The validity of the MC-based templates and fitting method is tested by applying the method to the γ -jet data sam-ple and comparing the extracted flavour compositions with the γ -jet Monte Carlo simulation predictions. This sample should contain a considerably higher fraction of light quark jets than the inclusive dijet sample. Figure81shows the fit to the jet width in the γ -jet data for jets with|η| < 0.8 and 60≤ pTjet<80 GeV. The heavy quark jet fractions are fixed to those obtained from the γ -jet Monte Carlo simulation.
Fig. 81 The jet width template fit in a γ -jet data sample using tem-plates derived from the inclusive jet Monte Carlo simulation sam-ple created using the PYTHIA MC10 tune. Jets with |η| < 0.8 and 60≤ pjetT <80 GeV are shown. The fraction of heavy quark jets is taken directly from the MC simulation
The extracted light quark and gluon jet fractions are con-sistent with the true fractions in Monte Carlo simulation, though with large uncertainties, as shown in Table16. Us-ing the ntrkvariable gives consistent results, but with large systematic uncertainties.
18.7 Flavour composition in a multijet sample
The template fit method is also useful for fits to multijet events for various jet multiplicities. These events contain additional jets that mainly result from gluon radiation and hence include a larger fraction of gluon jets than does the γ-jet sample.
For this particular analysis, the templates built from the inclusive jet sample are used to determine the flavour con-tent for each jet multiplicity bin. However, the pTspectrum of the sub-leading jets is more steeply falling than the lead-ing jet pT. An additional systematic uncertainty is estimated to account for the difference in pTspectra. This uncertainty is determined by rederiving templates built with a flat pT
distribution and a significantly steeper pT distribution than
Fig. 82 Fitted values of the average light quark and gluon jet fraction in events with three or more jets as a function of pTjetcalculated using the number of tracks ntrktemplates (a) and the jet width templates (b).
Non-isolated anti-kt jets (0.8≤ Rmin<1.0) with R= 0.6 and with
|η| < 0.8 calibrated with the EM+JES scheme are shown. The frac-tion of heavy quark jets is fixed to that of the Monte Carlo simulafrac-tion.
The flavour fractions obtained in data are shown with closed markers, while the values obtained from the Monte Carlo simulation are shown with open markers. The error bars indicate the statistical uncertainty of the fit. Below each figure the impact of the different systematic ef-fects is shown with markers and the combined systematic uncertainty is indicated by a shaded band
Fig. 83 Fitted values of the average light quark and gluon jet fraction in events with four or more jets as a function of pjetT for isolated anti-kt jets with R= 0.6 and with |η| < 0.8 calibrated with the EM+JES scheme. The fraction of heavy quark jets is fixed from the Monte Carlo simulation. The number of tracks ntrk(a) and the jet width (b)
tem-plate distributions are used in the fits. The flavour fractions obtained in data are shown with closed markers, while the values obtained from the Monte Carlo simulation are shown with open markers. The error bars indicate the statistical uncertainty of the fit. Below each figure the systematic uncertainty is shown as a shaded band
that of the dijet sample. The slope of the steeply falling dis-tribution is taken from the pT of the sixth leading jet in Monte Carlo events with six jets, generated using ALPGEN. The fits are repeated with these modified templates, and the largest difference is assigned as a pjetT spectrum shape sys-tematic uncertainty.
Figure82compares the fractions of light quark and gluon jets obtained with a fit of the jet width and ntrk distribu-tions in events with three or more jets in data and Monte Carlo simulation as a function of pjetT for non-isolated (0.8≤ Rmin<1.0) jets with |η| < 0.8. The higher gluon jet frac-tions predicted by the Monte Carlo simulation are repro-duced by the fit, and the data and the Monte Carlo simu-lation are consistent. The total systematic uncertainty on the measurement is below 10 % over the measured pjetT range.
The average flavour fractions obtained from fitting the jet width and ntrkdistributions in events with four or more jets are shown in Fig.83. In both cases, the extracted fractions are consistent with the Monte Carlo predictions within the systematic uncertainties, and the total systematic uncertainty is similar to the one for the three-jet bin.
The extracted light quark and gluon jet fractions, with the total systematic uncertainty from the width and ntrkfits, are summarised in Fig.84as a function of inclusive jet multi-plicity. The fractions differ by 10 % between the data and the Monte Carlo simulation, but are consistent within uncer-tainties. The total systematic uncertainty is around 10 % for each multiplicity bin. Thus, for the four-jet bin, the flavour dependent jet energy scale systematic uncertainty can be re-duced by a factor of∼10, from about 6 % obtained assuming a 100 % flavour composition uncertainty to less than 1 % af-ter having deaf-termined the flavour composition with a 10 % accuracy. A summary of the flavour fit results using the jet width templates for the different samples is provided in Ta-ble16.
18.8 Summary of jet response flavour dependence
The flavour dependence of the jet response has been stud-ied, and an additional term to the jet energy scale systematic uncertainty has been derived.
A generic template fit method has been developed to reduce this uncertainty significantly for any given sample
Fig. 84 Fitted values of the average light quark and gluon jet fraction as a function of inclusive jet multiplicity with total uncertainties on the fit as obtained using the number of tracks ntrk(a) and the jet width (b) distributions. The fraction of heavy quark jets is fixed from the Monte Carlo simulation. The flavour fractions obtained in data are shown with closed markers, while the values obtained from the Monte Carlo
sim-ulation are shown with open markers. Anti-ktjets with R= 0.6 cali-brated with the EM+JES scheme are used. The error bars indicate the statistical uncertainty of the fit. Below each figure the impact of the different systematic effects is indicated by markers. and the combined systematic uncertainty is shown at the bottom of the figure as a shaded band
of events. Templates derived in dijet events were applied to both γ -jet and multijet events, demonstrating the po-tential of the method to reduce the systematic uncertainty.
The light-flavour portion of the flavour dependent jet energy scale can be reduced from∼6 % to below 1 %.
19 Global sequential calibrated jet response for a quark sample
In this section, the performance of the GS calibration (see Sect. 11) is tested for a γ -jet sample. The jet energy scale after each GS correction can be verified using the in situ techniques such as the direct pTbalance technique in γ -jet events (see Sect. 10.2), where mainly quark induced jets are tested. The flavour dependence of the GS calibration is tested for jets with|η| < 1.2.
The measurement is first made with jets calibrated with the EM+JES calibration and is repeated after the applica-tion of each of the correcapplica-tions that form the GS calibraapplica-tion.
To maximise the available statistics one pseudorapidity bin
is used |η| < 1.2. The Monte Carlo based GS corrections are applied to both data and Monte Carlo simulation. The systematic uncertainty associated with the GS calibration is evaluated by computing the data to Monte Carlo simulation ratio of the response after the GS calibration relative to that for the EM+JES calibration.
For 25≤ pjetT <45 GeV, the agreement between the re-sponse in data and Monte Carlo simulation is 3.2 % af-ter EM+JES and 4.2 % afaf-ter GS calibration. For 210≤ pjetT <260 GeV, the agreement is 5 % after EM+JES and 2.5 % after GS calibration. Therefore systematic uncertain-ties derived from the agreement of data and Monte Carlo simulation vary from 1 % at pTjet= 25 GeV to 2.5 % for pjetT = 260 GeV. These results are compatible within the statistical uncertainty with the uncertainty evaluated using inclusive jet events (see Sect.12.1.3).
The obtained results indicate that the uncertainty in a sample with a high fraction of light quark jets is about the same as in the inclusive jet sample.
20 JES uncertainties for jets
with identified heavy quark components
Heavy flavour jets such as jets induced by bottom (b) quarks (b-jets) play an important role in many physics analyses.
The calorimeter jet response uncertainties for b-jets is evaluated using single hadron response measurements in samples of inclusive jet and b ¯b dijet events. The JES un-certainty arising from the modelling of the b-quark produc-tion mechanism and the b-quark fragmentaproduc-tion can be deter-mined from systematic variations of the Monte Carlo simu-lation.
Finally, the calorimeter pTjet measurement can be com-pared to the one from tracks associated to the jets for inclu-sive jets and identified b-jets. From the comparison of data to Monte Carlo simulation the b-jet energy scale uncertainty relative to the inclusive jet sample is estimated.