ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2024). Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data. J. Instrum., 19(2), P02009–58pp.
Abstract: This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb-1 of LHC proton -proton collision data recorded at -Js = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z -boson decays into electron -positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z -boson decays, 0.4% at ET – 10 GeV, and 0.3% at ET – 1 TeV; for photons at ET <^>' 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using .11tfr -, ee and radiative Z -boson decays.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., et al. (2019). Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data. J. Instrum., 14, P12006–69pp.
Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb(-1) of proton-proton collision data collected at root s = 13 TeV between 2015 and 2017.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fernandez Martinez, P., et al. (2015). Modelling Z -> ττ processes in ATLAS with τ-embedded Z -> μμ data. J. Instrum., 10, P09018–41pp.
Abstract: This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with Z -> tau tau decays. In Z -> μμevents selected from proton-proton collision data recorded at root s = 8 TeV with the ATLAS experiment at the LHC in 2012, the Z decay muons are replaced by tau leptons from simulated Z -> tau tau decays at the level of reconstructed tracks and calorimeter cells. The tau lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the Z boson and tau leptons as well as the detector response to the tau decay products are obtained from simulation. All other aspects of the event, such as the Z boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called tau-embedding method is particularly relevant for Higgs boson searches and analyses in tau tau final states, where Z -> tau tau decays constitute a large irreducible background that cannot be obtained directly from data control samples. In this paper, the relevant concepts are discussed based on the implementation used in the ATLAS Standard Model H -> tau tau analysis of the full datataset recorded during 2011 and 2012.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fernandez Martinez, P., et al. (2016). Performance of b-jet identification in the ATLAS experiment. J. Instrum., 11, P04008–126pp.
Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.
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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., Cabrera Urban, S., et al. (2023). Tools for estimating fake/non-prompt lepton backgrounds with the ATLAS detector at the LHC. J. Instrum., 18(11), T11004–61pp.
Abstract: Measurements and searches performed with the ATLAS detector at the CERN LHC often involve signatures with one or more prompt leptons. Such analyses are subject to 'fake/non-prompt' lepton backgrounds, where either a hadron or a lepton from a hadron decay or an electron from a photon conversion satisfies the prompt-lepton selection criteria. These backgrounds often arise within a hadronic jet because of particle decays in the showering process, particle misidentification or particle interactions with the detector material. As it is challenging to model these processes with high accuracy in simulation, their estimation typically uses data-driven methods. Three methods for carrying out this estimation are described, along with their implementation in ATLAS and their performance.
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