Studying Hadronization by Machine Learning Techniques
High Energy Physics - Phenomenology
2022-01-11 v2 Machine Learning
Abstract
Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, results of two ResNet networks are presented by investigating global and kinematical quantities, indeed jet- and event-shape variables. The widely used Lund string fragmentation model is applied as a baseline in TeV proton-proton collisions to predict the most relevant observables at further LHC energies.
Cite
@article{arxiv.2111.15655,
title = {Studying Hadronization by Machine Learning Techniques},
author = {Gábor Bíró and Bence Tankó-Bartalis and Gergely Gábor Barnaföldi},
journal= {arXiv preprint arXiv:2111.15655},
year = {2022}
}