Navegando IF - Instituto de Física por Autor "Adamowski, M."
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ItemFirst results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform(2020) Abi, Babak; Abu, Adam Abed; Acciarri, Roberto; Acero Ortega, Mario A.; Adamov, George; Adamowski, M.; Adams, David; Adrien, P.; Adinolfi , Marco; Ahmad, Zulfequar; Gomes, Ricardo AvelinoThe ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber with an active volume of 7.2 × 6.1 × 7.0 m3 . It is installed at the CERN Neutrino Platform in a specially-constructed beam that delivers charged pions, kaons, protons, muons and electrons with momenta in the range 0.3 GeV/𝑐 to 7 GeV/𝑐. Beam line instrumentation provides accurate mo mentum measurements and particle identification. The ProtoDUNE-SP detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment, and it incorporates full-size components as designed for that module. This paper describes the beam line, the time projection chamber, the photon detectors, the cosmic-ray tagger, the signal processing and particle reconstruction. It presents the first results on ProtoDUNE-SP’s performance, including noise and gain measurements, 𝑑𝐸/𝑑𝑥 calibration for muons, protons, pions and electrons, drift electron life time measurements, and photon detector noise, signal sensitivity and time resolution measurements. The measured values meet or exceed the specifications for the DUNE far detector, in several cases by large margins. ProtoDUNE-SP’s successful operation starting in 2018 and its production of large samples of high-quality data demonstrate the effectiveness of the single-phase far detector design. ItemScintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC(2022) Abud, Adam Abed; Abi, Babak; Acciarri, Roberto; Acero Ortega, Mario A.; Adames, Márcio Rostirolla; Adamov, George; Adamowski, M.; Adams, David; Adinolfi, Marco; Aduszkiewicz, Antoni; Gomes, Ricardo AvelinoDUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6×6×6 m3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties. ItemSeparation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network(2022) Abud, Adam Abed; Abi, Babak; Acciarri, Roberto; Acero Ortega, Mario A.; Adames, Márcio Rostirolla; Adamov, George; Adamowski, M.; Adams, David; Adinolfi, Marco; Aduszkiewicz, Antoni; Gomes, Ricardo AvelinoLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neu trino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article pro poses an algorithm based on a convolutional neural network to perform the classification of energy deposits and recon structed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experi mental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.