Jul 6 – 13, 2022
Bologna, Italy
Europe/Rome timezone

Studying Hadronization by Machine Learning Techniques

Jul 8, 2022, 7:05 PM
1h 25m
Bologna, Italy

Bologna, Italy

Palazzo della Cultura e dei Congressi
Poster Detectors for Future Facilities, R&D, novel techniques Poster Session


Gabor Biro (Wigner RCP)


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 latest 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.

[1] G. Bíró, B. Tankó-Bartalis, G.G. Barnaföldi; arXiv:2111.15655

In-person participation Yes

Primary author

Gabor Biro (Wigner RCP)


Bence Tanko-Bartalis (Wigner RCP, Oxford University) Gergely Gabor Barnafoldi (Wigner RCP)

Presentation materials