Repository logo

Browsing by Author "Kucharczyk, Marcin"

Filter results by typing the first few letters
Now showing 1 - 4 of 4
  • thumbnail.journal.alt
    Item type:Thesis, Access status: Restricted ,
    Analiza procesu wytwarzania cienkich warstw tlenkowych z użyciem impulsowej wiązki elektronów - PED
    (Data obrony: 2015-01-30) Kucharczyk, Marcin
    Wydział Inżynierii Metali i Informatyki Przemysłowej
  • thumbnail.journal.alt
    Item type:Thesis, Access status: Restricted ,
    Badanie korelacji Fermiego-Diraca dla par protonów w eksperymencie LHCb
    (Data obrony: 2015-02-04) Małecki, Bartosz
    Wydział Fizyki i Informatyki Stosowanej
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Machine learning based event reconstruction for the MUonE experiment
    (Wydawnictwa AGH, 2024) Zdybał, Miłosz; Kucharczyk, Marcin; Wolter, Marcin
    A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Track finding with Deep Neural Networks
    (Wydawnictwa AGH, 2019) Kucharczyk, Marcin; Wolter, Marcin
    High energy physics experiments require fast and efficient methods for reconstructing the tracks of charged particles. The commonly used algorithms are sequential and the required CPU power increases rapidly with the number of tracks. Neural networks can speed up the process due to their capability of modeling complex non-linear data dependencies and finding all tracks in parallel. In this paper, we describe the application of the deep neural network for reconstructing straight tracks in a toy two-dimensional model. It is planned to apply this method to the experimental data obtained by the MUonE experiment at CERN.