ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., Cerda Alberich, L., et al. (2018). Performance of missing transverse momentum reconstruction with the ATLAS detector using proton proton collisions at root s=13 TeV. Eur. Phys. J. C, 78(11), 903–46pp.
Abstract: The performance of the missing transverse (E-T(miss) momentum) reconstruction with the ATLAS detector is evaluated using data collected in proton-proton collisions at the LHC at a centre-of-mass energy of 13 TeV in 2015. To reconstruct E-T(miss), fully calibrated electrons, muons, photons, hadronically decaying tau-leptons, and jets reconstructed from calorimeter energy deposits and charged-particle tracks are used. These are combined with the soft hadronic activity measured by reconstructed charged-particle tracks not associated with the hard objects. Possible double counting of contributions from reconstructed charged-particle tracks from the inner detector, energy deposits in the calorimeter, and reconstructed muons from the muon spectrometer is avoided by applying a signal ambiguity resolution procedure which rejects already used signals when combining the various E-T(miss) contributions. The individual terms as well as the overall reconstructed E-T(miss) are evaluated with various performance metrics for scale (linearity), resolution, and sensitivity to the data-taking conditions. The method developed to determine the systematic uncertainties of the E-T(miss) scale and resolution is discussed. Results are shown based on the full 2015 data sample corresponding to an integrated luminosity of 3.2 fb(-1).
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., Cerda Alberich, L., et al. (2017). Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2. Eur. Phys. J. C, 77(10), 673–30pp.
Abstract: With the increase in energy of the Large Hadron Collider to a centre-of-mass energy of 13 TeV for Run 2, events with dense environments, such as in the cores of highenergy jets, became a focus for new physics searches as well as measurements of the Standard Model. These environments are characterized by charged-particle separations of the order of the tracking detectors sensor granularity. Basic track quantities are compared between 3.2 fb(-1) of data collected by the ATLAS experiment and simulation of protonproton collisions producing high-transverse-momentum jets at a centre-of-mass energy of 13 TeV. The impact of chargedparticle separations and multiplicities on the track reconstruction performance is discussed. The track reconstruction efficiency in the cores of jets with transverse momenta between 200 and 1600 GeV is quantified using a novel, datadriven, method. The method uses the energy loss, dE/ dx, to identify pixel clusters originating from two charged particles. Of the charged particles creating these clusters, themeasured fraction that fail to be reconstructed is 0.061 +/- 0.006 (stat.) +/- 0.014 (syst.) and 0.093 +/- 0.017 (stat.) +/- 0.021 (syst.) for jet transverse momenta of 200-400GeV and 1400-1600GeV, respectively.
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., Cerda Alberich, L., et al. (2017). Performance of the ATLAS Transition Radiation Tracker in Run 1 of the LHC: tracker properties. J. Instrum., 12, P05002–42pp.
Abstract: The tracking performance parameters of the ATLAS Transition Radiation Tracker (TRT) as part of the ATLAS inner detector are described in this paper for different data-taking conditions in proton-proton, proton-lead and lead-lead collisions at the Large Hadron Collider (LHC). The performance is studied using data collected during the first period of LHC operation (Run 1) and is compared with Monte Carlo simulations. The performance of the TRT, operating with two different gas mixtures (xenon-based and argon-based) and its dependence on the TRT occupancy is presented. These studies show that the tracking performance of the TRT is similar for the two gas mixtures and that a significant contribution to the particle momentum resolution is made by the TRT up to high particle densities.
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., Cerda Alberich, L., et al. (2017). Performance of the ATLAS trigger system in 2015. Eur. Phys. J. C, 77(5), 317–53pp.
Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, responsible for selecting events of interest at a recording rate of approximately 1 kHz from up to 40 MHz of collisions. This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton-proton collision data.
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC. Eur. Phys. J. C, 79(5), 375–54pp.
Abstract: The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.
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