Measurements of the charm jet cross section and nuclear modification factor in pPb collisions at $\sqrt{{s}_{NN}}$ = 5.02 TeV

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Physics

Letters

B

www.elsevier.com/locate/physletb

Measurements

of

the

charm

jet

cross

section

and

nuclear

modification

factor

in

pPb

collisions

at

√

s

_NN

=

5

.

02 TeV

.

The

CMS

Collaboration



CERN,Switzerland

a

r

t

i

c

l

e

i

n

f

o

a

b

s

t

r

a

c

t

Articlehistory:

Received28December2016 Receivedinrevisedform12May2017 Accepted19June2017

Availableonline23June2017 Editor: M.Doser Keywords: CMS Heavyions Charm-tagging Heavy-flavor

The CMS Collaboration presents the first measurement of the differential cross section of jets from charm quarks produced in proton–lead (pPb) collisions at a nucleon–nucleon center-of-mass energy of √sNN=

5.02 TeV, as well as results from charm quark jets in proton–proton (pp) collisions at √s=2.76 and

5.02 TeV. By comparing the yields of the pPb and pp collision systems at the same energy, a nuclear modification factor for charm jets from 55 to 400 GeV/c in pPb collisions at √sNN=5.02 TeV of RpA=

0.92 ±0.07 (stat)±0.11 (syst) is obtained. This is consistent with an absence of final-state energy loss for

charm quarks in pPb collisions. In addition, the fraction of jets coming from charm quarks is found to be consistent with that predicted by pythia 6 for pp collisions at √s=2.76 and 5.02 TeV, and is independent of the jet transverse momentum from 55 to 400 GeV/c.

©2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3_.

1. Introduction

The creationof a newstate of matter, known asquark–gluon plasma(QGP),hasbeenpredictedbylatticecalculationsforstates of matter withextremely highenergy densities [1]. Collisions of heavy nuclei studied atboth the BNLRHIC andCERN LHC facil-ities have been observed to create energy densities larger than thatrequiredforQGPcreation[2–5].TheQGPisastate ofmatter whichischaracterizedbyaneffectivedeconfinementofthequark andgluoncolordegreesoffreedom.Hard-scatteredpartonsare ex-pectedtoloseenergyvia elasticandinelasticinteractionsasthey traverse theQGP[6].Thisis commonlythoughtto be the mech-anismresponsiblefortheobservedsuppressionofhightransverse momentum (pT) hadronsandjets, or“jet quenching”, innuclear collisions[2,7–13].

Jetquenchingisexpectedto dependontheflavor ofthe frag-menting parton[14,15],primarily dueto two effects: first,heavy quarksmaysuffermass-dependenteffectsfurtherseparatingtheir energylossmeasurementsfromthoseofinclusivejets. For exam-ple, it is expected that the radiative and collisional energy loss mechanismsshould have differentstrengths forheavy quark and light quarkjets[16,17]. Therefore,heavy quarkscanprovide new informationon therelative jet quenchingpower ofthesevarious energylossmechanisms.Second,apureheavyflavoredjetsample doesnotgenerallycontainjetsseededbyhigh-pT gluons,contrary

 _{E-mailaddress:cms-publication-committee-chair@cern.ch.}

toameasurementofinclusivejets,whichcontainsasizable gluon-jet componentaspredictedby pythia[18]simulations.Underthe assumption that gluon radiation is the dominant mechanism for energyloss,gluonjetsareexpectedtoquenchmorestronglythan quarkjets,owingtothelargercolorfactorforgluonemissionfrom gluons than fromquarks [19]. By identifying charm and bottom jets(candbjets),measurements canbeperformedonajet sam-plewithanenhancedfractionofquarkjets.

The energy loss discrimination power ofboth effects is miti-gatedsomewhatduetothepresenceofgluonsplitting,whichisa next-to-leadingorderheavyquarkproductionmechanismwherea high-energygluoncansplitintoaquarkpair.Athigh-pT,theheavy flavoredquarkproductionfractionfromgluonsplittingisexpected to be roughly 50% [20], but as the gluon virtuality is also quite large,itmaybethecasethatthequarksfromgluonsplittingstill experiencethemajorityoftheQGPmediumevolution.

The CMS Collaboration has also previously observed QGP ef-fects on heavy-flavored objects through measurements of fully-reconstructed mesons [21]. While mesonmeasurements are able toaccessthelow-pTregimeinamoreeffectivewaythanjets,the measurementsarelessdirectasaresultofthefragmentation pro-cess.Inotherwords,theconnectiontotheborcquarkenergyloss issmearedbyitscombinationwithalightquarktocreatethe re-constructed object,whereas jetsaimtocapturetheentireenergy ofthefragmentingquark.

Previous measurements of jetsin proton–lead (pPb)collisions have not observed significant jet quenching effects [22–25], sug-gestingthatmeasurementsfrompPbcollisionscanplacelimitson

http://dx.doi.org/10.1016/j.physletb.2017.06.053

0370-2693/©2017TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).Fundedby SCOAP3_.

theextentof“coldnuclearmatter” effectsonjetproduction[26]. One such initial-state effect is due to the nuclear parton distri-bution functions (nPDFs). These nPDFs are expected to enhance thecharm quark yields by roughly 10–15%, asthe kinematic se-lections used in this analysis correspond to the “antishadowing” region ofthe Bjorken-x distribution [27]. While the modification factorsRpAforbothbjets[28]andinclusivejets[23]atanucleon– nucleoncenterofmassenergyof

√

sNN

=

5

.

02TeV havebeen mea-suredby CMS,thesemeasurements useda pythia simulationand an interpolated pp reference asbaselines, respectively, as at the timeofpublication,no5.02 TeV proton–proton(pp)datawas avail-able.Thisanalysispresentsthe firstmeasurementof aninclusive charmjet crosssection inpPb collisions at

√

sNN

=

5

.

02TeV, in-cludingcomparisonstothecrosssectionsinppcollisions atboth

√

s

=

2

.

76 and5.02 TeV.

2. Detection, reconstruction, and simulation

2.1.Detection

The CMS detector has excellent capabilities to perform dis-placed jet identification (b and c tagging) as demonstrated in Ref.[29].Thecentral featureoftheCMSapparatusisa supercon-ducting solenoid of 6 m internal diameter, providing a magnetic field of3.8 T. Withinthe solenoidvolume are asilicon pixeland strip tracker, a lead tungstate crystal electromagnetic calorime-ter,and a brass and scintillator hadron calorimeter(HCAL), each composedofabarrelandtwo endcapsections.Extensiveforward calorimetrycomplementsthecoverageprovidedbythebarreland endcap detectors. The tracker has a pseudorapidity coverage of

|

η

lab

|

<

2

.

4, while the calorimetry covers

|

η

lab

|

<

3. Muons are measuredingas-ionization detectorsembedded inthe steel flux-returnyoke outside the solenoid. A more detaileddescription of theCMSdetector,togetherwithadefinitionofthecoordinate sys-tem used and the relevant kinematic variables, can be found in Ref.[30].

Eventselectionsare identicalto previous pPb analyses[23,28, 31]andincludetherequirementofaprimaryvertexwithin15 cm of the nominal interaction point in the beam direction and the removalofeventsconsistingprimarilyofHCALnoise.Beam-related backgroundis suppressed by rejecting events in which lessthan 25%ofallreconstructedtracksareofgoodquality.

2.2.Reconstruction

Jets are reconstructed offline using the particle-flow algo-rithm[32],whichidentifieseach individualjet constituentasone of a number of different particle types, including photons, elec-trons,muons, chargedhadrons,andneutralhadrons.Thisis done usingan optimized combination ofinformation fromthe various elementsoftheCMSdetector[33].Theseparticle-flowcandidates do not have explicit kinematic selections, though charged tracks arelimitedtopT

>

400 MeV.Jetsareclusteredbytheanti-kT algo-rithm[34]witharadiusof0.3.Jetenergycorrectionsare derived fromsimulationandusingmeasurementsofenergybalancein di-jet and photon+jet events. Finally, an iterative underlying event removalprocedure is applied to jets inpPb events [35]. Jet mo-mentumisfoundfromsimulationtobe within2% ofthetruejet momentumoverthe whole pT spectrum anddetectoracceptance afterthe jet energy correctionsare applied for both pp andpPb collisions.Thisresidualnonclosureisprimarilyduetodifferingjet energyresolutionbetweenquarkandgluonjets.

Threedifferentdatasets collected by theCMSexperimentare used,corresponding to integrated luminositiesof 35 nb−1 ofpPb collisions at

√

sNN

=

5

.

02 TeV and 4.8 pb−1 of pp collisions at

√

s

=

2

.

76TeV takenduring the2013heavyionrunperiodatthe LHC, as well as 27.9 pb−1 of pp collisions at

√

s

=

5

.

02 TeV col-lectedduring the2015heavy ionrunperiod.DuringthepPbrun, the proton and lead beam energies per nucleon were different, which led to a center-of-mass pseudorapidity (η) shift of 0.465 unitswithrespecttothelaboratoryframe.Afteranintegrated lu-minosityof 20.9 nb−1 was collected, thedirections ofthe proton andleadbeamswerereversed.Inthisanalysis,thebeam parame-tersareredefinedsuchthattheprotonbeamisalwaystravelingin thepositive

η

direction. Therefore,the laboratoryandthe center-of-masspseudorapiditiesarerelatedas

η

lab

=

η

CM

+

0

.

465.

As jet energy corrections are only reliable for pT

>

20 GeV/c, single jets are required to havea rawonline pT above that cut-off and a fully-corrected pT

>

35 GeV/c. In order to mitigate ef-fectsfromthelimitedCMSinnertracker

η

acceptanceof

|

η

lab

|

<

2

.

4 andthe boost between thelab and center-of-massreference frames, jets in pPb collisions are required to be reconstructed within

|

η

CM

|

<

1

.

5,whilejetsinppcollisionscanbefoundwithin

|

η

CM

|

<

2

.

0.Whendirectcomparisonsofquantitiesinpp andpPb collisions areshown,jetsfrombothsystemsuseapseudorapidity selectionof

|

η

CM

|

<

1

.

5.

Events are selected online by one or more jet triggers with varying energy thresholds. In the 2.76 TeV pp and 5.02 TeV pPb analysis, five single-jettriggers with pT thresholds of20, 40,60, 80,and 100 GeV/c arecombinedin order tomaximize the num-berofacceptedeventsoverawiderangeofjet pT.Assomelower

pT triggersareprescaled, meaningthatafractionofthetriggered eventsarerandomlyrejectedtoconstraindatathroughput,a sim-ple OR of all triggers will bias the jet pT spectrum toward the largerthresholdtriggersandwillalsohavesignificantevent dupli-cation.Instead,atriggercombinationprocedurebasedonthe trig-gerprescalefactorsisused.Thistriggercombinationisalsousedin theanalysisofbjetsinpPb[28]andisbrieflydescribedhere.The jetwiththelargestonlineraw pT,i.e.the pT usedbythetriggers beforejetenergycorrections,isusedtoclassifyeachevent.Based on thisonlinerawjet pT, itis possibleto deducewhichtriggers havebeensatisfied,irrespectiveofwhetheratriggerisprescaled. Ifthehighestfiredtriggerconditionsaresatisfied,theeventiskept andweightedbythecorrespondingtriggerprescalefactor,elsethe eventisdiscarded.Afterthiscombination,thejetfindingefficiency ofthefullsample is

>

99.9%forjetsabove35 GeV/c,andthetotal eventselectionefficiencyisaround97%.

Forthe5.02 TeV ppdata,thetriggermenuwas slightlyaltered inpreparationforthehigherinstantaneousluminosityachievedin the 2015runperiod, soonly fourtriggers arecombined with pT thresholds of40,60,80,and100 GeV/c. Asa resultofjet energy smearingeffectsfromreconstructionandresolutionunfolding,the absence of a 20 GeV/c trigger effectively places a 55 GeV/c lower boundonthe leadingjet pT forthe 5.02 TeV ppdata,ratherthan theroughly40 GeV/c boundat2.76 TeV.

2.3. Simulation

This analysis relies on simulations of pp collisions at 2.76 and5.02 TeV,aswell assimulationsof pPbcollisions at5.02 TeV. Monte Carlo (MC) simulations of inclusive quantum chromody-namic (QCD) hard-scattering events are generated using pythia 6.424 [18], tune Z2 [36]. These events are generated imposing thresholds on the transverse momentum of the hard scattering subprocess(p

ˆ

T) inordertoforceproductionofjetswithhigh pT. Inordertoproperlybuildtemplates,unfoldthejetresolution,and calculatethetaggingefficiencyintheproton–nucleusenvironment, minimum-bias pPb events are produced using the hijing 1.383 event generator [37] at

√

sNN

=

5

.

02 TeV. Simulated events from pythia6areproducedat5.02 TeV inconjunctionwithapPb

back-Fig. 1. Efficiencyoftaggingbjets(left)andlightpartonjets(right)forthehigh-purity(3+track),andhigh-efficiency(2+track)versionsofthesimplesecondaryvertex(SSV) taggerasafunctionofcjettaggingefficiency.Thecharm-to-bottomdiscriminationpowerisvirtuallyunchangedbetweenthehigh-efficiencyandhigh-purityversionsofthe tagger,whilethelightpartonjetmistagrateisreducedbyafactorofthreeattheanalysisworkingpoint,shownastheclosedredcrossontheplots.(Forinterpretationof thereferencestocolorinthisfigure,thereaderisreferredtothewebversionofthisarticle.)

groundevent. In this way,each simulatedpPb eventcontains at leastonejet producedby ahard scatteringsubprocesswhilestill accuratelyrepresentingthejetresolutionandenergyscaleinapPb environment.Toaccount forpossibledifferencesinreconstruction performancebetweenthetwoboost directions,MC sampleswere obtainedforbothdirectionsoftheprotonbeam. Forppcollisions,

η

lab isidenticalto

η

CM.Jetsgeneratedbythe hijing simulationof theunderlyingpPbeventsare rejectedintheanalysissincethese jetscan be quenched [37],possibly resulting in a modified frag-mentation pattern which would bias the jet energy corrections. Withinthekinematicselections oftheanalysis, thejetsfrom hi-jing_{accountforlessthan1%ofthetotaljetfraction.}

3. Charm quark tagging

InMonteCarlostudies, acharm jet isdefinedasanyjet con-taining a prompt charm quark within the jet cone and ignoring jetswhichcontainab

→

ccascadedecay.Identificationofsuchjets isachievedby taggingverticesconsistent withdecays ofhadrons containing a charm quark. Even though the maximum displace-ment of such charmed hadron decays is only on the order of 100 μm forthekinematicselectionsofthisanalysis, thepresence ofa silicontrackerverycloseto theinteraction pointat CMS al-lowsforthediscrimination ofsecondaryverticeswithsuch small displacementvalues.Forproton–protoncollisions,individual track vertexinguncertainties inthebeamdirectionare ontheorder of 100 μm at1 GeV/c and 40 μm at10 GeV/c, whiletheuncertainties in the transverse directionare on the order of 70 μm at 1 GeV/c and20 μm at10 GeV/c [38].

ThiscjetanalysiscloselyfollowspreviousCMSanalysis strate-giesfor heavy-flavor jet identification, ortagging, specificallythe measurementsofbquark jetsinheavy ionenvironments inCMS, bothinlead–leadcollisions[39]andpPbcollisions[28].This analy-sisstrategyusestwodifferenttaggerstoidentifycjets.Whileboth taggersassign a numericaldiscriminator quantifying how“charm like”eachjetis,each taggerusesaslightlydifferentidentification strategy.Thefirsttaggerisknownasthesimplesecondaryvertex (SSV) tagger [29] and uses reconstructed displaced vertices. The versionoftheSSVtaggerusedinthisanalysisisthe“high-purity” (SSVHP)one,whichrequiresthepresenceofasecondaryvertexin thejetconewithatleastthreeassociatedtracks,eachwithtrack

pT

>

1 GeV/c.Allversionsrequirethatallsecondaryverticesshare fewer than20% of trackswithany other vertex. The inclusionof

thethird associatedvertextrackinthehigh-purityversion ofthe taggerallowsfortheselectionofataggingworkingpointthat re-duces themisidentification rateoflight jetsby a factorof three, while still keeping a largemajority of cjets, as showninFig. 1. With areducedlight jet contamination, cjetsbeginto dominate small regions of kinematic phase space, which this analysis ex-ploitstoextract relativeflavorcontributionsoflight,c,andbjets tothetotaljetsample.

The second tagger used in this analysis is known as the jet probability (JP)tagger[29],andisusedtocross-checkthetagging efficiency predictedby simulation usingcontrol samplesin data. Thistaggerusesanumericaldiscriminator basedonthepresence of single tracks that are significantly displaced from theprimary vertex,andisthereforelargelyuncorrelatedwithsecondaryvertex reconstruction performance. The efficiency of a particular tagger (e.g.SSVHP)canbecalculatedwiththeJPtagger:



tag

=

CcfctaggedNtaggedjets fcpretagNpretagjets

,

(1)

where fctagged isthe purityofthesample fromaJP discriminator template fitafterapplyingthe SSVHPdiscriminatorselection,and

fcpretag is the same but before thisselection, Njetspretag and N tagged jets denote the number ofjets before andafter tagging, respectively, andCc denotesthefractionofjetsthatcanbeidentifiedbytheJP

tagger(generallyveryclosetoone).

The taggingefficiency is calculated both from simulation and using distributions of the JP tagger [29] both before and after imposing the SSVHP taggingrequirement. Aunique advantage of using the JP tagger for calculating tagging efficiency via Eq. (1)

is that it can be calibrated using data to correct for the effects of tracking resolution.Tracks with negative values ofimpact pa-rametersignificance (i.e. tracks withvertexdisplacementson the away-side ofthevertexfromthejet)arepurelyaproduct of res-olutionsmearingandthesecanbeusedtocompute aprobability for theassociation of anygiven trackto theprimary vertex. The taggerdistributionsarecalibratedindependentlyindataand sim-ulationsuchthatthedistributionofnegativeimpactparametersis flat(byconstruction)asafunctionoftrackdisplacement.Through thecalibrationoftheJP tagger,theimpactparametersignificance distributions in both data and simulation are transformed from unboundedintoboundeddistributions,suchthatbothcanbe ana-lyzedonanequalfooting.Oncerecalibrated,theresidualdifference

betweenthetaggingefficiencyderived fromsimulation andfrom the JP calculation (Eq. (1)) is used as the systematicuncertainty estimation.

The c jet purity calculation relies on another discriminating variableknownasthecorrectedsecondary vertexmass.This was firstdevelopedasa toolforidentifyingbjetsbytheexperiments atLEP [40] andSLC[41] andisalsousedby theLHCb Collabora-tion[42].Themotivationbehindthisvariableistocorrectforany missing mass of the decay vertex due to neutral or unobserved particles.Ifthemomentumvectorofthecollectionofparticles as-sociatedtoavertexisnotparalleltothevectorpointingfromthe primaryvertextothesecondary vertexdecaypoint,i.e.theflight directionoftheconstituentparticles,onecan useconservationof momentumtocalculateaminimumpossiblemassthevertexmust havehad.Thisminimumpossiblemassiscalledthecorrected sec-ondaryvertexmass,orMcorr,andisdefinedas:

Mcorr

=



M2

_{+ (}

_p

_/

_c

₎

2_sin2

_θ

_{+ (}

_p

_/

_c

₎

_sin

_θ,

₍₂₎ whereM istheinvariantmassofthevertex, p isthemomentum ofthevectorsumofthereconstructedparticlesthatformthe sec-ondaryvertex,and

θ

isdefinedastheanglebetweenthatsummed momentumvectorandtheflightdirectionofthevertex.Ifall par-ticles that belong to a givensecondary vertexare reconstructed, theangle

θ

shouldbezero,andthesecondaryvertexmassneeds no correction. Otherwise, the value of Mcorr is used in the cal-culationof the vertex mass to account for the nonreconstructed momentum.

ThecjetpurityisfoundusingtemplatefitsofMcorr,afterusing the SSVHP tagger. The numerical values of the SSVHP discrimi-nator are correlated to the significance of the secondary vertex displacementwithrespecttotheprimaryvertexandareobtained usingtheformula:SSVHP

=

ln

(

1

+ |

d

|/

σ

(

d

))

,whered isthe three-dimensional vertex displacement and

σ

(

d

)

is the uncertainty in the displacement measurement. The working point used in this analysisrequires SSVHP

>

1.68, which maximizes the estimated cjetpurityfromtheMCsamples,increasing thecjetpurityfrom around 10% to around 30%. Once the working point selection is appliedtothesample,distributions ofcorrectedsecondary vertex massfromlightparton,c,andbjetsinthe pythia+hijing or pythia simulationsare fittodistributions indata.Theshapesofthe dif-ferentflavortemplatesarefixed,buttherelativenormalizationsof eachflavortemplateareallowedtofloatindependently.Asseenin

Fig. 2forpPbcollisions,andinFig. 3forppcollisionsat5.02 TeV, b jets dominate the Mcorr distributions for vertex masses above 3 GeV

/

c2_{, while the light parton jet contribution is significantly} reduced by the SSVHP tagger requirement. Because of this light partonjet removal, the relative c jet contribution to the sample below3 GeV

/

c2 isquitelarge,allowingforanaccurateextraction ofthecjetpurityinthedatasample.

Fig. 4showsthectaggingpurityandefficiencyofthe sample afterapplying the SSVHP tagger selection for 5.02 TeV pPb colli-sions,bothindataandsimulation.Fig. 5depictsthesamefor5.02 and2.76 TeV ppcollisions,again,bothindataandsimulation.

Oncetheefficiencyandpurityvaluesarefound,thetotal num-berofcjetsinthesampleisobtainedpT binby pTbinusing: Nc jets

=

N_jetstagged

fc



tag

,

(3)

where N_jetstagged is the numberof jets passing the SSVHP working pointselection, fc isagainthecjettaggingpurity,and



tag isthe taggingefficiency.Aftercorrectingfortaggingefficiencyandpurity, the cjet pT spectrum isobtained. Thisspectrum isthen passed

Fig. 2. Correctedsecondaryvertexmassdistributionsfrom pythia+hijing forcjets (green),lightpartonjets(blue),andbjets(red)inthejet pTrange55–80 GeV/c (upper)and120–170 GeV/c (lower).Relativenormalizationsofthesethree distribu-tionsarefittoadistributionfrompPbcollisiondata(black).Statisticaluncertainties areshowninblackfordataandforindividualsimulatedflavorcomponentsandare showninblueforthesumofthesimulateddistributions.Thebottompanelsofboth plotsshowtheratioofdatatosimulation.(Forinterpretationofthecolorsinthis figure,thereaderisreferredtothewebversionofthisarticle.)

throughasingular valuedecomposition(SVD) [43]unfolding pro-cedure,asimplementedbytheRooUnfold[44]package toremove thejetresolutioneffects.

4. Systematic uncertainties and cross checks

Systematicuncertainties for thisanalysisare divided intotwo primarycategories:charmtaggingandjetreconstruction.

4.1. Taggingsystematicuncertainties

Anumberofsystematicchecksonthecharm-taggedspectrum areconsidered,includingvaryingtheSSVHPworkingpoint, calcu-latingthectaggingefficiencyusingtheJP taggermethodinstead ofobtainingthevaluefromsimulation,varyingthegluonsplitting fractionintheMC sample,varyingtheMCtemplates withintheir statisticaluncertainties,andfinallyreweighting andvaryingtheD mesondecayparameterswithintheuncertaintiesoftheworld av-erageinthesimulation[45].

Thetaggerworkingpointisvariedoverthediscriminator work-ing pointregionwheretheuseofa discriminatorenhancesthec

Fig. 3. Correctedsecondaryvertexmassfroma pythia 6,tuneZ2simulationforc jets(green),lightpartonjets(blue)andbjets(red)inthejetpTrange55–80 GeV/c (upper)and120–170 GeV/c (lower).Relativenormalizationsofthesethree distribu-tionsarefittoadistributionfromppcollisiondata(black).Statisticaluncertainties areshown inblackfordataandfor individualsimulatedflavorcomponentsand areshowninblueforthesumofthesimulateddistributions.Thebottompanelsof bothplotsshowtheratioofdatatosimulation.(Forinterpretationofthecolorsin thisfigure,thereaderisreferredtothewebversionofthisarticle.)

jetpurity.Withavery loosediscriminator selection,thecjet pu-rityis slightly enhanced relative to an unbiased sample, while a verytight selection removesthegreatmajority ofbothlight par-tonandcjetssuchthat thebjetsdominatethesample. Thereis a narrow window in which the c jet purity is larger than in an unbiasedsample,correspondingtotheSSVHPdiscriminatorvalues between1.2and2.4. At itspeak, the SSVHPtaggerenhancesthe cjet purityfromaround 10% toaround 30%.To testthe stability ofthe SSVHP tagger, multipletemplate fits to the corrected sec-ondary vertexmass are performed, varying theworking point of thetaggerinstepsof0.2unitsoverthisrangeandcalculatingthe effectivestandarddeviationfromallworkingpointvariations.This leads toa 2–5%uncertainty, dependingon jet pT.An uncertainty is derived fromthe difference between the tagging efficiency as obtainedfromsimulationandviafits totheJPtagger discrimina-torfromEq.(1).Thedifferencesintaggingefficiencybetweenthe pythia6 estimationandusing theJP taggerstem primarily from statisticalfluctuation inthe templates, along with a slighteffect fromapolynomialsmoothingoftheseuncertaintiesasafunction of pT. Thesedifferences introducea 5–15% uncertainty,also asa functionof pT.

Fig. 4. Taggingpurity(upper)andefficiency(lower)fortheworkingpoint selec-tionofSSVHP>1.68 inpPbcollisionsat5.02 TeV forsimulation(openredsquares) anddata(closedblackpoints).(Forinterpretationofthereferencestocolorinthis figure,thereaderisreferredtothewebversionofthisarticle.)

Oneoftheprimarytheoreticalunknownsinheavy-flavorjetsis theimpactofhigher-ordercorrections,suchasgluonsplitting,and howtheseeffectsmanifestthemselvesinthesefits.Toaccountfor this, thegluonsplittingfractioninsimulationisvariedby50%up ordownandthedistributionsofcorrectedsecondaryvertexmass are refit to the modified MC templates, where both g

→

cc and g

→

bb splittingeventsareconsidered.Thenumericalvalueof50% is usedto coverobserveddiscrepancies acrossvarious MC gener-ators as well as discrepancies of MC generators to data, though these are primarily driven by b jet studies, where data is avail-able.The pythia 6 generatorshowsagluon splittingcontribution ofabout35%,whereasthe pythia 8generatorshowsamuchlarger contributionofaround60%[16].Furthermore,measurementsof b-dijet angular correlations in 7 TeV pp collisions show significant deviationbetweendataandsimulationaswellasacrossgenerators forsmalldijetangularseparation(



R) values,wheregluon split-tingeffectsdominate[46].Itisassumedthatgluonsplittingeffects areasuncertainforcjetsastheyareforbjets.Overall,systematic uncertainty fromthevariation ofthe gluon splittingcontribution isanappreciableeffectinboth pPbandppcollisions,thoughless than15%.

The template statistical uncertainty is accounted for by vary-ing thedistributions oflightparton, c,andbjetsfromMC within their statistical uncertainties using a parametric MC study. The uncertainty is estimatedby fitting a Gaussian distribution to the fluctuationsinpurity,wheretheGaussianwidthisusedasthe

un-Fig. 5. The taggingpurity(upper) and efficiency (lower) for the working point selection ofSSVHP>1.68 in pp collisions at 5.02 TeV (square markers) and at 2.76 TeV (circularmarkers).Puritycurvesfromsimulation(openredmarkers)and data(closedmarkers) areshown,obtainedbyfittingtemplatestothe data.The lowerplot showsefficiency curvesfrom simulation(open redmarkers)and the cross-checkbasedonJPtagging.(Forinterpretationofthe referencestocolorin thisfigure,thereaderisreferredtothewebversionofthisarticle.)

certainty value. These valuesare pT-dependent,ranging from 5% atintermediate pT toaround10%atlow(

≈

60 GeV/c)andhighpT (

≈

300 GeV/c).

Thisanalysisaccountsforthepossibilitythatthe pythia simula-tiondoesnotaccuratelyreproducetheDmesondecaykinematics. Since a secondary vertex that corresponds to a decay involving atleastthree particles is requiredin orderto tag jets, the influ-enceof theDmesondecayparameters isstudiedby reweighting boththerelativecharmquarkfragmentationandthesuccessiveD mesondecay parameters insimulation to match theworld aver-agevalues from previous experiments. We findthat reweighting andvaryingthesevalueswithintheiruncertaintiesleadstoa5.5% effect, independent of the jet pT, collision species, and collision energy.

The contributions from each source ofsystematic uncertainty aresummed inquadratureto obtainan overall systematic uncer-taintyfromcjettagging.Whensummed,thesetagging uncertain-tiesleadtoa 10–12%uncertaintyon thefractionofcharm quark jets(c jet fraction)in pp collisions, anda 10–20% uncertaintyin pPbcollisions, wherethemajorityoftheextrauncertaintyinpPb relativetoppcomesfromtheJP-taggercalibrationandadditional unavoidablecouplingofstatisticalfluctuationsindatato the sys-tematicuncertaintycalculationathigh-pT.

Fig. 6. Thecjetcrosssections(upperpanels)andfraction(lowerpanels)asa func-tionofcjet pT for 5.02 TeV (topfigure) and 2.76 TeV pp data(bottomfigure), comparedtopredictionsfrom pythia 6.Systematicuncertaintiesareshownasfilled boxes.

4.2. Jetreconstructionsystematicuncertainties

Additional uncertainties stem from jet reconstruction. Jet en-ergycorrectionsare derivedfromsimulationsamplesandvia en-ergybalance measurementsusingphoton+jetevents.The residual non-closure of the correctionsleads to a jet energy scale uncer-tainty ranging from 2–3%, depending on pT and

η

. In addition, the effect of jet resolution is calculated by first smearing MC jets to match distributions of jet resolution in data, and then by using a parameterized MC study, which leads to an uncer-tainty ofabout5%.The SVDunfoldingprocedureiscross-checked bycomparing toalternativeunfoldingmethods,including D’Agos-tini’s method [47], and by varying the raw simulated spectrum, known as the “truth” spectrum. The uncertainty on the unfold-ing procedure is around 5%, while a 4% uncertainty is found for the simulation ofthe “truth” spectrum shape. Together, all these reconstruction-based uncertainties are added in quadrature and totalbetween12–15%inpPbcollisionsandaround15%inpp col-lisions.Finally,theintegratedluminosity measurementofthepPb data has an uncertainty of 3.6%, while the corresponding uncer-taintiesinppdataat2.76and5 TeV are3.7and3.6%,respectively. Astheuncertaintiesfromthejetenergyresolution,luminosity, un-folding,andthe“truth”spectrumarecanceled inthecjetfraction measurement,theyareappliedonlytothecrosssection measure-ment.

Fig. 7. Thecjet crosssection (upperpanel)and RpA(lowerpanel)asafunction ofcjet pT for 5.02 TeV pPband ppdata.Statisticaluncertaintiesaresolidblack lines,whilesystematicuncertaintiesareshownasfilledcoloredboxes.Integrated luminosityuncertaintiesforppandpPbdataareshownasfilledboxesaroundunity. 5. Results

Thecjet pTcrosssectioninppcollisionsareshowninFig. 6for 5.02 TeV (upper)and2.76 TeV (lower)collisions.The dataare cor-rectedforjet resolutionby a singular valuedecomposition (SVD) unfoldingprocedure. Both crosssectionsare comparedto predic-tions from theZ2 tune of pythia 6. The bottom panelsof Fig. 6

show the c jet fraction, that is, the total number of charm jets relative to the number of inclusive jets, in pp for both collision energies.Acomparisonofthecjet fractionsat2.76and5.02 TeV suggeststhatthecollisionenergydependenceofthecjetfraction issmallifanyandthetwomeasurementsareconsistentwitheach otherwithin systematicuncertainties.Inaddition,datafromboth energiesconfirmthe pythia predictions.

The c jet cross sections as functionsof pT are shown in the upper panel of Fig. 7 forpPb and pp collisions at 5.02 TeV. The crosssectionsarenormalizedbythetotalintegratedluminosityof thesample.ThepPbcjetcrosssection isalsoscaledbythemass numberoflead( A

=

208)whichnormalizesthepPbmeasurement perbinary nucleon–nucleoncollision,aspredictedbytheGlauber model[48,49].Thisadditional scalingallows foradirect compar-ison of the pPb data to the pp data at the same center-of-mass energy.ThedirectcomparisonisknownastheRpA value,whichis definedas: RpA

=

1 A d

σ

pPb

_/

_dp T d

σ

pp

_/

_dp T

.

(4)

InthelowerpanelofFig. 7,thecjet RpA valueiscalculatedat 5.02 TeV.We observe RpA valuesconsistent withunity forall pT bins, suggestingthat initial state nuclear modification effects are smallfor cjets atlarge pT, confirming perturbative QCD predic-tionsindicating suchbehavior. Thisabsenceofinitial stateeffects is consistent with similar CMS observations for b and inclusive jets[23,28].Fitting a constant to the pPb c jet RpA pT distribu-tionyields RpA

=

0

.

92

±

0

.

07(stat)

±

0

.

11(syst).

6. Summary

The transverse momentum differential crosssection forc jets hasbeen obtainedforpPb collisions at

√

sNN

=

5

.

02 TeV, aswell as for pp collisions at

√

s

=

2

.

76 and 5.02 TeV. The c jet frac-tion of

≈

6% is consistent with pythia simulations for pp colli-sionsatbothcenter-of-massenergies.Bycomparingthecross

sec-tions for pPb and pp collisions, a pT-independent RpA value of 0

.

92

±

0

.

07(stat)

±

0

.

11(syst) is observed for c jets at 5.02 TeV, indicatingthat nosignificantjetenergymodificationispresentin pPbcollisions forcjetswith pT

>

55 GeV/c.Thesemeasurements indicate that proton–lead initial state effects on c jets between 55–400 GeV/c aresmallandthatcharmjetquenchinginlead–lead collisionsshouldnotbeinfluencedbysucheffects.

Acknowledgements

WecongratulateourcolleaguesintheCERNaccelerator depart-ments for the excellent performance of the LHC and thank the technical andadministrativestaffs atCERNand atother CMS in-stitutes for their contributions to the success of the CMS effort. Inaddition,wegratefullyacknowledgethecomputingcentersand personneloftheWorldwideLHCComputingGridfordeliveringso effectively thecomputinginfrastructure essentialto our analyses. Finally,weacknowledgetheenduringsupportfortheconstruction and operationof the LHCand theCMS detectorprovided by the followingfundingagencies:

BMWFW andFWF (Austria);FNRS andFWO(Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, Ministry of Science and Technology of the People’s Republic of China, andNSFC (China);COLCIENCIAS(Colombia);MSESandCSF (Croatia); RPF(Cyprus); SENESCYT(Ecuador);MoER,ERCIUT, and ERDF(Estonia); AcademyofFinland,MEC,andHIP (Finland);CEA andCNRS/IN2P3 (France);BMBF, DFG,andHGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);INFN (Italy); MSIP andNRF (Republicof Ko-rea);LAS (Lithuania);MOEandUM(Malaysia);BUAP,CINVESTAV, CONACYT,LNS,SEP,andUASLP-FAI(Mexico);MBIE(NewZealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna), MON, ROSATOM, RAS, RFBR andRAEP (Russia); MESTD (Serbia);SEIDIandCPAN(Spain);SwissFundingAgencies (Switzer-land); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thai-land);TUBITAKandTAEK(Turkey);NASUandSFFR(Ukraine);STFC (UnitedKingdom);DOEandNSF(USA).

Individuals have received support from the Marie-Curie pro-gram and the European Research Council and EPLANET (Euro-pean Union); the Leventis Foundation; theA. P. Sloan Founda-tion; the Alexander von Humboldt Foundation; the Belgian Fed-eral Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technolo-gie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of theCzech Republic; theCouncil of Scienceand Indus-trial Research, India; the HOMING PLUS program of the Foun-dation for Polish Science, cofinanced from European Union, Re-gional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/ 02861, Sonata-bis 2012/07/E/ST2/01406; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Na-tional Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; theRachadapisekSompotFundforPostdoctoralFellowship, Chula-longkornUniversityandtheChulalongkornAcademic intoIts2nd Century Project Advancement Project (Thailand); and the Welch Foundation,contractC-1845.

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The CMS Collaboration

A.M. Sirunyan,

A. Tumasyan

YerevanPhysicsInstitute,Yerevan,Armenia

W. Adam,

E. Asilar,

T. Bergauer,

J. Brandstetter,

E. Brondolin,

M. Dragicevic,

J. Erö,

M. Flechl,

M. Friedl,

R. Frühwirth

1

,

V.M. Ghete,

C. Hartl,

N. Hörmann,

J. Hrubec,

M. Jeitler

1

,

A. König,

I. Krätschmer,

D. Liko,

T. Matsushita,

I. Mikulec,

D. Rabady,

N. Rad,

B. Rahbaran,

H. Rohringer,

J. Schieck

1

,

J. Strauss,

W. Waltenberger,

C.-E. Wulz

1

InstitutfürHochenergiephysik,Wien,Austria

O. Dvornikov,

V. Makarenko,

V. Zykunov

InstituteforNuclearProblems,Minsk,Belarus

V. Mossolov,

N. Shumeiko,

J. Suarez Gonzalez

NationalCentreforParticleandHighEnergyPhysics,Minsk,Belarus

S. Alderweireldt,

E.A. De Wolf,

X. Janssen,

J. Lauwers,

M. Van De Klundert,

H. Van Haevermaet,

P. Van Mechelen,

N. Van Remortel,

A. Van Spilbeeck

UniversiteitAntwerpen,Antwerpen,Belgium

S. Abu Zeid,

F. Blekman,

J. D’Hondt,

N. Daci,

I. De Bruyn,

K. Deroover,

S. Lowette,

S. Moortgat,

L. Moreels,

A. Olbrechts,

Q. Python,

S. Tavernier,

W. Van Doninck,

P. Van Mulders,

I. Van Parijs

VrijeUniversiteitBrussel,Brussel,Belgium

H. Brun,

B. Clerbaux,

G. De Lentdecker,

H. Delannoy,

G. Fasanella,

L. Favart,

R. Goldouzian,

A. Grebenyuk,

G. Karapostoli,

T. Lenzi,

A. Léonard,

J. Luetic,

T. Maerschalk,

A. Marinov,

A. Randle-conde,

T. Seva,

C. Vander Velde,

P. Vanlaer,

D. Vannerom,

R. Yonamine,

F. Zenoni,

F. Zhang

2

UniversitéLibredeBruxelles,Bruxelles,Belgium

A. Cimmino,

T. Cornelis,

D. Dobur,

A. Fagot,

G. Garcia,

M. Gul,

I. Khvastunov,

D. Poyraz,

S. Salva,

R. Schöfbeck,

M. Tytgat,

W. Van Driessche,

E. Yazgan,

N. Zaganidis

GhentUniversity,Ghent,Belgium

H. Bakhshiansohi,

C. Beluffi

3

,

O. Bondu,

S. Brochet,

G. Bruno,

A. Caudron,

S. De Visscher,

C. Delaere,

M. Delcourt,

B. Francois,

A. Giammanco,

A. Jafari,

P. Jez,

M. Komm,

G. Krintiras,

V. Lemaitre,

A. Magitteri,

A. Mertens,

M. Musich,

C. Nuttens,

K. Piotrzkowski,

L. Quertenmont,

M. Selvaggi,

M. Vidal Marono,

S. Wertz

UniversitéCatholiquedeLouvain,Louvain-la-Neuve,Belgium

N. Beliy

UniversitédeMons,Mons,Belgium

W.L. Aldá Júnior,

F.L. Alves,

G.A. Alves,

L. Brito,

C. Hensel,

A. Moraes,

M.E. Pol,

P. Rebello Teles

CentroBrasileirodePesquisasFisicas,RiodeJaneiro,Brazil

E. Belchior Batista Das Chagas,

W. Carvalho,

J. Chinellato

4

,

A. Custódio,

E.M. Da Costa,

G.G. Da Silveira

5

,

D. De Jesus Damiao,

C. De Oliveira Martins,

S. Fonseca De Souza,

L.M. Huertas Guativa,

H. Malbouisson,

D. Matos Figueiredo,

C. Mora Herrera,

L. Mundim,

H. Nogima,

W.L. Prado Da Silva,

A. Santoro,

A. Sznajder,

E.J. Tonelli Manganote

4

,

A. Vilela Pereira

S. Ahuja

a

,

C.A. Bernardes

a

,

S. Dogra

a

,

T.R. Fernandez Perez Tomei

a

,

E.M. Gregores

b

,

P.G. Mercadante

b

,

C.S. Moon

a

,

S.F. Novaes

a

,

Sandra S. Padula

a

,

D. Romero Abad

b

,

J.C. Ruiz Vargas

a

a_{UniversidadeEstadualPaulista,SãoPaulo,Brazil} b_{UniversidadeFederaldoABC,SãoPaulo,Brazil}

A. Aleksandrov,

R. Hadjiiska,

P. Iaydjiev,

M. Rodozov,

S. Stoykova,

G. Sultanov,

M. Vutova

InstituteforNuclearResearchandNuclearEnergy,Sofia,Bulgaria

A. Dimitrov,

I. Glushkov,

L. Litov,

B. Pavlov,

P. Petkov

UniversityofSofia,Sofia,Bulgaria

W. Fang

6

BeihangUniversity,Beijing,China

M. Ahmad,

J.G. Bian,

G.M. Chen,

H.S. Chen,

M. Chen,

Y. Chen

7

,

T. Cheng,

C.H. Jiang,

D. Leggat,

Z. Liu,

F. Romeo,

S.M. Shaheen,

A. Spiezia,

J. Tao,

C. Wang,

Z. Wang,

H. Zhang,

J. Zhao

InstituteofHighEnergyPhysics,Beijing,China

Y. Ban,

G. Chen,

Q. Li,

S. Liu,

Y. Mao,

S.J. Qian,

D. Wang,

Z. Xu

StateKeyLaboratoryofNuclearPhysicsandTechnology,PekingUniversity,Beijing,China

C. Avila,

A. Cabrera,

L.F. Chaparro Sierra,

C. Florez,

J.P. Gomez,

C.F. González Hernández,

J.D. Ruiz Alvarez,

J.C. Sanabria

UniversidaddeLosAndes,Bogota,Colombia

N. Godinovic,

D. Lelas,

I. Puljak,

P.M. Ribeiro Cipriano,

T. Sculac

UniversityofSplit,FacultyofElectricalEngineering,MechanicalEngineeringandNavalArchitecture,Split,Croatia

Z. Antunovic,

M. Kovac

UniversityofSplit,FacultyofScience,Split,Croatia

V. Brigljevic,

D. Ferencek,

K. Kadija,

B. Mesic,

S. Micanovic,

L. Sudic,

T. Susa

InstituteRudjerBoskovic,Zagreb,Croatia

A. Attikis,

G. Mavromanolakis,

J. Mousa,

C. Nicolaou,

F. Ptochos,

P.A. Razis,

H. Rykaczewski,

D. Tsiakkouri

UniversityofCyprus,Nicosia,Cyprus

M. Finger

8

,

M. Finger Jr.

8

CharlesUniversity,Prague,CzechRepublic

E. Carrera Jarrin

UniversidadSanFranciscodeQuito,Quito,Ecuador

E. El-khateeb

9

,

S. Elgammal

10

,

A. Mohamed

11

AcademyofScientificResearchandTechnologyoftheArabRepublicofEgypt,EgyptianNetworkofHighEnergyPhysics,Cairo,Egypt

M. Kadastik,

L. Perrini,

M. Raidal,

A. Tiko,

C. Veelken

NationalInstituteofChemicalPhysicsandBiophysics,Tallinn,Estonia

P. Eerola,

J. Pekkanen,

M. Voutilainen

J. Härkönen,

T. Järvinen,

V. Karimäki,

R. Kinnunen,

T. Lampén,

K. Lassila-Perini,

S. Lehti,

T. Lindén,

P. Luukka,

J. Tuominiemi,

E. Tuovinen,

L. Wendland

HelsinkiInstituteofPhysics,Helsinki,Finland

J. Talvitie,

T. Tuuva

LappeenrantaUniversityofTechnology,Lappeenranta,Finland

M. Besancon,

F. Couderc,

M. Dejardin,

D. Denegri,

B. Fabbro,

J.L. Faure,

C. Favaro,

F. Ferri,

S. Ganjour,

S. Ghosh,

A. Givernaud,

P. Gras,

G. Hamel de Monchenault,

P. Jarry,

I. Kucher,

E. Locci,

M. Machet,

J. Malcles,

J. Rander,

A. Rosowsky,

M. Titov,

A. Zghiche

IRFU,CEA,UniversitéParis-Saclay,Gif-sur-Yvette,France

A. Abdulsalam,

I. Antropov,

S. Baffioni,

F. Beaudette,

P. Busson,

L. Cadamuro,

E. Chapon,

C. Charlot,

O. Davignon,

R. Granier de Cassagnac,

M. Jo,

S. Lisniak,

P. Miné,

M. Nguyen,

C. Ochando,

G. Ortona,

P. Paganini,

P. Pigard,

S. Regnard,

R. Salerno,

Y. Sirois,

T. Strebler,

Y. Yilmaz,

A. Zabi

LaboratoireLeprince-Ringuet,EcolePolytechnique,IN2P3-CNRS,Palaiseau,France

J.-L. Agram

12

,

J. Andrea,

A. Aubin,

D. Bloch,

J.-M. Brom,

M. Buttignol,

E.C. Chabert,

N. Chanon,

C. Collard,

E. Conte

12

,

X. Coubez,

J.-C. Fontaine

12

,

D. Gelé,

U. Goerlach,

A.-C. Le Bihan,

K. Skovpen,

P. Van Hove

InstitutPluridisciplinaireHubertCurien(IPHC),UniversitédeStrasbourg,CNRS-IN2P3,France

S. Gadrat

CentredeCalculdel’InstitutNationaldePhysiqueNucleaireetdePhysiquedesParticules,CNRS/IN2P3,Villeurbanne,France

S. Beauceron,

C. Bernet,

G. Boudoul,

C.A. Carrillo Montoya,

R. Chierici,

D. Contardo,

B. Courbon,

P. Depasse,

H. El Mamouni,

J. Fan,

J. Fay,

S. Gascon,

M. Gouzevitch,

G. Grenier,

B. Ille,

F. Lagarde,

I.B. Laktineh,

M. Lethuillier,

L. Mirabito,

A.L. Pequegnot,

S. Perries,

A. Popov

13

,

D. Sabes,

V. Sordini,

M. Vander Donckt,

P. Verdier,

S. Viret

UniversitédeLyon,UniversitéClaudeBernardLyon1,CNRS-IN2P3,InstitutdePhysiqueNucléairedeLyon,Villeurbanne,France

A. Khvedelidze

8

GeorgianTechnicalUniversity,Tbilisi,Georgia

I. Bagaturia

14

TbilisiStateUniversity,Tbilisi,Georgia

C. Autermann,

S. Beranek,

L. Feld,

A. Heister,

M.K. Kiesel,

K. Klein,

M. Lipinski,

A. Ostapchuk,

M. Preuten,

F. Raupach,

S. Schael,

C. Schomakers,

J. Schulz,

T. Verlage,

H. Weber,

V. Zhukov

13

RWTHAachenUniversity,I.PhysikalischesInstitut,Aachen,Germany

A. Albert,

M. Brodski,

E. Dietz-Laursonn,

D. Duchardt,

M. Endres,

M. Erdmann,

S. Erdweg,

T. Esch,

R. Fischer,

A. Güth,

M. Hamer,

T. Hebbeker,

C. Heidemann,

K. Hoepfner,

S. Knutzen,

M. Merschmeyer,

A. Meyer,

P. Millet,

S. Mukherjee,

M. Olschewski,

K. Padeken,

T. Pook,

M. Radziej,

H. Reithler,

M. Rieger,

F. Scheuch,

L. Sonnenschein,

D. Teyssier,

S. Thüer

RWTHAachenUniversity,III.PhysikalischesInstitutA,Aachen,Germany

V. Cherepanov,

G. Flügge,

B. Kargoll,

T. Kress,

A. Künsken,

J. Lingemann,

T. Müller,

A. Nehrkorn,

A. Nowack,

C. Pistone,

O. Pooth,

A. Stahl

15

M. Aldaya Martin,

T. Arndt,

C. Asawatangtrakuldee,

K. Beernaert,

O. Behnke,

U. Behrens,

A.A. Bin Anuar,

K. Borras

16

,

A. Campbell,

P. Connor,

C. Contreras-Campana,

F. Costanza,

C. Diez Pardos,

G. Dolinska,

G. Eckerlin,

D. Eckstein,

T. Eichhorn,

E. Eren,

E. Gallo

17

,

J. Garay Garcia,

A. Geiser,

A. Gizhko,

J.M. Grados Luyando,

P. Gunnellini,

A. Harb,

J. Hauk,

M. Hempel

18

,

H. Jung,

A. Kalogeropoulos,

O. Karacheban

18

,

M. Kasemann,

J. Keaveney,

C. Kleinwort,

I. Korol,

D. Krücker,

W. Lange,

A. Lelek,

J. Leonard,

K. Lipka,

A. Lobanov,

W. Lohmann

18

,

R. Mankel,

I.-A. Melzer-Pellmann,

A.B. Meyer,

G. Mittag,

J. Mnich,

A. Mussgiller,

E. Ntomari,

D. Pitzl,

R. Placakyte,

A. Raspereza,

B. Roland,

M.Ö. Sahin,

P. Saxena,

T. Schoerner-Sadenius,

C. Seitz,

S. Spannagel,

N. Stefaniuk,

G.P. Van Onsem,

R. Walsh,

C. Wissing

DeutschesElektronen-Synchrotron,Hamburg,Germany

V. Blobel,

M. Centis Vignali,

A.R. Draeger,

T. Dreyer,

E. Garutti,

D. Gonzalez,

J. Haller,

M. Hoffmann,

A. Junkes,

R. Klanner,

R. Kogler,

N. Kovalchuk,

T. Lapsien,

T. Lenz,

I. Marchesini,

D. Marconi,

M. Meyer,

M. Niedziela,

D. Nowatschin,

F. Pantaleo

15

,

T. Peiffer,

A. Perieanu,

J. Poehlsen,

C. Sander,

C. Scharf,

P. Schleper,

A. Schmidt,

S. Schumann,

J. Schwandt,

H. Stadie,

G. Steinbrück,

F.M. Stober,

M. Stöver,

H. Tholen,

D. Troendle,

E. Usai,

L. Vanelderen,

A. Vanhoefer,

B. Vormwald

UniversityofHamburg,Hamburg,Germany

M. Akbiyik,

C. Barth,

S. Baur,

C. Baus,

J. Berger,

E. Butz,

R. Caspart,

T. Chwalek,

F. Colombo,

W. De Boer,

A. Dierlamm,

S. Fink,

B. Freund,

R. Friese,

M. Giffels,

A. Gilbert,

P. Goldenzweig,

D. Haitz,

F. Hartmann

15

,

S.M. Heindl,

U. Husemann,

I. Katkov

13

,

S. Kudella,

H. Mildner,

M.U. Mozer,

Th. Müller,

M. Plagge,

G. Quast,

K. Rabbertz,

S. Röcker,

F. Roscher,

M. Schröder,

I. Shvetsov,

G. Sieber,

H.J. Simonis,

R. Ulrich,

S. Wayand,

M. Weber,

T. Weiler,

S. Williamson,

C. Wöhrmann,

R. Wolf

InstitutfürExperimentelleKernphysik,Karlsruhe,Germany

G. Anagnostou,

G. Daskalakis,

T. Geralis,

V.A. Giakoumopoulou,

A. Kyriakis,

D. Loukas,

I. Topsis-Giotis

InstituteofNuclearandParticlePhysics(INPP),NCSRDemokritos,AghiaParaskevi,Greece

S. Kesisoglou,

A. Panagiotou,

N. Saoulidou,

E. Tziaferi

NationalandKapodistrianUniversityofAthens,Athens,Greece

I. Evangelou,

G. Flouris,

C. Foudas,

P. Kokkas,

N. Loukas,

N. Manthos,

I. Papadopoulos,

E. Paradas

UniversityofIoánnina,Ioánnina,Greece

N. Filipovic

MTA-ELTELendületCMSParticleandNuclearPhysicsGroup,EötvösLorándUniversity,Budapest,Hungary

G. Bencze,

C. Hajdu,

D. Horvath

19

,

F. Sikler,

V. Veszpremi,

G. Vesztergombi

20

,

A.J. Zsigmond

WignerResearchCentreforPhysics,Budapest,Hungary

N. Beni,

S. Czellar,

J. Karancsi

21

,

A. Makovec,

J. Molnar,

Z. Szillasi

InstituteofNuclearResearchATOMKI,Debrecen,Hungary

M. Bartók

20

,

P. Raics,

Z.L. Trocsanyi,

B. Ujvari

InstituteofPhysics,UniversityofDebrecen,Hungary

S. Bahinipati,

S. Choudhury

22

,

P. Mal,

K. Mandal,

A. Nayak

23

,

D.K. Sahoo,

N. Sahoo,

S.K. Swain

NationalInstituteofScienceEducationandResearch,Bhubaneswar,India

S. Bansal,

S.B. Beri,

V. Bhatnagar,

R. Chawla,

U. Bhawandeep,

A.K. Kalsi,

A. Kaur,

M. Kaur,

R. Kumar,

P. Kumari,

A. Mehta,

M. Mittal,

J.B. Singh,

G. Walia

Ashok Kumar,

A. Bhardwaj,

B.C. Choudhary,

R.B. Garg,

S. Keshri,

S. Malhotra,

M. Naimuddin,

N. Nishu,

K. Ranjan,

R. Sharma,

V. Sharma

UniversityofDelhi,Delhi,India

R. Bhattacharya,

S. Bhattacharya,

K. Chatterjee,

S. Dey,

S. Dutt,

S. Dutta,

S. Ghosh,

N. Majumdar,

A. Modak,

K. Mondal,

S. Mukhopadhyay,

S. Nandan,

A. Purohit,

A. Roy,

D. Roy,

S. Roy Chowdhury,

S. Sarkar,

M. Sharan,

S. Thakur

SahaInstituteofNuclearPhysics,Kolkata,India

P.K. Behera

IndianInstituteofTechnologyMadras,Madras,India

R. Chudasama,

D. Dutta,

V. Jha,

V. Kumar,

A.K. Mohanty

15

,

P.K. Netrakanti,

L.M. Pant,

P. Shukla,

A. Topkar

BhabhaAtomicResearchCentre,Mumbai,India

T. Aziz,

S. Dugad,

G. Kole,

B. Mahakud,

S. Mitra,

G.B. Mohanty,

B. Parida,

N. Sur,

B. Sutar

TataInstituteofFundamentalResearch-A,Mumbai,India

S. Banerjee,

S. Bhowmik

24

,

R.K. Dewanjee,

S. Ganguly,

M. Guchait,

Sa. Jain,

S. Kumar,

M. Maity

24

,

G. Majumder,

K. Mazumdar,

T. Sarkar

24

,

N. Wickramage

25

TataInstituteofFundamentalResearch-B,Mumbai,India

S. Chauhan,

S. Dube,

V. Hegde,

A. Kapoor,

K. Kothekar,

S. Pandey,

A. Rane,

S. Sharma

IndianInstituteofScienceEducationandResearch(IISER),Pune,India

S. Chenarani

26

,

E. Eskandari Tadavani,

S.M. Etesami

26

,

A. Fahim

27

,

M. Khakzad,

M. Mohammadi Najafabadi,

M. Naseri,

S. Paktinat Mehdiabadi

28

,

F. Rezaei Hosseinabadi,

B. Safarzadeh

29

,

M. Zeinali

InstituteforResearchinFundamentalSciences(IPM),Tehran,Iran

M. Felcini,

M. Grunewald

UniversityCollegeDublin,Dublin,Ireland

M. Abbrescia

a

,

b

,

C. Calabria

a

,

b

,

C. Caputo

a

,

b

,

A. Colaleo

a

,

D. Creanza

a

,

c

,

L. Cristella

a

,

b

,

N. De Filippis

a

,

c

,

M. De Palma

a

,

b

,

L. Fiore

a

,

G. Iaselli

a

,

c

,

G. Maggi

a

,

c

,

M. Maggi

a

,

G. Miniello

a

,

b

,

S. My

a

,

b

,

S. Nuzzo

a

,

b

,

A. Pompili

a

,

b

,

G. Pugliese

a

,

c

,

R. Radogna

a

,

b

,

A. Ranieri

a

,

G. Selvaggi

a

,

b

,

A. Sharma

a

,

L. Silvestris

a

,

15

,

R. Venditti

a

,

b

,

P. Verwilligen

a

a_{INFNSezionediBari,Bari,Italy} b_{UniversitàdiBari,Bari,Italy} c_{PolitecnicodiBari,Bari,Italy}

G. Abbiendi

a

,

C. Battilana,

D. Bonacorsi

a

,

b

,

S. Braibant-Giacomelli

a

,

b

,

L. Brigliadori

a

,

b

,

R. Campanini

a

,

b

,

P. Capiluppi

a

,

b

,

A. Castro

a

,

b

,

F.R. Cavallo

a

,

S.S. Chhibra

a

,

b

,

G. Codispoti

a

,

b

,

M. Cuffiani

a

,

b

,

G.M. Dallavalle

a

,

F. Fabbri

a

,

A. Fanfani

a

,

b

_,

_{D. Fasanella}

a

,

b

_,

_{P. Giacomelli}

a

_,

_{C. Grandi}

a

_,

_{L. Guiducci}

a

,

b

_,

S. Marcellini

a

,

G. Masetti

a

,

A. Montanari

a

,

F.L. Navarria

a

,

b

,

A. Perrotta

a

,

A.M. Rossi

a

,

b

,

T. Rovelli

a

,

b

,

G.P. Siroli

a

,

b

,

N. Tosi

a

,

b

,

15

a_{INFNSezionediBologna,Bologna,Italy} b_{UniversitàdiBologna,Bologna,Italy}

S. Albergo

a

,

b

,

S. Costa

a

,

b

,

A. Di Mattia

a

,

F. Giordano

a

,

b

,

R. Potenza

a

,

b

,

A. Tricomi

a

,

b

,

C. Tuve

a

,

b a_{INFNSezionediCatania,Catania,Italy}