Table Of ContentB-tagging at CMS
CristinaFerro1,a
IPHCStrasbourg.
Abstract. Theidentificationofbjetsisacrucialissuetostudyandcharacterizevariouschannelsliketopquark
eventsandmanynewphysicsscenarios.Differentb-taggingtechniquesaredefinedinCMSwhichbenefitfrom
the long life time, high mass and large momentum fraction of the b-hadron produced in b-quark jet. Efficient
2 algorithmshavebeendevelopedbasedonthemeasureofb-hadronsecondaryvertexorontrackswithalarge
√
1 impactparameter.Datacollectedin ppcollisionsat s=7TeVin2011areusedtoestimateboththeb-tagging
0 efficiencyandthemistagratefromlightflavorjets.
2
n
a 1 Introduction
J CMS Preliminary, s = 7 TeV data (#PV: 1-3)
70000
5 Theb-taggingalgorithmsinCMSmainlyrelyonthelong 60000 data (#PV: 4-7)
2 life time, high mass and large momentum fraction of b 50000 data (#PV: >7)
hadrons produced in b-quark jets, as well as on the pres- 40000
]
x ence of soft leptons from semi-leptonic b decays[1]. Due 30000
e tothehighinstantaneousluminosityduringthe2011data 20000
p- taking, the number of collision taking place in the same 10000
e bunchcrossing(pileupevents)isoftheorderof5to11on 00 5 10 15 20 25 30 35 40 45
1 [h average. #PV 4-7/#PV 1-3 01..5510 5 10 15 20 25nr. o3f0 sele3c5ted 4t0racks45
v CMS Preliminary, s = 7 TeV data (#PV: 1-3)
2 35000 data (#PV: 4-7) Fig.2.Numberoftracksassociatedtoajetafterselectioncuts.
9 30000
data (#PV: >7)
2 25000
5 20000 and the transverse momentum of the muon relative to the
.
1 15000 jet direction. In the following a brief description of these
0 10000 variableispresented.
2 5000
1 00 5 10 15 20 25 30 35 40 45
Xiv: #PV 4-7/#PV 1-3 01..5510 5 10 15 20 25 n30r. of 3tr5acks4 0in je4t5 2.1 Theimpactparametersignificance
r
a Fig.1.Numberoftracksassociatedtoajetwithoutanyselection
cut.
Thepresenceofpileupincreasesthetrackmultiplicity
intheevents,aswecanseeinFig.(1).Thisiswhyaspecial
selectionofthetrackswasappliedinordertoremovethe
tracks originating from pileup[2]. In Fig.(2), the number
oftrackspassingtheselectioncutsshowsasmallerpileup
dependence.
2 The b-tagging observables
Theb-taggingalgorithmsandtheirstudyarebasedonthe
measureofthreemainvariables:theimpactparametersig-
nificanceofthetracks,thepositionofthesecondaryvertex,
a e-mail:[email protected] Fig.3.Geometricmeaningoftheimpactparametersignificance.
EPJWebofConferences
The impact parameter (IP) is defined as the distance
betweenthetrackandtheprimaryinteractionvertex(PV)
at the point of closest approach. The IP is positive (nega-
tive)ifthetrackisproduceddownstream(upstream)with
respecttothePValongthejetdirection(Fig.(3)).TheIPis
calculatedin3dimensionsthankstothegoodx-y-zresolu-
tionprovidedbythepixeldetector.Animportantfeatures
of the IP is that it is Lorentz invariant and due to the b-
hadronlifetimethetypicalIPscaleissetbycτ ∼480µm.
In practice, the impact parameter significance IP/σ(IP) is
usedinordertotakeintoaccountresolutioneffects.Thanks
to the long lifetime of the b-hadrons the IP from b-jets is
expectedtobemainlypositive,whileforthelightjetsitis
almostsymmetricwithrespecttozero(Fig.4).
2.2 Thesecondaryvertex
Fig.4.Discriminatorsfor:Topleft TrackCountingHighEffi-
ThankstothehighresolutionoftheCMStrakingsystem, ciency(IP/σ(IP)),centerTrackCountingHighpurity(IP/σ(IP)),
it is possible to directly reconstruct the secondary vertex,
rightJetProbability, BottomleftJetBProbability,centerSimple
thepointwherethebhadrondecays(Fig.(3)).Thevertex SecondaryVertexHighefficiency,rightSimpleSecondaryVertex
reconstructionisperformedusingtheadaptivevertexfitter.
Highpurity.
The resulting list of vertices is then subject to a cleaning
procedure,rejectingSVcandidatesthatshare65%ormore
oftheirtrackswiththePV. thePV.TheJetBprobabilityisthendefinedinasimilar
waybutgivingmoreweighttothefourmostdisplaced
tracks.
2.3 Thetransversemomentumofthemuon – Soft-Lepton tagging algorithms rely on the properties
ofmuonsorelectronsfromsemileptonicb-decay.Due
Semileptonic decays of b hadrons give rise to b jets that tothelargeb-quarkmass,themomentumofthemuon
contain a muon with a branching ratio of about 11%, or transversetothejetaxis, prel,islargerformuonsfrom
T
20% when b→c→l cascade decays are included. This is b-hadrondecaysthanformuonsinlightflavorjets.
whythereconstructedmuonsinsideajetareusedtostudy – SecondaryVertextaggingalgorithmsrelyontherecon-
theperformanceofthelifetime-basedtaggingalgorithms. struction of at least one secondary vertex. The signif-
ThemuonsareseededfromtheCMSmuonchambers,and icance of the 3D flight distance is used as a discrimi-
are then linked to tracks found in the tracking system to nating variable. Two variants based on the number of
formglobalmuons.TheCMSmuonsystemisabletomea- tracksatSVareconsidered:N≥2forhighefficiency
suremuonswithhighacceptanceresolutionandefficiency. (SSVHE),andNtr≥3forhigh purity(SSVHP)[2].
Thecombinedsecondaryvertexalgorithmincludesthis
informationandprovidesdiscriminationevenwhenno
3 B-tagging algorithms secondaryverticesarefound.Themassofreconstructed
chargedparticlesatthesecondaryvertexisusedtomea-
suretheb-taggedsamplepurity.
Severlab-taggingalgorithmsareusedinCMS[1],[2].
The output of each algorithm is a discriminator value on
whichtheusercancutontoselectdifferentregionsinthe
efficiency versus purity phase space. In Fig.(4)these dis- 4 Performance of the taggers
criminatorsarepresented.
Varying the cuts on the discriminator, we obtain different
– Thetrackcountingalgorithmidentifiesab-jetifthere efficiency of the taggers. We establish standard operating
are at least N tracks with a significance of the impact pointsas,loose(L),medium(M),andtight(T),beingthe
parameter above a given threshold. The tracks are or- value at which the tagging of udsg jets is estimated from
dered in decreasing IP/σ(IP) and the discriminator is MCtobe10%,1%,or0.1%,respectively,forjettransverse
theimpactparametersignificanceoftheNthtrack.To momentum of about 80 GeV. In Fig.(5) the performance
getanhighb-jetefficiencywecanusetheIP/σ(IP)of for different taggers are shown. In Fig.(6) the effects of
thesecondtrack(TCHE),toselectb-jetswithhighpu- thepileupontheperformanceoftheTCHEtaggerispre-
ritythethirdtrackisthebetterchoice(TCHP). sented.Thankstothegoodselectionontrackstheperfor-
– The Jet Probability algorithm relies on the IP/σ(IP) mance of the taggers are not compromised by the pileup
measurement of all tracks in a jet. One can use the events.
negative tail of the IP/σ(IP) distribution to extract the
probabilitydensityfunction(PDF)fortracksnotcom-
ing from b/c-jets. By integrating on the PDF, we can 5 Physics results
compute the probability for tracks to originate from
the PV. Then combining the probability of the tracks Manymeasurementshavebeenobtainedusingtheb-tagging
√
we can assign to the jet a probability to come from algorithmsat s=7TeV.Someofthemusedtheb-tagging
ConferenceTitle,tobefilled
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