Publikationen

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    Suchergebnis: 7907 Publikationen passen auf ihre Suche. Die Liste fängt mit den jüngsten Publikationen an: (191 - 200)


    MPP-2023-326 Techniques to seed the self-modulation instability of a long proton bunch in plasma, L. Verra, G. Zevi Della Porta, E. Gschwendtner, M. Bergamaschi, P. Muggli, arxiv:2305.00431 (abs), (pdf), (ps), Eintrag bei inSPIRE.
    [AWAKE], [Conference-Paper]

    MPP-2023-325 On goodness-of-fit tests for arbitrary multivariate models, Lolian Shtembari, Allen Caldwell, arxiv:2211.03478 (abs), (pdf), (ps), Eintrag bei inSPIRE.
    [Statistical Methods], [Article]

    MPP-2023-324 Limit setting using spacings in the presence of unknown backgrounds, Lolian Shtembari, Allen Caldwell, arxiv:2303.09520 (abs), (pdf), (ps), Eintrag bei inSPIRE.
    [Statistical Methods], [Article]

    MPP-2023-323 Production and Testing of Prototype Resistive Plate Chambers, Timur Turkovic, (Volltext), TUM, München (2023-09-18).
    [ATLAS], [Thesis]

    MPP-2023-322 Search For Charged Higgs Bosons In H± → W ±H → l±νbb Decays At The Large Hadron Collider, Elias Hanser, (Volltext), TUM, München (2023).
    [ATLAS], [Bachelor-Thesis]

    MPP-2023-321 Study of the Higgs boson reconstruction in the 4-lepton decay channel for early Run 3 ATLAS data taking, Sarah Mavie Metz, (Volltext), TUM, München (2023).
    [ATLAS], [Bachelor-Thesis]

    MPP-2023-320 Optimization of the Search for Charged Higgs Bosons with the ATLAS detector at the LHC, Joseph Rakich, (Volltext), TUM, München (2023).
    [ATLAS], [Bachelor-Thesis]

    MPP-2023-319 Study of the Influence of Signal Pile-up on the Spatial Resolution of Muon Drift-Tube Chambers, Dilan Pocuc, (Volltext), TUM, München (2023).
    [ATLAS], [Bachelor-Thesis]

    MPP-2023-318 iDMEu: An initiative for Dark Matter in Europe and beyond, Marco Cirelli, Caterina Doglioni, Federica Petricca, arxiv:2312.14192 (abs), (pdf), (ps), Eintrag bei inSPIRE.
    [Astroparticle Physics], [Conference-Paper]

    MPP-2023-317 Optimal operation of cryogenic calorimeters through deep reinforcement learning, G. Angloher, S. Banik, G. Benato, A. Bento, et al., arxiv:2311.15147 (abs), (pdf), (ps), Eintrag bei inSPIRE.
    [CRESST], [Article]