Application of Gene Expression Programming in Improving the Event Selection of the Semi-leptonic Top Quark Pair Process

2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2021): Proceedings 2021
Andris Potrebko, Inese Poļaka

Searches for Beyond the Standard Model physics require probing the Standard Model with increased precision. One way this can be achieved is by improving the accuracy of the event selection classifiers. Recently, Gene Expression Programming (GEP) has been shown to provide complex yet easy to interpret classifiers in various fields. Previous attempts to apply GEP to high-energy physics (HEP), though limited by computational power available, achieved classifier accuracy of up to 95%. In this paper, we demonstrate that a selection algorithm optimized by GEP and applied to the top-quark pair production process’ semi-leptonic decay channel enables the increase of data purity for already highly pure data. Moreover, we explain how adding penalty cuts to the purity fitness function allows adjusting the optimized classifier to the needs of a specific measurement in terms of the size of the selected event sample and data purity.

Keywords
classification, data purity, event selection, evo- lutionary programming, gene expression programming, high- energy physics
DOI
10.1109/ITMS52826.2021.9615317
Hyperlink
https://ieeexplore.ieee.org/document/9615317

Potrebko, A., Poļaka, I. Application of Gene Expression Programming in Improving the Event Selection of the Semi-leptonic Top Quark Pair Process. In: 2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2021): Proceedings, Latvia, Riga, 14-15 October, 2021. Piscataway: IEEE, 2021, pp.203-208. ISBN 978-1-6654-0616-1. e-ISBN 978-1-6654-0615-4. Available from: doi:10.1109/ITMS52826.2021.9615317

Publication language
English (en)