| Autor: |
Saúl Alonso-Monsalve, Davide Sgalaberna, Xingyu Zhao, Clark McGrew, André Rubbia |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Communications Physics, Vol 6, Iss 1, Pp 1-12 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2399-3650 |
| DOI: |
10.1038/s42005-023-01239-4 |
| Popis: |
Abstract Particle track fitting is crucial for understanding particle kinematics. In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the design of future particle physics experiments and their data exploitation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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