monash 2013) + GEANT3 PYTHIA6(Perugia 2011) + GEANT3 R=0.2 (a) )c (GeV/ ch jet, det T,Leading p 0 1020304050 )c (GeV/〉 UE ρ 〈 0 0.2 0.4 0.6 0.8 1 1.2 1.4 = 5.02 TeVspp c > 0.15 GeV/ track T p | < 0.9 track η | | < 0.9-R jet η | R=0.3 (b) )c (GeV/ ch jet, det T,Leading p 0 1020304050 )c (GeV/〉 UE ρ 〈 0 0.2 0.4 0.6 0.8 1 1.2 1.4 R=0.4 (c) )c (GeV/ ch jet, det T,Leading p 0 1020304050 )c (GeV/ 〉 UE ρ 〈 0 0.2 0.4 0.6 0.8 1 1.2 1.4 R=0.6 (d) FIG. 1. Dependence of the average ρUE on leading jet transverse momentum at detector level compared with predictions from PYTHIA ( P erugia-2011 and M onash-2013 tunes) for the resolution parameter R¼0.2(a), 0.3 (b), 0.4 (c), and 0.6 (d). S. ACHARYA et al. PHYS. REV. D 100, 092004 (2019) 092004-4 The probability of reconstructing a charged jet at a given detector level pTas a function of the particle level pTis shown in Fig. 2(left) for charged jets with R¼0.4, demonstrating the detector response matrix. The probability distribution is derived from this detector response matrix and shown in Fig. 2(right) for four different pjet;particle Tintervals. The distributions have a pronounced peak at zero (pjet;detector T¼pjet;particle T). The tracking pT resolution induces upward and downward fluctuations with equal probability, whereas the finite detection efficiency of the charged particles results in an asymmetric response. In this analysis, an unfolding approach relying on a singular value decomposition (SVD) of the detector response matrix is used in order to reduce sizable statistical fluctuations that are introduced by instabilities in the inversion procedure [65]. This technique also produces a complete covariance matrix, along with its inverse, which allows for full uncertainty propagation. In addition, a Bayesian unfolding [63] was carried out for cross-check and systematic error assessments. Consistent results were obtained with both methods. To validate the unfolding process, and identify potential biases, closure tests are performed which compare the unfolded detector-level distribution to the particle-level truth in the MC simulation. Consistency of the unfolding procedure is also ensured by folding the solution to the detector level and comparing it to the uncorrected distribution used as input. No significant difference is found. )c (GeV/ jet,detector T p 0 20 40 60 80 100 120 140 160 180 200 )c (GeV/ jet,particle T p 0 20 40 60 80 100 120 140 160 180 200 3− 10 2− 10 1− 10 1 = 5.02 TeVspp PYTHIA8(Monash2013) + GEANT3 Charged Jets = 0.4R, T kAntic > 0.15 GeV/ track T p | < 0.9 track η | < 0.5, | jet η | jet,particle T p)/ jet,particle T pjet,detector T p ( 1−0.8−0.6−0.4−0.2−0 0.2 0.4 Probability density 5− 10 4− 10 3− 10 2− 10 1− 10 1 c < 10 GeV/ jet,particle T p 5 < c < 25 GeV/ jet,particle T p20 < c < 50 GeV/ jet,particle T p40 < c < 100 GeV/ jet,particle T p85 < = 5.02 TeVspp PYTHIA8(Monash2013) + GEANT3 Charged Jets = 0.4R, T kAntic > 0.15 GeV/ track T p | < 0.9 track η | < 0.5, | jet η | FIG. 2. Left: Detector response matrix for R¼0.4charged jets. Right: Probability distribution of the relative momentum difference of simulated ALICE detector response to charged jets in pp collisions at ffiffiffi s p¼5.02 TeV for four different pTintervals. Charged jets are simulated using PYTHIA 8 M onash-2013 and reconstructed with the anti-kTjet finding algorithm with R¼0.4. TABLE I. Summary of the systematic uncertainties for a selection of jet transverse momentum bins. Jet resoultion parameter Jet pTbin (GeV=c) Tracking efficiency (%) Track pT resolution (%) Unfolding (%) Normalization (%) Secondaries (%) Total (%) R¼0.25–6 1 negligible 1.4 2.3 2.4 3.7 20–25 2.6 negligible 2.3 2.3 2.2 4.7 40–50 5.2 negligible 3.8 2.3 2.5 7.3 85–100 10 negligible 7.8 2.3 2.6 13.1 R¼0.35–6 1.5 0.1 2.9 2.3 2.2 4.6 20–25 4.1 0.1 3.4 2.3 2.3 6.3 40–50 6.2 0.1 4.3 2.3 2.6 8.3 85–100 8.4 0.1 7.0 2.3 2.7 11.5 R¼0.45–6 0.9 1.9 1.9 2.3 2.1 4.2 20–25 3.7 1.9 1.8 2.3 2.4 5.6 40–50 5.4 1.9 2.5 2.3 2.5 7.2 85–100 7.5 1.9 4.5 2.3 2.8 9.6 R¼0.65–6 3.4 1 2.1 2.3 1.9 5.1 20–25 5.7 1 1.7 2.3 2.6 6.9 40–50 6.8 1 2.2 2.3 2.6 8 85–100 8.3 1 4.0 2.3 2.7 9.9 MEASUREMENT OF CHARGED JET CROSS SECTION IN PP …PHYS. REV. D 100, 092004 (2019) 092004-5 VI. SYSTEMATIC UNCERTAINTIES The various sources of systematic uncertainties and their corresponding estimates obtained in this study are summarized in Table Iand discussed in detail in the following sections. All systematic uncertainties listed in Table Iare considered as uncorrelated except the unfolding one. Therefore, these systematic uncertainties were treated separately and their respective contributions are added in quadrature. In the ratio of the measured cross sections for different radii, the uncertainties from the same source cancel out partially, and the remaining relative difference is taken as the systematic uncertainty on the ratio. The total uncertainty on the jet cross section ratio is determined by adding the remaining contributions from different resources in quadrature. A. Tracking efficiency and momentum resolution To evaluate the impact of the limited track reconstruction efficiency and momentum resolution on the jet cross sections, a fast detector response simulation is used to reduce computing time. The efficiency and resolution are varied independently, and a new response matrix is computed for each variation. The detector-level distributions are then unfolded, and the resulting differences are used as systematic uncertainties. The relative systematic uncertainty on tracking efficiency is estimated to be 3% based on the variations of track selection criteria. The track efficiency contributes a relative systematic uncertainty of up to 8% on the jet cross sections, since it introduces a reduction and smearing of the jet momentum scale. The systematic uncertainty of the jet cross sections due to the tracking efficiency uncertainty, which is the dominant source of uncertainty, increases with increasing jet pTand resolution parameter, while the systematic uncertainty due to momentum resolution is negligible with no pTdependence and a weak dependence on the jet resolution parameter. B. Unfolding The reconstructed jet transverse momentum spectra presented in this paper are unfolded using a detector response computed with the M onash2013 tune of the PYTHIA 8event generator [39]. This particular choice of MC event generator affects the detector response by influencing the correlation between the particleand detector-level quantities used to evaluate the response matrix. Such a MC event generator dependence is quantified by comparing the unfolded spectrum using the default response matrix and generator prior with those obtained with the HERWIG and PYTHIA 6event generators with P erugia-0 and P erugia-2011 tune [51]. This comparison is accomplished by using detector responses from fast simulation. The resulting uncertainty is on the order of 5%. The SVD unfolding method [65],whichisthedefault approach used in this analysis, is regularized by the choice of an integer-valued parameter, which separates statistically significant and nonsignificant singular values of the orthogonalized response matrix. The regularized parameter is tuned for each cone radius parameter, separately. To estimate the related systematic uncertainty, the regularization parameter is varied by 2around the optimal value. The unfolded results are stable against regularization parameter variations with a maximum deviation of 1% at high pT. Lastly, the SVD unfolded spectra are compared with the results obtained with the Bayesian unfolding method [63]. Within uncertainties, the solutions of both unfolding methods are consistent. The uncertainties discussed above are added in quadrature and referred to as the unfolding systematic uncertainty in Table I. C. Cross section normalization A systematic uncertainty on the integrated luminosity measurement of 2.3% [36] is propagated to the cross section as fully correlated across all pTbins. Therefore, it cancels out in the ratio of cross sections. D. Contamination from secondary particles Contamination from secondary particles produced by weak decays of strange particles (e.g., K0 Sand Λ), photon conversions, or hadronic interactions in the detector material, and decays of charged pions is significantly reduced by the requirement on the distance of closest approach of the tracks to the primary vertex point. The uncertainty due to the )c (GeV/ ch jet T p 0 20406080100 /GeV)c (mb ηd T p/dσ 2 d 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 = 0.2R = 0.3 (x 10)R = 0.4 (x 100)R = 0.6 (x 1000)R = 5.02 TeVspp T kAntic > 0.15 GeV/ track T p | < 0.9 track η | R| < 0.9 - jet η | ALICE FIG. 3. Charged jet differential cross sections in pp collisions at ffiffiffi s p¼5.02 TeV after detector effect corrections. Statistical uncertainties are displayed as vertical error bars. The total systematic uncertainties are shown as shaded bands around the data points. Data are scaled to enhance visibility. S. ACHARYA et al. PHYS. REV. D 100, 092004 (2019) 092004-6 secondary contribution corresponds to a jet transverse momentum scale uncertainty of 0.5% [27,28]. VII. RESULTS A. Charged jet cross sections The inclusive charged jet cross sections using the anti-kT jet finding algorithm in pp collisions at ffiffiffi s p¼5.02 TeV are fully corrected for detector effects and are presented in Fig. 3. In this study, the inclusive charged jet cross sections are reported for jet resolution parameters R¼0.2, 0.3, 0.4, and 0.6. The choice of Ris driven by which aspects of jet formation are investigated since the relative strength of perturbative and nonperturbative (hadronization and underlying event) effects on the jet transverse momentum distribution show a strong Rdependence [25]. Pseudorapidity ranges are limited to jηj<0.9−Rto avoid edge effects at the limit of the tracking detector acceptance. /GeV)c (mb η d T p/dσ 2 d 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 ALICE PYTHIA8 Monash 2013 PYTHIA8 A14 NNPDF2.3LO PYTHIA8 CUETP8M1 NNPDF2.3LO PYTHIA6 Perugia 2011 | < 0.7 jet η | = 0.2R T kAnti- (a) )c (GeV/ ch jet T p 10 2 10 MC / DATA 1 1.5 | < 0.6 jet η | = 0.3R T kAnti- (b) = 5.02 TeVspp c > 0.15 GeV/ track T p | < 0.9 track η | )c (GeV/ ch jet T p 10 2 10 /GeV)c (mb ηd T p/dσ 2 d 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 ALICE PYTHIA8 Monash 2013 PYTHIA8 A14 NNPDF2.3LO PYTHIA8 CUETP8M1 NNPDF2.3LO PYTHIA6 Perugia 2011 | < 0.5 jet η | = 0.4R T kAnti- (c) )c (GeV/ ch jet T p 10 2 10 MC / DATA 1 1.5 | < 0.3 jet η | = 0.6R T kAnti- (d) = 5.02 TeVspp c > 0.15 GeV/ track T p | < 0.9 track η | )c (GeV/ ch jet T p 10 2 10 FIG. 4. Comparison of the charged jet cross section to LO MC predictions for the resolution parameter R¼0.2(a), 0.3 (b), 0.4 (c), and 0.6 (d). Statistical uncertainties are displayed as vertical error bars. The systematic uncertainty on the data is indicated by a shaded band drawn around unity. The red lines in the ratio correspond to unity. MEASUREMENT OF CHARGED JET CROSS SECTION IN PP …PHYS. REV. D 100, 092004 (2019) 092004-7 The differential cross sections of charged jets reconstructed using different jet resolution parameters Rare compared with LO PYTHIA predictions in Fig. 4. Figure 5 shows the comparison with POWHEG predictions. The ratios of the MC distributions to measured data are shown in the bottom panels. The model predictions qualitatively describe the measured cross sections, but fail to reproduce the shape over the entire jet transverse momentum range. The comparison between data and models is similar to earlier measurements at a lower center-of-mass energy [66]. Although NLO corrections to inclusive single-jet production improve the LO prediction and the NLO predictions agree within 10% with the data in the studied phase space, the NLO prediction still disagrees with the data at the lowest kinematic phase space by up to 50%, with very large theoretical uncertainty at low transverse momentum, as shown in Fig. 5. At this low-pTregion below 10 GeV=c, nonperturbative effects, such as soft particle production, /GeV)c (mb ηd T p/dσ 2 d 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 ALICE POWHEG + PYTHIA8 PYTHIA Tune: A14 NNPDF2.3LO | < 0.7 jet η | = 0.2R T kAnti- (a) POWHEG systematics PDF uncertainty Scale uncertainty )c (GeV/ ch jet T p 10 2 10 MC / DATA 1 1.5 | < 0.6 jet η | = 0.3R T kAnti- (b) = 5.02 TeVspp c > 0.15 GeV/ track T p | < 0.9 track η | )c (GeV/ ch jet T p 10 2 10 /GeV)c (mb ηd T p/d σ 2 d 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 ALICE POWHEG + PYTHIA8 PYTHIA Tune: A14 NNPDF2.3LO | < 0.5 jet η | = 0.4R T kAnti- (c) POWHEG systematics PDF uncertainty Scale uncertainty )c (GeV/ ch jet T p 10 2 10 MC / DATA 1 1.5 | < 0.3 jet η | = 0.6R T kAnti- (d) = 5.02 TeVspp c > 0.15 GeV/ track T p | < 0.9 track η | )c (GeV/ ch jet T p 10 2 10 FIG. 5. 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Caianiello’dell’Universit`a, Salerno, Italy 30bGruppo Collegato INFN, Salerno, Italy 31Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy 32Dipartimento di Scienze e Innovazione Tecnologica dell’Universit`a del Piemonte Orientale and INFN Sezione di Torino, Alessandria, Italy 33aDipartimento Interateneo di Fisica ‘M. Merlin’, Bari, Italy 33bSezione 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, 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 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, Republic of Korea S. 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D 100, 092004 (2019) 092004-20 61Institut de Physique Nucl´eaire d’Orsay (IPNO), Institut National de Physique Nucl´eaire et de Physique des Particules (IN2P3/CNRS), Universit´e de Paris-Sud, Universit´e Paris-Saclay, Orsay, France 62Institute for Nuclear Research, Academy of Sciences, Moscow, Russia 63Institute for Subatomic Physics, Utrecht University/Nikhef, Utrecht, Netherlands 64Institute for Theoretical and Experimental Physics, Moscow, Russia 65Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovakia 66Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 67Institute of Physics of the Czech Academy of Sciences, Czech Republic 68Institute of Space Science (ISS), Bucharest, Romania 69Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 70Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de M´exico, Mexico City, Mexico 71Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 72Instituto de Física, Universidad Nacional Autónoma de M´exico, Mexico City, Mexico 73iThemba LABS, National Research Foundation, South Africa 74Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 75Joint Institute for Nuclear Research (JINR), Dubna, Russia 76Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 77KTO Karatay University, Konya, Turkey 78Laboratoire de Physique Subatomique et de Cosmologie, Universit´e Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 79Lawrence Berkeley National Laboratory, Berkeley, California, USA 80Lund University Department of Physics, Division of Particle Physics, Lund, Sweden 81Nagasaki Institute of Applied Science, Nagasaki, Japan 82Nara Women’s University (NWU), Nara, Japan 83National and Kapodistrian University of Athens, School of Science, Department of Physics, Athens, Greece 84National Centre for Nuclear Research, Warsaw, Poland 85National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India 86National Nuclear Research Center, Baku, Azerbaijan 87National Research Centre Kurchatov Institute, Moscow, Russia 88Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 89Nikhef, National institute for subatomic physics, Amsterdam, Netherlands 90NRC Kurchatov Institute IHEP, Protvino, 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, ˇRežu 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 BoškovićInstitute, Zagreb, Croatia 107Russian Federal Nuclear Center (VNIIEF), Sarov, Russia 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, Universit´e de Nantes, CNRS-IN2P3, Nantes, France 115Suranaree University of Technology, Nakhon Ratchasima, Thailand MEASUREMENT OF CHARGED JET CROSS SECTION IN PP …PHYS. 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D 100, 092004 (2019) 092004-21 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, Krakow, 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 College of Southeast Norway, Tonsberg, Norway 125University of Cape Town, South Africa 126University of Houston, Houston, Texas, USA 127University of Jyväskylä, Jyväskylä, Finland 128University of Liverpool, Liverpool, United Kingdom 129University of Science and Techonology of China, Hefei, China 130University of Tennessee, Knoxville, Tennessee, USA 131University of the Witwatersrand, Johannesburg, South Africa 132University of Tokyo, Tokyo, Japan 133University of Tsukuba, Tsukuba, Japan 134Universit´e Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 135Universit´e de Lyon, Universit´e Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, Villeurbanne, Lyon, France 136Universit´e de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France 137Universit´e Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, D´epartment de Physique Nucl´eaire (DPhN), Saclay, France 138Universit`a degli Studi di Foggia, Foggia, Italy 139Universit`a degli Studi di Pavia, Pavia, Italy 140Universit`a 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, Hungarian Academy of Sciences, Budapest, Hungary 146Yale University, New Haven, Connecticut, USA 147Yonsei University, Seoul, Republic of Korea aDeceased. bAlso at Dipartimento DET del Politecnico di Torino, Turin, Italy. cAlso at M. V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear, Physics, Moscow, Russia. dAlso at Department of Applied Physics, Aligarh Muslim University, Aligarh, India. eAlso at Institute of Theoretical Physics, University of Wroclaw, Poland. S. ACHARYA et al. PHYS. REV. D 100, 092004 (2019) 092004-22