Track finding with Deep Neural Networks

• Autorzy: Kucharczyk Marcin , Wolter Marcin

Identyfikatory

DOI

10.7494/csci.2019.20.4.3376

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

deep neural network; machine learning; tracking; HEP

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2019
• Tom: Vol. 20 (4)
• Strony: 475--491
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Kucharczyk Marcin

• Institute of Nuclear Physics PAN, Krakow, Poland

autor

Wolter Marcin

• Institute of Nuclear Physics PAN, Krakow, Poland

Bibliografia

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