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Aiola, S., Amhis, Y., Billoir, P., Jashal, B. K., Henry, L., Oyanguren, A., et al. (2021). Hybrid seeding: A standalone track reconstruction algorithm for scintillating fibre tracker at LHCb. Comput. Phys. Commun., 260, 107713–5pp.
Abstract: We describe the Hybrid seeding, a stand-alone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by exploiting the knowledge of the LHCb magnetic field and the position of energy deposits in the scintillating fibre tracker detector. Moreover, we achieve a low fake rate and a small contribution to the overall timing budget of the LHCb real-time data processing.
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ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., Tönnis, C., et al. (2020). Model-independent search for neutrino sources with the ANTARES neutrino telescope. Astropart Phys., 114, 35–47.
Abstract: A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the sky. Techniques originating from the domains of machine learning, pattern recognition and image processing are used to purify the sample of neutrino candidates and for the analysis of the obtained skymap. In contrast to a dedicated search for a specific neutrino emission model, this approach is sensitive to a wide range of possible morphologies of potential sources of high-energy neutrino emission. The application of these methods to ANTARES data yields a large-scale excess with a post-trial significance of 2.5 sigma. Applied to public data from IceCube in its IC40 configuration, an excess consistent with the results from ANTARES is observed with a post-trial significance of 2.1 sigma.
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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). Electron and photon energy calibration with the ATLAS detector using 2015-2016 LHC proton-proton collision data. J. Instrum., 14, P03017–60pp.
Abstract: This paper presents the electron and photon energy calibration obtained with the ATLAS detector using about 36 fb(-1) of LHC proton-proton collision data recorded at root s = 13 TeV in 2015 and 2016. The different calibration steps applied to the data and the optimization of the reconstruction of electron and photon energies are discussed. The absolute energy scale is set using a large sample of Z boson decays into electron-positron pairs. The systematic uncertainty in the energy scale calibration varies between 0.03% to 0.2% in most of the detector acceptance for electrons with transverse momentum close to 45 GeV. For electrons with transverse momentum of 10 GeV the typical uncertainty is 0.3% to 0.8% and it varies between 0.25% and 1% for photons with transverse momentum around 60 GeV. Validations of the energy calibration with J/psi -> e(+)e(-) decays and radiative Z boson decays are also presented.
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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., 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), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo Gimenez, V., et al. (2021). The ATLAS Fast TracKer system. J. Instrum., 16(7), P07006–61pp.
Abstract: The ATLAS Fast TracKer (FTK) was designed to provide full tracking for the ATLAS high-level trigger by using pattern recognition based on Associative Memory (AM) chips and fitting in high-speed field programmable gate arrays. The tracks found by the FTK are based on inputs from all modules of the pixel and silicon microstrip trackers. The as-built FTK system and components are described, as is the online software used to control them while running in the ATLAS data acquisition system. Also described is the simulation of the FTK hardware and the optimization of the AM pattern banks. An optimization for long-lived particles with large impact parameter values is included. A test of the FTK system with the data playback facility that allowed the FTK to be commissioned during the shutdown between Run 2 and Run 3 of the LHC is reported. The resulting tracks from part of the FTK system covering a limited eta-phi region of the detector are compared with the output from the FTK simulation. It is shown that FTK performance is in good agreement with the simulation.
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ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fassi, F., Ferrer, A., et al. (2013). Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run. J. Instrum., 8, P07004–72pp.
Abstract: This paper presents a summary of beam-induced backgrounds observed in the ATLAS detector and discusses methods to tag and remove background contaminated events in data. Trigger-rate based monitoring of beam-related backgrounds is presented. The correlations of backgrounds with machine conditions, such as residual pressure in the beam-pipe, are discussed. Results from dedicated beam-background simulations are shown, and their qualitative agreement with data is evaluated. Data taken during the passage of unpaired, i.e. non-colliding, proton bunches is used to obtain background-enriched data samples. These are used to identify characteristic features of beam-induced backgrounds, which then are exploited to develop dedicated background tagging tools. These tools, based on observables in the Pixel detector, the muon spectrometer and the calorimeters, are described in detail and their efficiencies are evaluated. Finally an example of an application of these techniques to a monojet analysis is given, which demonstrates the importance of such event cleaning techniques for some new physics searches.
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ATLAS Collaboration(Abat, E. et al), Bernabeu Verdu, J., Castillo Gimenez, V., Costa, M. J., Escobar, C., Ferrer, A., et al. (2011). A layer correlation technique for pion energy calibration at the 2004 ATLAS Combined Beam Test. J. Instrum., 6, P06001–35pp.
Abstract: A new method for calibrating the hadron response of a segmented calorimeter is developed and successfully applied to beam test data. It is based on a principal component analysis of energy deposits in the calorimeter layers, exploiting longitudinal shower development information to improve the measured energy resolution. Corrections for invisible hadronic energy and energy lost in dead material in front of and between the calorimeters of the ATLAS experiment were calculated with simulated Geant4 Monte Carlo events and used to reconstruct the energy of pions impinging on the calorimeters during the 2004 Barrel Combined Beam Test at the CERN H8 area. For pion beams with energies between 20 GeV and 180 GeV, the particle energy is reconstructed within 3% and the energy resolution is improved by between 11% and 25% compared to the resolution at the electromagnetic scale.
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Bouhova-Thacker, E., Kostyukhin, V., Koffas, T., Liebig, W., Limper, M., Piacquadio, G. N., et al. (2010). Expected Performance of Vertex Reconstruction in the ATLAS Experiment at the LHC. IEEE Trans. Nucl. Sci., 57(2), 760–767.
Abstract: In the harsh environment of the Large Hadron Collider at CERN (design luminosity of 10(34) cm(-2) s(-1)) efficient reconstruction of vertices is crucial for many physics analyses. Described in this paper is the expected performance of the vertex reconstruction used in the ATLAS experiment. The algorithms for the reconstruction of primary and secondary vertices as well as for finding photon conversions and vertex reconstruction in jets are described. The implementation of vertex algorithms which follows a very modular design based on object-oriented C++ is presented. A user-friendly concept allows event reconstruction and physics analyses to compare and optimize their choice among different vertex reconstruction strategies. The performance of implemented algorithms has been studied on a variety of Monte Carlo samples and results are presented.
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CALICE Collaboration(Lai, S. et al), & Irles, A. (2024). Software compensation for highly granular calorimeters using machine learning. J. Instrum., 19(4), P04037–28pp.
Abstract: A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
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