Estimating event-by-event multiplicity by a Machine Learning Method for Hadronization Studies

2024/08/30 by G. Bíró, Bíró, Gábor, Gábor Papp +3
Earth and Planetary Sciences · #Cold Fusion and Nuclear Reactions #Computational Physics (physics.comp-ph) #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph)

paper · pdf · doi:10.48550/arxiv.2408.17130

openalex publication_date 2024/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 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, the prediction results of three trained ResNet networks are presented, by investigating charged particle multiplicities at event-by-event level. The widely used Lund string fragmentation model is applied as a training-baseline at √(s)= 7 TeV proton-proton collisions. We found that neural-networks with \gtrsimO(10³) parameters can predict the event-by-event charged hadron multiplicity values up to N_ch\lesssim 90 .

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