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A machine learning approach to particle physics data analysis: the process J/psi -> g p pbar

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)
Supervisor:
Supervisor nameSupervisor E mail
Messchendorp, J.G.J.G.Messchendorp@rug.nl
Degree programme: Physics
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 11 Jul 2019
Last Modified: 12 Jul 2019 07:29
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/20112

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