Isolabella, Tommaso (2019) A machine learning approach to particle physics data analysis: the process J/psi -> g p pbar. Master's Thesis / Essay, Physics.
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Abstract
The feasibility of machine learning methods such as boosted decision trees and artificial neural networks is studied in the context of high-level data analysis in particle physics. In particular, the process J/psi -> gamma p pbar, as produced at the BESIII experiment, is considered. A good performance is found for both algorithms.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Supervisor name: | Messchendorp, J.G. |
| Degree programme: | Physics |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 11 Jul 2019 |
| Last Modified: | 12 Jul 2019 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/20112 |
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