Number of the records: 1  

The Mercedes water Cherenkov detector

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    SYSNO ASEP0568439
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleThe Mercedes water Cherenkov detector
    Author(s) Assis, P. (PT)
    Bakalová, Alena (FZU-D) ORCID
    de Almeida, U.B. (BR)
    Brogueira, P. (PT)
    Conceição, R. (PT)
    De Angelis, A. (IT)
    Gibilisco, L. (PT)
    González, B.S. (PT)
    Guillén, A. (ES)
    La Mura, G. (PT)
    Mendes, L.M.D. (PT)
    Mendes, L.F. (PT)
    Pimenta, M. (PT)
    Shellard, R.C. (BR)
    Tomé, B. (PT)
    Vícha, Jakub (FZU-D) RID, ORCID
    Number of authors16
    Article number899
    Source TitleEuropean Physical Journal C. - : Springer - ISSN 1434-6044
    Roč. 82, č. 10 (2022)
    Number of pages8 s.
    Languageeng - English
    CountryDE - Germany
    Keywordssingle-layer water Cherenkov detector ; Extensive Air Shower
    Subject RIVBF - Elementary Particles and High Energy Physics
    OECD categoryParticles and field physics
    R&D ProjectsLTT20002 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    Method of publishingOpen access
    Institutional supportFZU-D - RVO:68378271
    UT WOS000866156500004
    EID SCOPUS85139644007
    DOI10.1140/epjc/s10052-022-10857-1
    AnnotationThe concept of a small, single-layer water Cherenkov detector, with three photomultiplier tubes (PMTs), placed at its bottom in a 120 degrees star configuration (Mercedes Water Cherenkov Detector) is presented. The PMTs are placed near the lateral walls of the stations with an adjustable inclination and may be installed inside or outside the water volume. To illustrate the technical viability of this concept and obtain a first-order estimation of its cost, an engineering designwas elaborated. The sensitivity of these stations to low energy Extensive Air Shower ( EAS) electrons, photons and muons is discussed, both in compact and sparse array configurations. It is shown that the analysis of the intensity and time patterns of the PMT signals, using machine learning techniques, enables the tagging of muons, achieving an excellent gamma/hadron discrimination for TeV showers.
    WorkplaceInstitute of Physics
    ContactKristina Potocká, potocka@fzu.cz, Tel.: 220 318 579
    Year of Publishing2023
    Electronic addresshttps://hdl.handle.net/11104/0339754
Number of the records: 1  

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