Chapter 2 discussed how the ATLAS experiment detects particles trough their energy deposits within complex systems. The deposited energy corresponds to signals directly proportional to the energy loss, or the space-time characteristics, of the interacting particles. These signals are interpreted as sets of end-level objects with similar characteristics. The categorisation is based upon a close correspondence with the physical objects that the detection aims. These sets of reconstructed objects are therefore associated to leptons (electrons or muons), jets, missing energy in the transverse plane, etc. The detailed and formal definition of each reconstructed object was thoroughly detailed in Sec. 3.5. However, these definitions are subject to several uncertainties that arise from the detection processes which underpin their construction. A complex set of parameters is used for calibrating the reconstructed objects in order to provide the response with respect to the input signals. The knowledge of the detector response is included in complex simulations that are compared with respect to data for well known physical phenomena. Data-to-simulation differences define the level of this knowledge. The following sections discuss how the effect of those differences is included as input on the statistical model which extracts thettγ¯ cross section.

7.2.1 Leptons

Simulation-to-data differences obtained for the lepton trigger, reconstruction and identification efficiencies are corrected by applying scale factors. The scale factors are determined from data as a function of the lepton kinematics. The trigger uncertainty on the efficiency is extracted using a Tag and Probe technique on data fromZ(→``)¯ andW(→eν)decays. The trigger efficiencies are given as a function of the different single lepton triggers or run periods [155]. The impact on the t¯tγ selection efficiency is determined using simulations. Varying the scale factor values (within1σ of their uncertainty) determines the effect on thet¯tγ selection efficiency.

The accuracy of the lepton scale and lepton resolution has been studied in data fromZ(→``)¯ decays. In the case of electrons, smearing corrections to the energy scale in data and to the en-ergy resolution in simulations are applied [107]. For muons, both momentum scale and smearing corrections are applied in the simulation [112]. The impact of the electron (muon) energy (mo-mentum) scale on the efficiency is evaluated by shifting up and down the electron (muon) energy (momentum) by the corresponding relative scale uncertainty. The effect is document in Fig. 7.6.

The same procedure is applied to evaluate the systematic uncertainty associated with the lepton resolution. Both lepton scale and resolution uncertainties are propagated to the calculation of ETmiss.

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Figure 7.6: Electron (muon) energy (momentum) scale variations as a function of the photon energy in the transverse plane. In each frame the top distributions show the normalised difference of the up (dotted line) and down (dashed line) variation with respect to the nominal t¯tγ sample (continuous line). The filled histogram corresponds to the statistical uncertainty of the simulated sample. The last bin contains any overflow.

7.2.2 Photons

Photons share similar properties with that of electrons. Correction factors are applied to the simulated samples based upon measurements in data of well known radiative phenomena. These factors are varied within the measured uncertainties.

Photon identification efficiency

The accuracy of the photon identification has been studied from radiativeZ(→``γ)¯ decays. These decays offer the possibility to study the extrapolation from the electron objects to the photon objects [111].

Photon energy scale and resolution

In the same way as done for electrons, normalisation corrections to the photon energy scale are applied. Data-to-simulation discrepancies are corrected by applying correction factors [156]. The corresponding systematic uncertainties on t¯tγ selection efficiency are estimated by varying the photon energy scale and energy resolution scale factors according to their uncertainties. Figure 7.7 shows the effect of the variations to the photon energy scale correction factors. Figure 7.8 shows the effect of the variation of the photon energy resolution correction factors within their uncertainties.

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Figure 7.7: Effect of the photon energy scale in the t¯tγ selection efficiency as a function of the photon energy in the transverse plane. The distribution on the left (right) shows the effect in the electron (muon) channel. The last bin contains any overflow.

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Figure 7.8: Effect of variations of the photon energy resolution in thet¯tγ selection efficiency as a function of the photon energy in the transverse plane. The distribution on the left (right) shows the effect in the electron (muon) channel. The last bin contains any overflow.

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7.2.3 Jets

Jet reconstruction efficiency

The jet reconstruction efficiency is measured as the fraction of jets built from tracks reconstructed by the Inner Detector (ID) matched to jets reconstructed by the calorimeters using anin-situTag and Probe technique. The efficiencies measured in a sample of minimum bias events shows a good agreement overall between data and simulations except at lowpT(j). The systematic uncertainty from thein-situ determination (2% forpT(j)<30 GeV and negligible for higher momenta [113]) is larger than the observed shift. The observed difference between data and simulations is applied (to simulations) by discarding randomly a fraction of the jets taken within the inefficiency range.

The effect as a function of the photon energy in the transverse plane is shown in Fig. 7.9. It can be seen that the effect is small but not negligible. In the range ET(γ) >20 GeV it corresponds to a < 1% and 0.1% uncertainty on the selection efficiency in the electron and muon channels respectively.

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Figure 7.9: Differences of track based each frame the top distributions show the normalised differ-ence of the up (down) variation with respect to the nominalt¯tγ sample in a dotted (dashed) line.

The dashed area corresponds to the statistical uncertainty of the simulated sample. The last bin contains any overflow.

Jet energy scale

The jets used in this analysis are calibrated at the EM scale with the EM+JES scheme [157]. The jet calibration corrects for detector effects on the jet energy measurement as non compensating calorimeter, dead material, leakage and out-of-calorimeter jet cone. The calibration is implemented as energy correction factors (so called jet response factors) derived from simulations in different pseudorapidity regions. The uncertainty on the jet energy scale is derived by combining infor-mations from the single hadron response measured with in-situ techniques and with single pion test-beam measurements. They include uncertainties on the amount of material, the description of the electronic noise and the simulation choice [113]. The application of the uncertainty includes terms for flavour composition, flavour response and close-by jets. The effect on the t¯tγ selection efficiency as a function of ET(γ), see Fig. 7.10, is estimated on t¯tγ simulations by shifting the

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Figure 7.10: Effect of shifted values for the jet response factors within their uncertainty on the photon energy in the transverse plane. The effect on the electron (muon) channel is shown on the left (right). The last bin contains any overflow.

correction factors within their uncertainties.

Jet energy resolution

Dijet balance and bi-sector techniques2show a good agreement between data and simulations [158].

The jet energy resolution systematic uncertainty to the t¯tγ selection efficiency is evaluated by smearing the simulated jets by one sigma around the systematic uncertainties of the measured resolution, see Fig. 7.11.

Jet vertex fraction

The systematic uncertainty associated to the cut on the jet vertex fraction(PJVF(PV)), see Eq. 3.3, is obtained by applying simultaneously up and down variations to the nominal scale factors3. 7.2.4 Missing energy in the transverse plane

The uncertainties on the energy scale and resolution of leptons, jets and photons are propagated to the ETmisscalculation. Two additional sources, and specific to the ETmiss, are considered.

Soft jets and cell out terms

The systematic uncertainty associated with these ETmiss components is estimated by varying the energy scale of soft jets (7GeV< pT(j)<20GeV) and calorimeter clusters or cells not associated with any reconstructed object (cell out) within their respective uncertainties. These two terms are fully correlated and thus no distinction is made. The effects of the up and down jet energy scale

2These techniques involve the decomposition of the highest-pTjets in four-vector projections orthogonal to each jet’sη, φplane.

3These are the the jet selection efficiency and inefficiency, the pile-up jet rejection efficiency and inefficiency.

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Figure 7.11: Estimation of the effect on thet¯tγ selection by smearing the simulated jet resolution by one sigma around the measured energy resolution in dijet balance an bi-sector techniques.

The effect of the electron (muon) channel is shown on the left (right). The last bin contains any overflows.

variations on thet¯tγ selection efficiency is shown on Fig. 7.12. The effect is small across allET(γ) ranges, and overall is found to be 0.2%in the electron channel and 0.1%in the muon channel.

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Figure 7.12: Effect of the jet energy scale up and down variations for jets with7GeV< pT(j)<

20GeV. as a function of the photon energy in the transverse plane. The effect on the electron (muon) channel is shown on the left (right). The last bin contains any overflow.

Pile-up

The systematic uncertainty in theETmiss due to multiple interactions per bunch crossing (pile-up) is computed by varying the jet, soft jet and cell out terms by 6.6%. The latter uncertainty has been determined by comparing the sum of the MET soft-terms (by excluding events with jets with pT >20 GeV) for data and MC inZ →µµ decays in differentη regions. The effect is small and

it is summarised in Fig. 7.13 for both electron and muon channels independently.

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Figure 7.13: Dependence of the ETmiss calculation to pileup conditions as a function of ET(γ).

The plot on the left (right) shows the dependance in the electron (muon) channel. The last bin contains ay overflow.

7.2.5 Heavy flavour jet identification.

The algorithms identifying the jet flavour (b-tagging) associate a jet as being originated from a b-quark with a given efficiency, see Sec. 3.6. The efficiency and the probability of mis-tagging a light jet as a b-jet (mistag rate) are extracted from data with different complementary methods.

The MV1b-tagging mathod is based on a neural network using the output weights of multiple b-tagging algorithms4. Data-to-simulations discrepancies are corrected with the applications of scale factors [115, 116]. The uncertainty on thettγ¯ selection efficiency is determined from simulations by varying these scale factors within their uncertainties and it is found to be around 5% of the efficiency.

Dans le document Enlightened Top Quark: measurements of the ttγ cross section and of its spectrum in transverse energy of the photon in the single lepton channel at √s = 7 TeV in 4.59 fb−1 of pp collision data collected with the ATLAS detector (Page 145-151)