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Miñano, M. (2011). Radiation Hard Silicon Strips Detectors for the SLHC. IEEE Trans. Nucl. Sci., 58(3), 1135–1140.
Abstract: While the Large Hadron Collider (LHC) began taking data in 2009, scenarios for a machine upgrade to achieve a much higher luminosity are being developed. In the current planning, it is foreseen to increase the luminosity of the LHC at CERN around 2018. As radiation damage scales with integrated luminosity, the particle physics experiments will need to be equipped with a new generation of radiation hard detectors. This article reports on the status of the R&D projects on radiation hard silicon strips detectors for particle physics, linked to the Large Hadron Collider Upgrade, super-LHC (sLHC) of the ATLAS microstrip detector. The primary focus of this report is on measuring the radiation hardness of the silicon materials and the detectors under study. This involves designing silicon detectors, irradiating them to the sLHC radiation levels and studying their performance as particle detectors. The most promising silicon detector for the different radiation levels in the different regions of the ATLAS microstrip detector will be presented. Important challenges related to engineering layout, powering, cooling and reading out a very large strip detector are presented. Ideas on possible schemes for the layout and support mechanics will be shown.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at root s=13 TeV. Phys. Rev. D, 100(5), 052011–38pp.
Abstract: This paper presents a measurement of quantities related to the formation of jets from high-energy quarks and gluons (fragmentation). Jets with transverse momentum 100 GeV < p(T) < 2.5 TeV and pseudorapidity vertical bar eta vertical bar < 2.1 from an integrated luminosity of 33 fb(-1) of root s = 13 TeV proton-proton collisions are reconstructed with the ATLAS detector at the Large Hadron Collider. Charged-particle tracks with p(T) > 500 MeV and vertical bar eta vertical bar < 2.5 are used to probe the detailed structure of the jet. The fragmentation properties of the more forward and the more central of the two leading jets from each event are studied. The data are unfolded to correct for detector resolution and acceptance effects. Comparisons with parton shower Monte Carlo generators indicate that existing models provide a reasonable description of the data across a wide range of phase space, but there are also significant differences. Furthermore, the data are interpreted in the context of quark- and gluon-initiated jets by exploiting the rapidity dependence of the jet flavor fraction. A first measurement of the charged-particle multiplicity using model-independent jet labels (topic modeling) provides a promising alternative to traditional quark and gluon extractions using input from simulation. The simulations provide a reasonable description of the quark-like data across the jet p(T) range presented in-this measurement, but the gluon-like data have systematically fewer charged particles than the simulation.
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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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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2020). Performance of the upgraded PreProcessor of the ATLAS Level-1 Calorimeter Trigger. J. Instrum., 15(11), P11016–48pp.
Abstract: The PreProcessor of the ATLAS Level-1 Calorimeter Trigger prepares the analogue trigger signals sent from the ATLAS calorimeters by digitising, synchronising, and calibrating them to reconstruct transverse energy deposits, which are then used in further processing to identify event features. During the first long shutdown of the LHC from 2013 to 2014, the central components of the PreProcessor, the Multichip Modules, were replaced by upgraded versions that feature modern ADC and FPGA technology to ensure optimal performance in the high pile-up environment of LHC Run 2. This paper describes the features of the new Multichip Modules along with the improvements to the signal processing achieved.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2020). Performance of the missing transverse momentum triggers for the ATLAS detector during Run-2 data taking. J. High Energy Phys., 08(8), 080–53pp.
Abstract: The factor of four increase in the LHC luminosity, from 0.5x10(34)cm(-2)s(-1) to 2.0x10(34)cm(-2)s(-1), and the corresponding increase in pile-up collisions during the 2015-2018 data-taking period, presented a challenge for the ATLAS trigger, particularly for those algorithms that select events with missing transverse momentum. The output data rate at fixed threshold typically increases exponentially with the number of pile-up collisions, so the legacy algorithms from previous LHC data-taking periods had to be tuned and new approaches developed to maintain the high trigger efficiency achieved in earlier operations. A study of the trigger performance and comparisons with simulations show that these changes resulted in event selection efficiencies of >98% for this period, meeting and in some cases exceeding the performance of similar triggers in earlier run periods, while at the same time keeping the necessary bandwidth within acceptable limits.
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