13–17 Jun 2022
Paradise Hotel Busan
Asia/Seoul timezone

Studying Hadronization by Machine Learning Techniques

POS-OTH-06
14 Jun 2022, 17:10
1h 50m
Metaverse

Metaverse

Board: OTH-06
Poster Other topics Poster

Speakers

Gergely Barnafoldi (Hungarian Academy of Sciences (HU)) Gergely Gabor Barnafoldi (Wigner Research Centre for Physics (Wigner RCP) (HU))

Description

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.

Here, I would like to present the results of two deep neural networks, 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 √s=7 TeV proton-proton collisions to predict the most relevant observables at further LHC energies. Non-liear QCD scaling properties were also identified and validated by experimental data.

Present via Online

Authors

Bartalis Bence Tanko (Wigner Research Centre for Physics) Gabor Biro (Wigner Research Centre for Physics (Wigner RCP) (HU)) Gergely Barnafoldi (Hungarian Academy of Sciences (HU))

Presentation materials