- Tytuł:
- Track finding with Deep Neural Networks
- Autorzy:
-
Kucharczyk, Marcin
Wolter, Marcin - Powiązania:
- https://bibliotekanauki.pl/articles/305791.pdf
- Data publikacji:
- 2019
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
deep neural networks
machine learning
tracking
HEP - Opis:
- 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.
- Źródło:
-
Computer Science; 2019, 20 (4); 475-491
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł