| Autor: |
Kucharczyk, Marcin, Wolter, Marcin |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Computer Science 20(4) (2019) 475-491 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.7494/csci.2019.20.4.3376 |
| Popis: |
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 a 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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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