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Physics Letters B
www.elsevier.com/locate/physletb
Transverse momentum spectra of inclusive b jets in pPb collisions at √
s
NN= 5 . 02 TeV
.CMSCollaboration
CERN,Switzerland
a r t i c l e i n f o a b s t ra c t
Articlehistory:
Received12October2015
Receivedinrevisedform10January2016 Accepted12January2016
Availableonline14January2016 Editor:M.Doser
Keywords:
CMS Physics Heavyions b-tagging
Wepresent ameasurement ofbjettransversemomentum(pT)spectrainproton-lead (pPb)collisions usingadatasetcorrespondingtoabout35 nb−1collectedwiththeCMSdetectorattheLHC.Jetsfromb quarkfragmentationarefoundbyexploitingthelonglifetimeofhadronscontainingabquarkthrough tagging methods using distributions of the secondaryvertex mass and displacement. Extracted cross sectionsforbjetsarescaledbytheeffectivenumberofnucleon–nucleoncollisionsandarecomparedto areferenceobtainedfrom pythia simulationsofppcollisions.The pythia-basedestimateofthenuclear modificationfactorisfoundtobe1.22±0.15(stat+syst pPb)±0.27(syst pythia) averagedoveralljets withpTbetween55and400 GeV/c andwith|ηlab| <2.Wealsocomparethisresulttopredictionsfrom modelsusingperturbativecalculationsinquantumchromodynamics.
©2016CERNforthebenefitoftheCMSCollaboration.PublishedbyElsevierB.V.Thisisanopenaccess articleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).FundedbySCOAP3.
1. Introduction
By colliding heavy nuclei at ultra-relativistic energies, suffi- cientlylarge energy densitiesare reached toform aquark–gluon plasma(QGP),astatewhichischaracterizedbyaneffectivedecon- finementofquarks andgluons[1,2].Hard-scattered partonshave beenpredictedtosufferenergylossastheytraversetheQGP, pri- marilyviacollisionalandradiativeprocesses[3,4].Thisenergyloss iscommonlythoughttobethemechanismresponsiblefortheob- served suppression of high transverse momentum (pT) hadrons and jets in nucleus–nucleus collisions relative to proton–proton (pp) collisions [5,6]. This suppression phenomenon, otherwise knownas“jetquenching”,wasdiscoveredattheRHICexperiments atBNL[7–14]andhasbeeninvestigatedfurtherusingfullyrecon- structed jetsat the CERN LHC [15–18]. Studies of partonenergy lossareexpectedtorevealthefundamentalpropertiesoftheQGP.
The quenching of jets in heavy ion collisions should depend on the flavor of the fragmenting parton [5]. For example,under theassumptionthatradiativeenergylossisthedominantmecha- nism,gluonjetsareexpectedtoquenchmorestronglythanquark jets, owing to the larger color factor for gluon emission from gluonsthan from quarks [19]. There are also theoretical predic- tions that radiative energy loss may not be dominant for heavy quarks,includingmodelsbasedoncollisionalenergylossofquarks within the medium and models favoring an interpretation based
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onmesonicrecombinationanddisassociationwithinthemedium, e.g. Refs. [20,21].It isexpectedthat thereshould be some mass- dependenceofpartonicenergylossatlowmomentum,andthere- fore,b quark jet (b jet)energy lossmight be differentfromthat oflightquark jets[22,23].At high-pT,however,theCMSCollabo- ration hasshownthatbjetsuppressioninPbPbisconsistentwith thatoflightquarkjetsabove80 GeV/c[16].
Here we present the first measurement of inclusive b-tagged jetsinproton-lead(pPb)collisions.ThismeasurementinpPbpro- vides the first direct evidence that the jet quenching observed in PbPb is dominated by final-state effects, rather than poten- tialnuclearinitial-stateeffects.Furthermore,thesemeasurements will provide a factorization of cold nuclear matter effects from the medium suppression effects for jets in PbPb collisions. Such a differentiation betweeninitial-state andquenching effects asa functionofflavorcanplaceconstraintsontheenergylossmecha- nismsofpartonsinthehotanddensemedium.Thisisespecially importantinlightoftheCMSmeasurementofthenuclearmodifi- cationfactorofchargedparticlesinpPbcollisions,whichindicates surprisinglylargeinitial-stateeffects[24].
Measurements of dijets in pPb have also shown that a theo- retical description ofdijet yields as a function of pseudorapidity requiresnext-to-leadingordereffectswithcontributionsfromnu- clear partondistribution functions (nPDFs) [25,26]. While pythia simulations predict weak correlationsof Björken-x andsingle jet pseudorapidity,weinvestigatethepseudorapidity-dependentmod- ificationfactorinordertoprobeforthepresenceofstrongunan- ticipated effects in the heavy flavor sector. Any effects would http://dx.doi.org/10.1016/j.physletb.2016.01.010
0370-2693/©2016CERNforthebenefitoftheCMSCollaboration.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).FundedbySCOAP3.
depend predominantlyonthegluonnPDFs,anareawhichhasalso beenexploredtheoretically[27].Thisisincontrasttotheprevious dijetmeasurement,whereleadingorderquarkjetprocesseshavea significantcontributiontothemeasurement,especiallyathigh-pT. WepresentmeasurementsofbjetproductioninpPbcollisions ata nucleon–nucleoncenter-of-massenergyof √
sNN=5.02 TeV, recordedwiththeCMSdetector,usinganintegratedluminosityof about35 nb−1 deliveredbytheLHC.Thecrosssectionforbjetsis measuredandcomparedtopp crosssectionssimulatedusingthe pythiaeventgenerator[28],tuneZ2[29].Theresultingestimated nuclearmodificationfactors(RPYTHIApA )arecomparedtoaprediction basedonperturbativeQCD(pQCD)[30].
2. Detectorandeventselection
The CMS detector has excellent capabilities to perform b jet identification (b tagging) as demonstrated in Ref. [31]. The cen- tral feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T.
Withinthe solenoid volume are a silicon pixel andstrip tracker, aleadtungstatecrystalelectromagneticcalorimeter(ECAL), anda brass and scintillator hadron calorimeter(HCAL), each composed of a barrel and two endcap sections. The tracker has a pseu- dorapidity coverage of |ηlab| <2.4, while the calorimetry covers
|ηlab| <3.Muonsaremeasuredingas-ionizationdetectorsembed- dedin the steel flux-return yoke outsidethe solenoid. Extensive forward calorimetry complements the coverage provided by the barrel and endcap detectors. A more detailed description of the CMS detector, together with a definition of the coordinate sys- tem used and the relevant kinematic variables, can be found in Ref.[32].
The event selection is identical to previous pPb jet analy- ses [24,25], and includes the reconstruction of a primary inter- action vertex, a careful removal of any noise artifacts from the hadroniccalorimeter, along witha requirement that the primary interactionvertexiswithin15 cm ofthenominalinteractionpoint alongthebeamaxis.
In this analysisa newtrigger combinationalgorithm is used, allowing forthe maximization ofstatisticalprecision over a very large range of jet pT. Triggers with thresholds ranging from 20 through 100 GeV/care combined.Except forthe 100 GeV/ctrig- ger, these triggers are prescaled, meaning only a fraction of the total numberof events are recorded. Ineach event, the jet with themaximumonlinerawjetpT (i.e. thelargestjetpT seenbyany ofthefive triggers)isfound. Ifthehighest-threshold triggerthat shouldhaveidentifiedthejetisabsent(prescaledaway),thewhole eventis rejected. Otherwise, all jets in the eventare assigneda weightbasedontheprescalevalueofthathighest-thresholdtrig- ger.Theresultingspectrumisfullyefficientabove≈30 GeV/c.
As described,e.g. in Ref.[25],thedifference in thecharge-to- massratioofprotonsandleadnucleiresultsinasymmetricbeam energies for the two colliding species, which leads to a rapidity shiftof 0.465 unitsbetweenthe nucleon–nucleon center-of-mass frameandthelaboratory frame.Inaddition,after thedatacorre- spondingtoanintegratedluminosityof20.9 nb−1 werecollected, thecirculation directionsof theproton andlead beamswere re- versed. This analysis will use ηCM for the center-of-mass frame and ηlab forthelabframepseudorapidities,wherepositive ηwill always refer tothe beamorientation where theproton beamdi- rectionistowardpositivez.Inthisorientation, ηlab=ηCM+0.465.
Thisanalysisrequiresthatalljetsmusthave−2.5<ηCM<1.5, whichensures that all jetsfragment primarily within the tracker acceptanceof|ηlab| <2.4.Finally,thebackgroundenergyfromthe underlyingpPbeventis estimatedinnarrowranges ofpseudora- pidity, as described in Ref. [33], and is subtracted from the jet.
After the underlying event subtraction, jets must have a recon- structed pT>55 GeV/c andaraw transversemomentum(before jet energycorrections)greaterthan25 GeV/c andmustbe found in an event where a single-jet trigger fires. This requirement is madeinordertoproperlymergeeventsfrommultipletriggers,as discussedearlierinthissection.
In order to estimate the kinematic and resolution properties of jets, simulated dijet eventsare generatedwith pythia version 6.424, tune Z2[28].Thesedijets are thenembedded intoa min- imum bias pPb backgroundevent simulatedby the hijing heavy ioneventgenerator,version1.383[34].
3. Analysisprocedure 3.1. Jetreconstruction
Jetsarereconstructedofflineprimarilyfromtheenergydeposits in thecalorimetertowers,clusteredby theanti-kT algorithm [35, 36] with a size parameter of 0.3. The constituent particles of the jet arereconstructed usingthe particleflow eventalgorithm, which identifies each individual particlewith an optimizedcom- bination of information from the various elements of the CMS detector[37].Therawjetenergyisobtainedfromthesumofthe tower energies,andthe rawjet momentumby thevectorialsum oftheconstituentparticlemomenta,whichresultsinanonzerojet mass. Theraw jetenergies are then correctedto establisha uni- form response of the calorimeter in η and a calibrated absolute responsein pT.Thefinalparticle-flow-basedjetenergyresolution amounts typically to 15% at 10 GeV, 8% at 100 GeV, and 4% at 1 TeV, tobecomparedto about40%, 12%,and5% obtainedwhen thecalorimetersaloneareusedforjetclustering.
Jetenergycorrectionsarederivedfromsimulation,andarecon- firmed within situmeasurements of theenergy balance indijet and photon+jet events. Jet momentum is found from simula- tion to be within 1% to 2% of the true jet momentum over the whole pT spectrumanddetectoracceptanceusedinthisanalysis.
Additional selection criteriaare applied to each event to remove spurious jet-like features originating fromisolated noise patterns incertainHCALregions.
3.2. Taggingbjets
Identificationofbjetsisbasedonkinematicvariablesrelatedto the relativelylong lifetimeandlargemass ofB hadrons.Charged tracks associatedwithjetsareused toreconstructsecondaryver- ticesfromBhadronand/orsubsequentcharmhadrondecaysfrom the b→ ccascade. The primary discriminator usedin thisanal- ysisto identifybjetstakesadvantage ofthedisplacedsecondary vertex. This secondaryvertex basedalgorithm iscalledthe “sim- ple secondary vertex” (SSV) tagger and is described in detail in Ref. [31].Effectively,jetsareassignedadiscriminatorvalue based on the secondaryvertexflight distance significance,whichis the ratio of the distance between the primary and secondary vertex toits uncertainty.Toremoveadditionalcontributionsofjetsfrom longlivedlightmesons,secondaryvertexmassescompatiblewith the K0S meson and displacements larger than 2.5 cm are explic- itlyrejected. Usingthisdiscriminator, thecontributionofbjetsis enhancedbyrequiringthatsecondaryverticesarefarfromthepri- maryvertex.TheSSVselectionvalueusedinthisanalysisis 2.0,re- quiringthatthesecondaryvertexistwostandarddeviationsaway from the primary vertex. This is chosen to give a misidentifica- tionrateontheorderof1%forlight-flavorjetsand10%forcharm jets, based on simulation. The corresponding b taggingefficiency is about65% for both pp andpPb collisions, whichuse identical reconstruction procedures. This is in contrast to the PbPb b jet
Fig. 1. DistributionsoftheJPtaggerdiscriminatorbefore(top)andafter(bottom) applyingthe SSVtaggerselection.Filledblackpointsaredata,whilethe colored histogramsdenotecontributionsfromsimulatedb,c,andlight-flavorjetsinred, greenand blue,respectively,obtainedfromafit todata.Statisticaluncertainties fromdataareinblack,whilestatisticaluncertaintyfromthetemplatesareshown indarkgreen.(Forinterpretationofthereferencestocolorinthisfigurelegend,the readerisreferredtothewebversionofthisarticle.)
analysisatCMSwhere the btagging efficiencyisabout 45% due totheneedforadedicatedregionaltrackreconstructionowingto theverylargemultiplicitiesreachedincentralcollisions[16].
The b tagging efficiency is obtained by simply counting the numbers of b jets before andafter tagging in simulation,but is cross-checkedusing a data-driven method from the output of a secondbtaggingalgorithm:thejetprobability(JP)algorithm.The advantageofthissecond taggeris thatit doesnot relyupon the reconstruction of a secondary vertex[31]. Instead, the JP tagger calculates the compatibility of each track in the jet cone with the primary vertex using a probability distribution based on the three-dimensional impact parameter significance. In essence, the less compatible the jet tracks are with the primary vertex, the greater the likelihood of the jet being from a b quark fragmen- tation. Tracksmay alsohave anegative impactparameter, which ariseswhentheyarefoundtobedisplacedfromtheprimaryver- texon the opposite side ofthe vertexfromthe jet. Thesetracks mainlycomefromprimaryparticleswithanimproperlymeasured impact parameter due to finite vertex resolution effects or from poorlymeasuredtrackkinematicparameters.Sincethesetypesof tracksareessentiallyrandomlyassociatedwiththevertex,theyare notusedtotagjets, butinsteadcan beusedtocalibrate thetag- ger.Randomlyassociatedtracksshouldhavenocorrelationtothe vertexas a function of displacement,so the total distribution of thesetracksasafunctionoftrackdisplacementshouldbeflat.Ifit isnot,thetaggeriscalibratedbyapplyingaweightingfunctionin ordertoflattenthespectrum[31].OncetheJPtaggeriscalibrated, discriminator values are obtained by calculating the sum of the
Fig. 2. Thetop panelshowsthelikelihoodofmisidentifyingalight-flavor(circles anddottedlines)orcharm(squaresanddashedlines)jetasabjet,asafunction ofthebtaggingefficiency.ShownistheSSVtaggerforpPb(purple)andpp(green) collisions.Thebottom panelshowsatemplatefittothesecondaryvertexinvari- antmassdistributioninpPbcollisionsforjetswith90<pT<110 GeV/c.Filled blackpointsaredata,whilethecoloredhistogramsdenotedistributionsofb,c,and light-quarkjetsinred,greenandblue,respectively,extractedfromthefittodata.
Statisticaluncertaintiesfromdataareshownasblackverticalbars,whilestatisti- caluncertaintiesfromthetemplatesareshownasdarkgreenverticalbarsaround thesumofthetemplates.(Forinterpretationofthereferencestocolorinthisfigure legend,thereaderisreferredtothewebversionofthisarticle.)
negative logarithm of all track-to-vertex probabilities, normalized bythefactorialofthenumberoftracksassociatedwiththejet.
Distributions ofthe JP tagger discriminator are plotted before andafter applying theSSV selection definedearlier. Byusing an unbinnedmaximumlikelihoodfittotheJPdistributions,thethree flavorcontributionsfromsimulationsaresimultaneouslyfittothe data.Fromthesefits,theSSVbtaggingefficiencycanbeextracted basedon Eq.(1),where Cb isthe fractionofb jetsfromsimula- tionthathaveaJPdiscriminatorvalue, fbtagged isthepurityofthe SSV >2taggedsample, fbuntagged isthepuritybeforetagging,and Nuntaggedjets and Ntaggedjets are thenumberofjetsbefore andafterthe SSVselection,respectively.
S S V= Cbf
tagged b Ntaggedjets
fbuntaggedNjetsuntagged. (1)
ExampledistributionsoftheJPtaggerdiscriminatorbeforeand afterSSVtaggingintherange90<pT<110 GeV/c areshownin Fig. 1.TheefficiencyfoundbyapplyingtheSSVtaggertoJP-tagged eventsindataandcalculatingtheefficiencydirectlyfromsimula- tionarecompatibletowithin5–20%,wherethedifferenceistaken asasystematicuncertainty.
Thebtagging efficiencyoftheSSV taggerisshownasa func- tion ofthemisidentificationprobability oflight-flavor andcharm jetson theleft inFig. 2.The efficiencyandpurityofthe taggers
