5.6.1 Evaluation of the systematic uncertainty using inclusive multi-jets events
In Section 5.5, data-based GS corrections were derived and compared to Monte-Carlo based corrections by applying both corrections to the same sample (the reference Pythia sample). In this section, the approach is dierent: the same GS corrections (here the Monte Carlo-based ones) are applied to both data and nominal Monte Carlo inclusive samples. The dierence between data and simulation therefore re ects dierences in the jet properties used as input to the GS calibration in the inclusive samples. Dierences in the jet properties used as input to GS between data and Monte Carlo simulation may lead to a degradation of linearity when Monte Carlo-based corrections are applied to data. For instance, if the fTile0 property is lower on average in data than in Monte Carlo simulation in some pjetT and η bin, the coe cient applied to jets will be lower in data than in Monte Carlo simulation, since the response decreases with fTile0, as can be seen from Figure 5.11. The response will therefore be lower in data than in simulation.
Figure 5.18 shows the mean value of fPS, fLAr3, fTile0 and width as a function of pjetT in the barrel for data and various Monte Carlo samples: the reference Pythia MC10, Pythia Perugia2010 and Herwig++. The agreement for fTile0 and fPS between data and Pythia MC10 is within 5% over the entire pjetT range. For fLAr3, this agreement is also within 5% except for 20 < pjetT < 30 GeV where a disagreement of 7.5% is observed. A larger disagreement is found for the jet width. Jets are 5% (10%) wider in data than in Monte Carlo simulation at 200 GeV (600 GeV). Figure5.19 shows the standard deviation of the properties as a function of pjetT in the barrel. The agreement between data and Pythia MC10 for fLAr3 and fPS is within 5% over the entire pjetT range. For fTile0and the jet width, disagreements of 10% are observed in some pjetT bins. Similar results are found in the other |η| bins for the layer fractions. For the jet width, the disagreement between data and Monte Carlo simulation is slightly worse in the range 2.1 < |η| < 2.8 than in the other ranges.
Figures 5.18 and 5.19show that the reference Pythia MC10 and the Pythia Perugia2010 tunes agree to within a few percent. The agreement of the Herwig++ sample with data is found to be as good as for the other samples for fLAr3 and fTile0, except for 20 < pjetT < 30 GeV.
For fPS and the jet width, disagreements of 5 to 10% are found between Herwig++ and the other samples for pjetT < 60 GeV. For pjetT > 160 GeV, Herwig++ is found to describe better the width observed in data than the other samples.
The systematic uncertainty can be quantitatively estimated by comparing the dierence in the correction coe cients EGSjet/EEM+JESjet between data and Monte Carlo simulations. Figure5.20 shows the correction coe cient as a function of pjetT in the barrel in data and in the reference Pythia sample after GSL and GS. Figure 5.21 shows the same quantity but as a function of η for 80 < pjetT < 110 GeV. Deviations from the unity in the ratios between data and Monte Carlo simulation as shown in the lower part of Figures 5.20 and 5.21 represent the systematic uncertainty associated to the GS corrections. This uncertainty is added in quadrature to the EM+JES uncertainty [3,116].
The results for all the pjetT and η ranges are the following: for 20 < pjetT < 30GeV and |η| < 2.1, the uncertainty varies from 0.5% to 0.7% depending on the |η| region. For pjetT > 30 GeV and
|η| < 2.1, the uncertainty is lower than 0.5%. For 2.1 < |η| < 2.8, the uncertainty varies from 0.4% to 1% depending on the pjetT bin. For a given pjetT , the uncertainty is higher for 2.1 < |η| < 2.8 than for |η| < 2.1 because of the poorer description of the jet width. For 2.1 < |η| < 2.8 the GSL scheme shows slightly larger dierence than the GS scheme. In general, the uncertainty is
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>Tile0<f pjetT for |η| < 0.6 for data and various Monte Carlo simulations. Jets are calibrated with the EM+JES calibration. The ratio of data over Monte Carlo responses are shown at the bottom.
lower than 1% for 20 < pjetT < 800GeV and 0 < |η| < 2.8.
It is worth noting that the uncertainty coming from the imperfect description of the jet properties described in this section and the dierences between data-based and Monte Carlo-based corrections presented in Section 5.5.3 are not independent. Indeed, the average response after GS in each pjetT and |η| bin, which depends on both the distribution of the properties and the GS corrections, is constrained to be close to the response after the EM+JES calibration. A change in the distribution of a jet property therefore translates into a change in the GS correction as a function of this property such that the average response remains unchanged. The dierences described in Section 5.5.3 are therefore partly caused by dierences in the jet properties.
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) tile1(fσ of pjetT for |η| < 0.6 for data and various Monte Carlo simulations. Jets are calibrated with the EM+JES calibration. The ratio of data over Monte Carlo responses are shown at the bottom.
5.6.2 Evaluation of the systematic uncertainty using γ+jet events
The jet energy scale after each correction in the GS calibration can also be veri ed using other common data-based jet energy scale validation methods. One of them, discussed in this section, is the pT balance method in γ+jet events.
Two methods based on γ+jet events have been described in detail in [133]. The method that balances directly a photon against a jet is used because it is simpler to apply to jets of dierent calibrations and because the other method (MPF) would require a missing transverse energy calculation that accounts for the changes in the jet energy scale introduced by the GS
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>jet EM+JES/Ejet GSL<E
Figure 5.20: Average jet energy after (a) GSL and (b) GS divided by the average jet energy after the EM+JES calibration as a function of pjetT in data and the reference Pythia sample in the barrel. The double ratio (EGS(L)/EEM+JES)data/(EGS(L)/EEM+JES)MC is shown at the bottom.
-3 -2 -1 0 1 2 3
Figure 5.21: Average jet energy after (a) GSL and (b) GS divided by the average jet energy after the EM+JES calibration as a function of η in data and the reference Pythia sample for 80 < pjetT < 110 GeV. The double ratio (EGS(L)/EEM+JES)data/(EGS(L)/EEM+JES)MC is shown at the bottom.
calibration coherently. The measurement is repeated with jets calibrated with the EM+JES calibration and after the application of each of the corrections that build up the GS calibration.
The datasets, event selection and analysis cuts used are the same as in [133]. The response is de ned as the ratio between the jet and the photon transverse momenta. Only one η bin is used (|η| < 1.2) to maximize the available statistics. The approach to evaluate the systematic
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uncertainty is similar to that described in Section 5.6.1: the Monte Carlo-based GS corrections are applied to both data and Monte Carlo simulation and the systematic uncertainty associated to GS is evaluated by computing the data/Monte Carlo ratio of the response after GS relative to EM+JES.
For 25 < pjetT < 45 GeV, the agreement between the response in data and Monte Carlo simulation is 3.2% after EM+JES and 4.2% after GS. For 210 < pjetT < 260GeV, the agreement is 5% after EM+JES and 2.5% after GS. The systematic uncertainty is shown in gure 5.22.
The errors bars represent statistical uncertainties. The systematic uncertainty varies from 1%
at pjetT = 25 GeV to 2.5% for pjetT = 260 GeV. These results are compatible within statistical uncertainty with the uncertainty evaluated using inclusive multi-jets (see Section 5.6.1).
(a) (b)
Figure 5.22: Ratio of the detector response in data and the nominal Pythia sample using direct pT balance in γ+jets events after (a) GSL and (b) GS relative to EM+JES. The jets used have
|η| < 1.2.
5.6.3 Final systematic uncertainty for the GS calibration
Data-driven methods for estimating the systematic uncertainty in events with dierent topology and avour composition (di-jet and γ+jet events) have also been presented, but the statistic available at the time this study was performed was not enough to quantify the uncertainty in such samples with high precision over the entire pT and η range. Therefore, it was decided to quote the systematic uncertainty calculated in the inclusive multi-jets sample as the nal systematic uncertainty on the GS calibration. It was found to be lower than 1% for |η| < 2.8 and 20 < pjetT < 800GeV.
This uncertainty has to be added in quadrature to the EM+JES calibration systematic uncer-tainty. The addition in quadrature was motivated by the fact that the systematic uncertainties for EM+JES and GS can be considered as independent. The possible correlations between the sources of the EM+JES systematic uncertainty [116,3] and the jet properties are brie y discussed below:
• Dead material could be responsible at least partly for the imperfect description of the jet properties, but its contribution to the EM+JES systematic uncertainty is small.
• The calorimeter cell noise threshold can change the cluster shapes and the fake cluster multiplicity, aecting the jet properties distribution. But this eect is only important for
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20 GeV< pT <30 GeV and is of the order of 2%. Added linearly to the GS systematic uncertainty in that bin would lead to a systematic uncertainty of around 2.7%, which is not big.
• The EM+JES non closure re ects the self consistency of the EM+JES calibration proce-dure. Therefore, it does not depend on the jet properties.
• The calorimeter response is mainly intrinsic to the detector and should not depend on the jet physics.
• The EM+JES systematic uncertainty due to physics model and parameters employed in the Monte Carlo generator was evaluated by comparing the nominal Monte Carlo samples with others generators and tunes: Alpgen and Perugia2010. The dierences in the jet properties distributions between the Monte Carlo nominal sample and Perugia2010 were studied and found to be in most bins smaller or equal to the dierences between the nominal samples and data (see Figure5.18). In addition, its contribution to the EM+JES systematic uncertainty is of the order of 1-2%, which is bigger that the systematic uncertainty for GS.
It looks like the contribution to the EM+JES systematic is coming mainly from another eect than dierences in the GS jet properties distributions.
The GS corrections were derived in a Monte Carlo simulation samples with a particular
avour composition (mixture of light quarks, heavy quarks and gluon initiated jets) and with a given selection of isolated jets. Therefore, the GS systematic uncertainty calculated in the inclusive multi-jets sample are only valid under these particular conditions. Extra systematic uncertainties need to be added to account for the dependence of the jet response on the jet topology and avour. Initial studies that can be used in the future (using more statistics) were presented in Sections 5.5.3and 5.6.2.