Speaker
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 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 presentetion, the prediction results of three trained ResNet networks are presented, by investigating charged particle multiplicities at event-by-event level [1]. The widely used Lund string fragmentation model is applied as a training-baseline at $\sqrt{s}=7$ TeV proton-proton collisions. We found that neural-networks with $≳O(10^3)$ parameters can predict the event-by-event charged hadron multiplicity values up to $N_{ch}≲90$. We also present results on how ML methods can preserve the KNO-scaling [2,3] of PYTHIA8 and HIJING++ generated events.
[1] Int.J.Mod.Phys.A 40 (2025) 21, 2542011
[2] J.Phys.Conf.Ser. 3206 (2026) 1, 012129
[3] PoS ICHEP2022 (2022) 1188