LHC

b-C

ON

F-20

12-0

1603

/04/

2012

LHCb-CONF-2012-016June 21, 2012

Measurement of jet production inZ0/γ∗ → µ+µ− events at LHCb in√

s = 7 TeV pp collisions

The LHCb collaboration 1

Abstract

The first LHCb measurement of Z0/γ∗ + jet production is presented. This mea-surement is performed in the Z0/γ∗ → µ+µ− channel, and is normalised to the totalinclusive Z0/γ∗ → µ+µ− cross-section, based on 1.02 ± 0.04 fb−1 of data collectedin 2011. The results are given for a fiducial acceptance inside the LHCb acceptance,defined as 2.0 ≤ η(µ) ≤ 4.5, pT(µ) ≥ 20 GeV, and with the reconstructeddimuon mass in the range 60 ≤ M ≤ 120 GeV. Jets are reconstructed using theanti-kT algorithm with R = 0.5, and corrected to the hadron level. Jets in ourfiducial acceptance are required to have 2.0 ≤ η ≤ 4.5 and pT ≥ 10 GeV, and beseparated from decay muons of the Z0/γ∗ by a distance ∆R(µ, jet) ≥ 0.4 in η − φspace. The cross-section ratio of Z0/γ∗(→ µ+µ−) + jet(s) events to Z0/γ∗ → µ+µ−

events is measured as 0.229±0.011. We also present the jet multiplicity distribution,and the Z0/γ∗ rapidity and pT distributions for the Z0/γ∗ + jet(s) events. Com-parisons are made to NLO predictions. Our results are consistent with StandardModel predictions.

1Conference report prepared for Physics at LHC 2012, 4-9 June 2012, Vancouver, Canada; Contactauthors: William Barter, barter@hep.phy.cam.ac.uk, and Albert Bursche, albert.bursche@physik.uzh.ch.

1 Introduction

The measurement of Z0/γ∗(→ µ+µ−) + jet(s) events (referred to from here as Z0 + jetevents) at LHCb complements the analysis of Z0/γ∗ → µ+µ− events already presented [1].Z0 + jet measurements provide a good test of perturbative Quantum Chromodynamics(pQCD). These measurements are also sensitive to Parton Distribution Functions (PDFs)in the low Bjorken-x and large Q2 region of phase space. This region of phase space isrelatively poorly constrained by existing measurements [2]. Z0 + jet measurements inthe forward region probed by LHCb are also sensitive to multi-jet distributions, andconsequently such measurements allow tests of different Monte Carlo generators. In suchmeasurements, LHCb plays a complementary role to ATLAS and CMS, which selectmuons with pseudorapidity, |η(µ)| < 2.4.

The analysis is based on data collected in 2011 corresponding to an integrated lumi-nosity of 1.02 ± 0.04 fb−1. We present results for the jet multiplicity distribution, andthe Z0 rapidity and transverse momentum (pT) distributions in inclusive Z0 + jet events2.The fiducial acceptance used in this analysis for Z0 selection is identical to the one used inRef [1]. Jets are reconstructed using the anti-kT jet clustering algorithm [3], with a radiusparameter R = 0.5, and are required to have3 2.0 ≤ η(jet) ≤ 4.5, and pT(jet) ≥ 10 GeV.We require jets to be well separated from the muons in the Z0 decay: ∆R(µ, jet) ≥ 0.4,where ∆R is the separation in η − φ space. The distributions presented are normalisedto the total inclusive Z0/γ∗ → µ+µ− cross-section in data. This reduces the effect ofsystematic uncertainties.

2 LHCb detector

The LHCb detector [4] is a single-arm forward spectrometer covering the pseudorapid-ity range 2 < η < 5, designed for the study of particles containing b or c quarks. Thedetector includes a high precision tracking system consisting of a silicon-strip vertex de-tector (VELO) surrounding the pp interaction region, a large-area silicon-strip detectorlocated upstream of a dipole magnet with a bending power of about 4 Tm, and threestations of silicon-strip detectors and straw drift-tubes placed downstream. The com-bined tracking system has a momentum resolution ∆p/p that varies from 0.4% at 5 GeVto 0.6% at 100 GeV, and an impact parameter resolution of 20 µm for tracks with hightransverse momentum. Charged hadrons are identified using two ring-imaging Cerenkovdetectors. Photon, electron and hadron candidates are identified by a calorimeter systemconsisting of scintillating-pad and pre-shower detectors, an electromagnetic calorimeterand a hadronic calorimeter. Muons are identified by a muon system composed of ironand gas chambers. The trigger consists of a hardware stage, based on information fromthe calorimeter and muon systems, followed by a software stage which applies a full event

2We define inclusive Z0 + n jet events to refer to events with at least n jets. Exclusive Z0 + n jetevents refers to events containing exactly n jets. Inclusive Z0 + jet events are events with a Z0 and atleast one jet.

3Throughout this note we use units such that c = 1.

1

reconstruction. To avoid the possibility that a few events with high occupancy dominatethe CPU time of the software trigger, a set of global event cuts (GEC) is applied on thehit multiplicities of most subdetectors used in the pattern recognition algorithms.

3 Data, simulated event samples, and theoretical

predictions

3.1 Data

The analysis presented here is based on data collected in 2011 with the LHCb detector.The selection corresponds to an integrated luminosity of 1.02± 0.04 fb−1.

3.2 Simulated event samples

Simulated samples are used to develop the event selection, estimate the backgrounds, crosscheck the efficiencies and to account for the effect of the underlying event. The Pythia6.4 [5] generator, configured as described in Ref. [6], with the CTEQ6ll [7] parametrisationfor the PDFs is used to simulate the hard process.

The hard partonic interaction is calculated in leading order pQCD and higher orderQCD radiation is modelled using initial and final state parton showers in the leading logapproximation [8]. The fragmentation into hadrons is simulated in Pythia by the Lundstring model [9]. All generated events are passed through a Geant4 [10][11] based detec-tor simulation, trigger emulation and event reconstruction chain of the LHCb experiment.The analysis used both Z0/γ∗(→ µ+µ−) + jet and Z0/γ∗ → µ+µ− samples.

3.3 Parton level predictions

Predictions are made at NNLO (with respect to the inclusive Z0 cross-section - this givesa NLO prediction of the Z0 + jet cross-section) using the FEWZ [12] generator and theMSTW08 NNLO PDF sets [13] for the overall ratio of the Z0 + jet cross-section to theZ0 cross-section, and for the ratio as a function of Z0 rapidity. The scale uncertaintiesare estimated by varying the renormalisation and factorisation scales by factors of twoaround the nominal value which is set to the Z0 mass. Note that PDF uncertainties aretaken at 68% confidence level.

These theory predictions are calculated at the parton level with the same fiducialregion as for the analysis. For the relevant distributions, there is interest in comparingthe hadron level results to the parton level predictions.

4 Jet reconstruction and detection

Input particles are selected, to be clustered into jets using a particle flow (PF) algorithm.It uses information from tracks where possible, and only uses calorimeter information

2

where there is no track information (for example, for neutron and photon contributions).The following inputs are used:

• Tracks reconstructed in different parts of the tracking system. If the track containshits in the VELO then it is only used as an input if the track is assigned to thesame primary vertex (PV) as the Z0 candidate.

• Short lived neutral hadron resonances (V 0s) from the same PV as the Z0,

• Reconstructed ECAL clusters assumed to be photons (which are well separated fromtracks to avoid double counting),

• Reconstructed HCAL clusters assumed to be neutral hadrons (which are well sepa-rated from tracks to avoid double counting).

The muons from the decay of the Z0 are excluded from the PF algorithm.The input particles are then clustered into jets using the FastJet [14, 15] interface,

and the anti-kT jet clustering algorithm [3] (with R = 0.5). This algorithm provides thekinematic variables of the jet. Plots illustrating the jet reconstruction performance areincluded as an appendix to this note.

The jet energies and momenta are then corrected back to the hadron level, to accountfor possible mis-measurement of the jet energy. Several effects mean that reconstructedjet energy does not correspond to the true hadron level energy of the jet, for examplecalorimeter cluster response, energy losses, and energy contributions from noise and pile-up particles. We correct the jet energy, keeping the jet direction unchanged using thefollowing parametrisation

Ecorr = kcorr(puncorrT , η, nPV)× Euncorr. (1)

We correct the jet transverse momentum by the same factor. The factors kcorr arecalculated from simulation in seven regions of pseudorapidity (from 2.0 to 4.8 in bins ofwidth 0.4), three ranges of number of primary vertices (nPV =1, 2 and ≥ 3), and in pT

bins (with the number of bins dependent on the pseudorapidity and number of primaryvertices). kcorr typically takes values around 1.1.

5 Event selection

5.1 Z0 selection

We use the same selection as in Ref [1]. Both reconstructed decay muons in the eventmust satisfy the following criteria:

• pT(µ) ≥ 20 GeV,

• 2.0 ≤ η(µ) ≤ 4.5,

3

• that the muon candidates have compatible hits in the muon stations and are asso-ciated to a track with a good track fit quality,

• have an uncertainty of less than 10% on the momentum as calculated from the trackfit.

In addition, we require:

• the invariant mass of the dimuon candidates, M(µ+µ−) must lie in the range60 ≤ M(µ+µ−) ≤ 120 GeV,

• at least one of the muons is responsible for a single muon trigger which selects eventscontaining at least one muon with pT ≥ 10 GeV.

5.2 Jet selection

There are certain jet identification cuts required to reject isolated leptons and photons.The hardest particle in the jet is required to carry less than 80% of the jet’s energy, thethree hardest particles should carry less that 90%, and there should be at least threeparticles in the jet pointing to the PV.

As well as these jet ID requirements on reconstructed jets, we also place kinematiccuts, in order to select jets within the LHCb fiducial acceptance:

• pT(jet) ≥ 10 GeV

• 2.0≤ η(jet) ≤4.5

We also select jets which are well separated in the event from the decay muons of the Z0

by requiring that ∆R(µ, jet) ≥ 0.4, where ∆R is the separation of the jet and the muonin η− φ space. This removes jets which contain significant final state radiation (from thedecay muons of the Z0).

These kinematic cuts form the fiducial acceptance in which the measurements aremade.

5.3 Jet distributions

The pseudorapidity and pT distribution of the leading jet in each event is shown forboth data and the Pythia Monte Carlo simulation in Figs. 1 and 2. In these figures,the simulation is scaled to have the same area as the data. These distributions are notcorrected for inefficiencies in detection, and are presented at the level of reconstructed Z0

+ jet events. The shapes of these distributions in data and simulation agree reasonably.

4

(Jet) [GeV]T

p20 40 60 80 100

Num

ber

of C

andi

date

s

1

10

210

310 = 7 TeVs

preliminaryLHCb

Data

Simulation

Figure 1: The reconstructed pT distribution of the leading jet in each Z0 + jet candidatein data (black) and the Pythia Monte Carlo simulation (red). Simulation is scaled tohave the same integral as data in the range presented. This distribution is not correctedfor inefficiencies in detection. The jet energy energy correction has been applied in bothdata and simulation.

6 Background

The background analysis builds upon the analysis of the background performed in thepaper on inclusive Z0/γ∗ → µ+µ− production [1]. The background level, B/(S+B) (whereB is the number of background candidates and S is the number of signal candidates) inthe inclusive Z0 production analysis was estimated to be 0.0025 ± 0.0005 [1]. This wascalculated by considering different sources and calculating their contribution.

Rather than perform a new background estimate for this measurement, we quantifyour background relative to the level in [1]. The dimuon mass spectrum for inclusiveZ0 candidates in 2011, and the Z0 + jet candidates in 2011, are overlaid in Figure 3,along with the ratio of the two distributions. The two distributions are similar: anyenhancement in background in the Z0 + jet sample (relative to the inclusive Z0 sample)is small. A straight line fit to the ratio is consistent with being flat, also suggesting thatthere is little background enhancement. To quantify the effect, we use the shape of themass spectrum in non jet events as a template. We fit this and an exponential (to accountfor any enhanced background) to the mass spectrum in jet events. Using this method, weestimate the background level (B/(S + B)) in the Z0 + jet sample to be 0.0031± 0.0006:consistent with the background level in [1].

5

(Jet)η2 2.5 3 3.5 4 4.5

Num

ber

of C

andi

date

s

0

200

400

600

800

1000

1200

1400

1600

= 7 TeVs

preliminaryLHCb

Data

Simulation

Figure 2: The reconstructed pseudorapidity distribution of the leading jet in each Z0 +jet candidate in data (black) and the Pythia Monte Carlo simulation (red). Simulationis scaled to have the same integral as data in the range presented. This distribution isnot corrected for inefficiencies in detection. The jet energy correction has been applied inboth data and simulation.

7 Efficiencies and resolution

7.1 Z0 reconstruction and trigger efficiencies

For this analysis, the muon track finding, trigger and GEC efficiencies are calculated fromdata. The trigger efficiency is calculated using a Tag and Probe method (described in [1])while the GEC distribution is obtained by superimposing signal events without pileupwith pileup events (using the same method as in [16]). The tracking efficiency is alsocalculated using a Tag and Probe method (described in [1]). Muon ID efficiencies aretaken from Monte Carlo simulation, but a systematic uncertainty is taken into accountbased on the consistency with a data-driven method.

The efficiencies may deteriorate in high multiplicity events. To account for this theefficiencies are calculated in jet multiplicity bins. The muon tracking and muon ID efficien-cies were also computed as a function of the muon pseudorapidity. The data are correctedfor the inefficiencies in candidate detection by suitably weighting the candidates on anevent by event basis.

7.2 Bin-to-bin migrations in jet multiplicity

The distributions we measure at the detector level differ from the true distributions due tothe finite jet energy resolution and inefficiencies of jet reconstruction and identification.

6

There is a migration of events with n true jets to events with m measured jets. Tomeasure the truth level distribution we need to undo this effect. Truth jets are producedby running the same anti-kT jet clustering algorithm over all the final state particlesproduced by simulation (before the effect of the detector is simulated), apart from theparticles which are directly produced from the decay of the Z0.

7.2.1 Jet multiplicity distribution

We correct the reconstructed jet multiplicity back to the level of the true jet multiplicity insimulation. For this correction we calculate a bin-to-bin migration matrix, from 4 bins ofthe measured jet multiplicity to the same 4 bins at the truth level. Before performing thebin-to-bin migrations on the efficiency corrected data we iteratively reweight the MonteCarlo corrections to describe data. This unfolding is stable, and when undone yields theinitial measured distribution. We cease the reweighting procedure when the correcteddistribution is stable at better than the 2% level with respect to the previous iteration.

7.2.2 Distributions of Z0 rapidity and pT

To determine the Z0 rapidity and pT distributions in jet events we apply an unfoldingprocedure similar to the one described above, but with a 2x2 matrix to describe the caseswith and without jets in the event. For these distributions the bin-to-bin migrations injet multiplicity are now calculated separately in each bin of Z0 rapidity or pT. This givesthe number of Z0 + jet events in each bin of Z0 rapidity and Z0 pT.

7.3 Z0 rapidity and pT resolution

In order to get the final Z0 rapidity and pT distribution, we consider small bin-to-binmigrations in the otherwise fully corrected Z0 rapidity and pT. These bin to bin migrationsare associated with finite resolution of the relevant distributions (where we measure thecandidate to be in one bin of Z0 rapidity or pT, but the candidate would be placed ina different bin if the reconstruction had perfect resolution). These are calculated withPythia event samples, by comparing differences between truth and reconstructed levelvariables. Apart from the highest bin in rapidity, all these migrations are smaller than7%.

8 Systematic uncertainties

We assess the following contributions to systematic uncertainties:

• Jet energy correction: The jet energy correction plays an important role in the distri-butions we present: it determines explicitly the number of jets (with pT ≥ 10 GeV)we see in each event. We calculate a systematic uncertainty associated with thedegree to which the detector response to jets is well modelled in simulation. This

7

is investigated by selecting back-to-back Z0 + jet candidate events where the Z0 pT

should balance the jet pT. We find that the pT balance in data and simulation areconsistent with each other up to within 3%. We therefore assign a 3% systematicuncertainty on the jet energy correction taken from simulation. To evaluate theeffect of this systematic effect, the analysis is repeated with the multiplicative jetenergy correction factors kcorr varied up and down by 0.03 in simulation. The meandifference from the central value is taken as the systematic error.

• Jet energy resolution: We consider the level at which the jet energy resolution indata and simulation ceases to agree, again considering the back-to-back Z0 + jetevents discussed in the preceding point. By smearing the jet energy in simulationusing a Gaussian of width α, we find that we cease to see agreement between dataand simulation when α = 0.08. We therefore repeat the analysis with the jet energyvalues in simulation smeared using a Gaussian of width 0.08. The resulting differencefrom the central value is taken as the systematic error.

• Jet ID selection: We find that the fraction of jets rejected by tightening the Jet IDselection agrees in data and simulation at the 1% level. We therefore assign a 1%systematic error on the jet ID selection. The effect of this systematic error is studiedby rejecting, at random, one jet in 100 in simulation, and repeating the analysis.The difference in the final results before and after applying this rejection sets thesystematic error.

• Trigger and GEC efficiencies: We treat the statistical errors associated with theefficiency determination as systematic uncertainties.

• Muon tracking efficiency: The tag and probe method to determine the muon track-ing efficiency is accurate to 1% (this number is found from applying the method tosimulation, and comparing the results to efficiencies calculated using truth level in-formation). We therefore combine this with the statistical uncertainty in determingthe tracking efficiencies from data to set an error on each efficiency calculated. Wealso assign a systematic error to the tracking efficiency based on agreement with theefficiency calculated from simulation.

• Muon ID efficiency: We take the statistical errors associated with the efficiencydetermination and use these to define systematic uncertainties. We also calculatethe Muon ID efficiency from data. This value agrees with the values from simulationsat the level of 1%. We therefore assign an additional systematic error of 1% on themuon ID efficiencies.

• Bin-to-bin migration in jet multiplicity: There are two sources we consider for thesystematic errors associated with the bin-to-bin migration in jet multiplicity. Thecorrected distribution is stable to better than the 2% level in every bin. The variationwith respect to the previous iteration is taken as a systematic error on the migration.

8

The statistical errors associated with the Monte Carlo weights are also included asa second source for the systematic uncertainty.

• Z0 rapidity and pT resolution: The statistical errors associated with the migrationsin Z0 rapidity and Z0 pT discussed in Section 7.3 are also included as a systematicuncertainty.

The effect of the different systematic errors on the jet multiplicity distribution is shownin Table 1. The systematic uncertainty increases with jet multiplicity. This comes fromtwo sources. For the bin-to-bin migration in jet multiplicity and the muon tracking, muonidentification, trigger and GEC uncertainties, there is an increase in the uncertainty atincreased jet multiplicity associated with a larger statistical error when calculating thesystematic uncertainty. Jet related systematic uncertainties necesssarily increase with thenumber of jets in the event, as the relevant corrections are applied to more jets.

9

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date

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Num

ber

of Z

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0.5 = 7 TeVs

preliminaryLHCb

Figure 3: The top plot shows the dimuon mass spectrum in data, for Z0 + jet events(blue) and all Z0 events (red) scaled to the same integral (unity) and overlaid. Below,we show the ratio of the number of Z0 events to the number of Z0 + jet events seen asa function of the dimuon invariant mass. A straight line fit, p0 + p1 ·M(µ+µ−), returnsp0 = 0.17±0.02 and p1 = 0.00001±0.00022 GeV−1. These distributions are not correctedfor any inefficiencies in Z0 or jet detection.

10

Tab

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The

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soci

ated

with

the

diff

eren

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Jet Multiplicity0 1 2 >2

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n J

ets)

0 (

Zσ

-210

-110

1

= 7 TeVs

preliminaryLHCb

Figure 4: Measured jet multiplicity distribution of Z0 events in data. Error bars show thecombination of statistical and systematic uncertainties in quadrature.

9 Measurement of cross-section ratios

9.1 Jet multiplicity

The jet multiplicity distribution, normalised to the total inclusive Z0 cross-section inour fiducial acceptance, is shown in Fig. 4. The results are summarised in Table 2.The jet multiplicities given here are exclusive (so that Z0 + n jets refers to candidateswith exactly n jets), save for the last bin, which contains candidates with three or morejets. The fraction of jet events (with jet pT ≥ 10 GeV, jet pseudorapidity in the range2.0 ≤ η ≤ 4.5, and where the jets are separated from decay muons of the Z0 by adistance ∆R ≥ 0.4 in η − φ space) is 0.229± 0.006± 0.009, where the first uncertainty isstatistical, and the second uncertainty is systematic. This result is compatible with thetheoretical prediction of 0.212+0.006

−0.009 ± 0.016, which has been calculated using FEWZ [12]with MSTW08 PDFs [13]. The first theoretical uncertainty is the PDF uncertainty, andthe second is the scale uncertainty. This prediction is NNLO with respect to Z0 production(so NLO for Z0 + jet production).

9.2 Z0 rapidity and pT

The fully corrected Z0 rapidity is shown in Fig. 5. Overlaid on this plot are the theorypredictions from FEWZ [12], using MSTW08 PDFs [13]. The pT distribution is shownin Fig. 6. The results are summarised in Tables 3 and 4. As for the jet multiplicitydistribution, we normalise the cross-section in each bin to the total Z0/γ∗ → µ+µ− cross-section in our fiducial acceptance. For the Z0 rapidity distribution the theory predictionsagree with the measured values to better than 1σ in all bins, save for the first bin (wherethere is ∼ 1.6σ difference). The experimental uncertainties are comparable in size with

12

Figure 5: The normalised Z0 rapidity distribution measured in Z0 + jet events in data (bluepoints), with the FEWZ+MSTW08 predictions overlaid (the red line gives the centralvalue, whilst the shaded area gives the PDF errors and scale uncertainties combined inquadrature). Data error bars show the combination of statistical and systematics errorsin quadrature. The top plot has a linear scale, whilst the bottom plot uses a logarithmicscale.

the theoretical uncertainties.

13

) [GeV]o(ZT

p0 20 40 60 80 100

]-1

) [G

eV0

(Zσ /

)0 d

pT

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(Z

σd

0

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0.007

= 7 TeVs

preliminaryLHCb

Figure 6: The normalised Z0 pT distribution measured in Z0 + jet events in data. Errorbars show the combination of statistical and systematics errors in quadrature.

14

Tab

le2:

The

rela

tive

jet

mult

iplici

tydis

trib

uti

onin

Z0

even

tsin

2011

LH

Cb

dat

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Jet

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Tab

le3:

The

rela

tive

Z0

rapid

ity

dis

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ution

inZ

0+

jet

even

tsin

2011

LH

Cb

dat

a.

Z0

Rap

idity

2.0-

2.5

2.5-

3.0

3.0-

3.5

3.5-

4.0

4.0-

4.5

dσ(Z

0+

Jet

)

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/σ(Z

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002

0.00

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Tot

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nty

0.00

80.

011

0.00

70.

002

0.00

013

Tab

le4:

The

rela

tive

Z0

p Tdis

trib

ution

inZ

0+

jet

even

tsin

2011

LH

Cb

dat

a.

Z0

p T/

GeV

0-10

10-1

7.5

17.5

-25

25-3

535

-50

50-1

00dσ(Z

0+

Jet

)

dpT

(Z0)

/σ(Z

0)

[GeV

−1]

0.00

580.

0068

0.00

560.

0034

10.

0017

10.

0003

8

Sta

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15Tot

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0000

80.

0000

2

15

10 Conclusions

The cross-section of Z0 + jet production at LHCb has been measured relative to theinclusive Z0 production cross-section, using 1.02±0.04 fb−1 of data. Jets are reconstructedusing the anti-kT jet clustering algorithm with R = 0.5. Jets in our fiducial acceptanceare required to have 2.0 ≤ η ≤ 4.5, pT ≥ 10 GeV, and be separated from decay muons ofthe Z0 by a distance ∆R ≥ 0.4 in η−φ space. The cross-section ratio of Z0/γ∗(→ µ+µ−)+ jet events to Z0/γ∗ → µ+µ− events is measured as 0.229± 0.011. This agrees with thetheory prediction of 0.212+0.006

−0.009±0.016 [12][13]. The jet multiplicity distribution has beenpresented, as have the Z0 rapidity and transverse momentum distributions in the inclusiveZ0 + jet events. This measurement has been made at the hadron level, though comparisonto theoretical predictions at the parton level remains interesting and is presented.

References

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s =7TeV, arXiv:1204.1620.

[2] R. Thorne, A. Martin, W. Stirling, and G. Watt, Parton distributions and QCD atLHCb, arXiv:0808.1847.

[3] M. Cacciari, G. P. Salam, and G. Soyez, The anti-kt jet clustering algorithm, JHEP04 (2008) 063, arXiv:0802.1189.

[4] LHCb Collaboration, A. A. Alves Jr. et al., The LHCb detector at the LHC, JINST3 (2008) S08005.

[5] T. Sjostrand, S. Mrenna, and P. Skands, PYTHIA 6.4 physics and manual, JHEP05 (2006) 026, arXiv:hep-ph/0603175.

[6] I. Belyaev et al., Handling of the generation of primary events in Gauss, the LHCbsimulation framework, Nuclear Science Symposium Conference Record (NSS/MIC)IEEE (2010) 1155.

[7] P. M. Nadolsky et al., Implications of CTEQ global analysis for collider observables,Phys. Rev. D78 (2008) 013004, arXiv:0802.0007.

[8] M. Bengtsson and T. Sjostrand, Parton showers in leptoproduction events, Z. Phys.C37 (1988) 465.

[9] B. Andersson, G. Gustafson, G. Ingelman, and T. Sjostrand, Parton fragmentationand string dynamics, Phys. Rept. 97 (1983) 31.

[10] GEANT4 collaboration, S. Agostinelli et al., GEANT4: A simulation toolkit, Nucl.Instrum. Meth. A506 (2003) 250.

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[11] GEANT4 collaboration, J. Allison et al., Geant4 developments and applications,IEEE Trans. Nucl. Sci. 53 (2006) 270.

[12] R. Gavin, Y. Li, F. Petriello, and S. Quackenbush, FEWZ 2.0: A code for hadronicZ production at next-to-next-to-leading order, Comput. Phys. Commun. 182 (2011)2388, arXiv:1011.3540.

[13] A. Martin, W. Stirling, R. Thorne, and G. Watt, Parton distributions for the LHC,Eur. Phys. J. C63 (2009) 189, arXiv:0901.0002.

[14] M. Cacciari and G. P. Salam, Dispelling the N3 myth for the kt jet-finder, Phys. Lett.B641 (2006) 57, arXiv:hep-ph/0512210.

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[16] LHCb collaboration, Inclusive low mass Drell-Yan production in the forward regionat√

s = 7TeV, LHCb-CONF-2012-013.

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Appendix

The figures in this appendix illustrate the jet reconstruction performance.

(GeV)T

Jet p10 20 30 40

Frac

tion

of je

t ene

rgy

0

0.2

0.4

0.6

0.8

1

= 7 TeVsLHCb Simulation, Charged particles

0π, γ

HCAL clusters

Figure 7: Mean energy fraction of the input particles used in the particle flow algorithm withrespect to the jet transverse momentum.

jetη

2 3 4 5

Jet

Eff

icie

ncy

0

0.2

0.4

0.6

0.8

1

> 5 GeVjet

Tp

> 15 GeVjet

Tp

= 7 TeVs

LHCb simulation

Figure 8: Efficiency of the jet reconstruction and identification measured in simulation. Jetswere selected in Z0 + jet events, where the jet is back-to-back with the Z0. The jet identificationcuts used are those outlined in Section 5.2. Two cuts on the jet transverse momentum areshown.

18

(GeV)jet

Tp

10 20 30 40

Jet

Eff

icie

ncy

0

0.2

0.4

0.6

0.8

1

<5.0jet

η1.5<

<4.5jet

η2.0<

= 7 TeVs

LHCb simulation

Figure 9: Efficiency of the jet reconstruction and identification measured in simulation. Jetswere selected in Z0 + jet events, where the jet is back-to-back with the Z0. The jet identificationcuts used are those outlined in Section 5.2. Two regions of jet pseudorapidity are shown.

(Z, Jet)φΔ0 1 2 3

Even

t Rat

e

00.05

0.10.150.2

0.250.3

0.350.4

0.45

2011 Data

= 7 TeV DatasPreliminaryLHCb

Figure 10: Angular separation between the Z0 and the jet in the transverse plane. The Z0

is identified in muon decays and the jet is reconstructed with the particle flow algorithm, us-ing the selections outlined in Section 5, as well as requiring that pT(Z0) ≥ 10 GeV and thatpT(Second Leading Jet)/pT(Leading Jet) ≤ 0.25.

19

(Z)T

(Jet) / pT

p0 0.5 1 1.5 2

Even

t Rat

e

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

2011 Data

Simulation

= 7 TeV DatasPreliminaryLHCb

Figure 11: Jet transverse momentum divided by Z0 transverse momentum, for the cuts detailedin Figure 10 and the requirement that |∆φ(Z0, Jet)| > 7π/8. The Z0 is identified in muon decays.Good agreement between data and simulation is observed.

η2

34

5

φ -3-2-10123

[GeV

/c]

Tp

01020304050607080

= 7 TeV DatasPreliminaryLHCb Reconstructed Z

Decay MuonsJet

Figure 12: An event display from 2011 data of a Z0 + jet candidate. The reconstructedcandidates have pT(Z0) = 75 GeV and pT(jet) = 64 GeV.

20