Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at √s = 8 TeV
CMS Collaboration, Vardan Khachatryan, Albert M. Sirunyan, Armen Tumasyan, Wolfgang Adam, Thomas Bergauer, Marko Dragicevic, Janos Erö, Markus Friedl, Rudolf Fruehwirth, Vasile Mihai Ghete, Christian Hartl
Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at s = 8 TeV -The diphoton resonance and Higgs results from the CMS Detector of the LHC M.I. Pedraza-Morales - Recent citations ABSTRACT: A description is provided of the performance of the CMS detector for photon reconstruction and identification in proton-proton collisions at a centre-of-mass energy of 8 TeV at the CERN LHC. Details are given on the reconstruction of photons from energy
... its in the electromagnetic calorimeter (ECAL) and the extraction of photon energy estimates. The reconstruction of electron tracks from photons that convert to electrons in the CMS tracker is also described, as is the optimization of the photon energy reconstruction and its accurate modelling in simulation, in the analysis of the Higgs boson decay into two photons. In the barrel section of the ECAL, an energy resolution of about 1% is achieved for unconverted or late-converting photons from H → γγ decays. Different photon identification methods are discussed and their corresponding selection efficiencies in data are compared with those found in simulated events. JINST 10 P08010 are included. The interactions used to simulate pileup are generated with PYTHIA 6.426 , the same version that is used for other purposes as described below. Samples of simulated Higgs boson events produced in gluon-gluon and vector-boson fusion processes are obtained using the next-to-leading-order matrix-element generator POWHEG (version 1.0) [7-11] interfaced with PYTHIA. For the associated Higgs boson production with W and Z bosons, and with tt pairs, PYTHIA is used alone. Direct-photon production in γ + jet processes is simulated using PYTHIA alone. Nonresonant diphoton processes involving two prompt photons are simulated using SHERPA 1.4.2  . The SHERPA samples are found to give a good description of diphoton continuum events accompanied by one or two jets. To complete the description of the diphoton background in the H → γγ channel, the remaining processes where one of the photon candidates arises from misidentified jet fragments are simulated with PYTHIA. The cross sections for these processes are scaled to match their values measured in data, using the K-factors at 8 TeV that were obtained at 7 TeV [13, 14] . Simulated samples of Z → e + e − and Z → µ + µ − γ events, generated with MADGRAPH 5.1 , SHERPA, and POWHEG , are used for some tests, for comparison with data, and for the derivation of energy scale corrections in data and resolution corrections in the simulations. The simulation of the ECAL response has been tuned to match test beam results, and uses a detailed simulation of the 40 MHz digitization based on an accurate model of the signal pulse as a function of time. The effects of electronics noise, fluctuations due to the number of photoelectrons, and the amplification process of the photodetectors are included. The simulation also includes a spread of the single-channel response corresponding to the estimated intercalibration precision, an additional 0.3% constant term to account for longitudinal nonuniformity of light collection, and the few nonresponding channels identified in data. The measured intercalibration uncertainties range from 0.35% in most of the barrel, to 0.9% at the end of the fourth barrel module, and 1.6% in most of the region covered by the endcaps with a steep rise for |η| > 2.3. As a general rule, for the simulation of data taken at 7 and 8 TeV, the response variation with time is not simulated. However, for the simulation of photon signals and Z-boson background samples used for data-MC comparisons of the photon energy scale, energy resolution, and photon selection, two refinements are implemented: the changes in the energy-equivalent noise in the electromagnetic calorimeter during the data-taking period are simulated, and a significantly increased time window (starting 300 ns before the triggering bunch crossing) is used to simulate out-of-time pileup. These refinements improve the agreement between data and simulated events, seen when comparing distributions of shower shape variables, and they provide improved corrections to the energy measurement. Photon reconstruction Photons for use as signals or signatures in measurements and searches, rather than for use in the construction of jets or missing transverse energy, are reconstructed from energy deposits in the ECAL using algorithms that constrain the clusters to the size and shape expected for electrons and photons with p T 15 GeV. The algorithms do not use any hypothesis as to whether the particle originating from the interaction point is a photon or an electron, consequently electrons from Z → e + e − events, for which pure samples with a well defined invariant mass can be selected, can provide -4 -2015 JINST 10 P08010 excellent measurements of the photon trigger, reconstruction, and identification efficiencies, and of the photon energy scale and resolution. The reconstructed showers are generally limited to a fiducial region excluding the last two crystals at each end of the barrel (|η| < 1.4442). The outer circumferences of the endcaps are obscured by services passing between the barrel and the endcaps, and this area is removed from the fiducial region by excluding the first ring of trigger towers of the endcaps (|η| > 1.566). The fiducial region terminates at |η| = 2.5 where the tracker coverage ends. The photon reconstruction proceeds through several steps. Sections 4.1, 4.2, and 4.3 cover the intercalibration of the individual channels, the clustering of recorded energy signals resulting from showers in the calorimeter, and the energy assignment to a cluster. Section 4.4 discusses the procedure used in the H → γγ analysis to (i) obtain corrections for fine-tuning the photon energy assignment in data, and (ii) tune the resolution of simulated photons reconstructed in MC samples. Section 4.5 examines the resulting photon resolution in data and in simulation. Section 4.6 discusses the estimation of the uncertainty in the energy scale after implementing the corrections obtained in section 4.4.