Measurement of the Jet Energy Scale in the CMS experiment ... - IIHE

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30 CHAPTER 3: Object Reconstructionin a 5×5 crystal window around it [63]. Nominally electrons radiate photons whiletraversing the tracker material. About 35% of the electrons radiate more than 70% oftheir initial energy before reaching the ECAL. The radiated photons reaching the ECALspread their energy in the φ direction and this makes the situation more complicatedto cluster the energy of such electrons. The idea is to collect the electron energy bymaking a cluster of clusters, the so called supercluster, along a φ road which containsthe bremsstrahlung photons emitted along the electron trajectory in the tracker.3.1.1 Electron Clustering AlgorithmsThe basic Hybrid and Island clustering algorithms can be used for electrons in theECAL barrel and endcap respectively [64]. The main step for both of these algorithmsis to make clusters. A cluster is created by looking for a bump of deposited energyaround a local energy maximum called the seed crystal. For the barrel electrons, theenergy is collected in dominoes made from 3 or 5 crystals in η. Dominoes centeredaround the seed crystal make a cluster. For the endcap electrons, instead of makingdominoes, the search for energy deposited around the seed crystal is started cell by cell.The energy of each crystal is added to the cluster until a rise in the energy is observed.The final step in the clustering algorithms is simply to collect the nearby clusters inthe φ direction in order to recover the radiation emitted along the trajectory of theelectron.3.1.2 Electron Track ReconstructionHaving clustered the energy of an electron, the next step is to reconstruct and associatea track to it. The starting point for reconstructing a track in the tracker is to find twohits in the pixel detector. The two pixel hits that are found serve as a seed to build anelectron track in the silicon tracker detector.In order to reduce the number of possible hit combinations that can form a candidateseed for reconstructing the electron track, the search for seeds is restricted to a regionwhich is compatible with a supercluster in the ECAL. This approach of superclusterdrivenpixel seed finding has the advantage of increasing the purity of the sample ofcandidate electron tracks. Hits in the pixel layers are predicted by backward propagationof the position of the supercluster through the magnetic field towards the pixeldetector. A first compatible hit is then looked for in the innermost pixel layer. Thepredicted trajectory is then propagated to look for a second pixel hit in the next pixellayer. When two hits are found, a seed is generated.Starting from the seed and using a Bethe Heitler modeling of the electron energy losses,a trajectory is created and compatible hits on the next silicon layers are searched for.The procedure is stopped if no hit is found in two subsequent layers. The compatibilityamong the predicted trajectory and the measured hits is checked using a fit methodcalled the Gaussian Sum Filter (GSF) and is defined in terms of a χ 2 value. Finallythe best track candidate with the smallest χ 2 and having a minimum of five hits is keptand associated to the electron.Besides to the supercluster-driven pixel seed finding method, there is a tracker-driven
CHAPTER 3: Object Reconstruction 31seeding algorithm which has been developed for non-isolated electrons [65]. In a trackerdrivenseeding procedure, the reconstruction of the track of an electron starts from thetracker and then a supercluster in the ECAL is matched to the reconstructed track.The performance plot which compares the efficiency to reconstruct the track of theelectron is shown in Figure 3.1.Figure 3.1: Electron seeding efficiency (solid line) as a function of the generated electronη (left) and generated electron p T (right) for electrons in a simulated sample of Z →e + e − events [66]. The individual contributions from the supercluster-driven (dashedline) and from the tracker-driven (dotted line) seeding algorithms are also shown.As it is seen from Figure 3.1, the electron seeding efficiency obtained from thetracker-driven approach is higher for the low-p T electrons, as aimed for. Also twodrops observed in the η distribution of the electron seeding efficiency refer to thetransition region between the ECAL barrel and the ECAL endcaps. The lower efficiencyobtained from the supercluster-driven approach compared to the efficiency derived fromthe tracker-driven approach is mainly due to an inefficiency in the reconstruction ofsuperclusters in the transition region.3.1.3 Electron Momentum DeterminationThe final electron momentum is obtained by combining the energy measurement providedby the electromagnetic calorimeter with the momentum measurement at thevertex provided by the tracker. If the two measurements are comparable, then a combinationof both energy and momentum measurements is used as an estimate of thefinal electron momentum. Otherwise, if they disagree significantly, the energy measurementfrom the ECAL sub-detector is taken as the final estimation given that itexceeds 15 GeV. In case that the ECAL measures an energy below 15 GeV, then themomentum measured by the tracker is used and assigned to the electron. The idea behindthis is that in particular at energies of around 15 GeV and below, the momentumestimation from the tracker is more precise than the energy measurement provided by
Page 1: Vrije Universiteit BrusselFaculteit
Page 5 and 6: IntroductionThe Standard Model of p
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CHAPTER 5: Estimating of the Jet En
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2χ / ndf18.76 / 7p0 9.492e-08 ±9.
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Chapter 6ConclusionThe Standard Mod
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CHAPTER 6: Conclusion 129The method
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Bibliography[1] M. E. Peskin and D.
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BIBLIOGRAPHY 133[40] The ALICE Coll
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BIBLIOGRAPHY 135[76] The CMS Collab
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SummaryJets appear in the final sta
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Measurement of the Jet Energy Scale in the CMS experiment ... - IIHE

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