Machine Learning based KNO-scaling of charged hadron multiplicities with Hijing++

2023/03/09 by Bíró, Gábor, Barnaföldi, Gergely Gábor
#FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph)

paper · doi:10.48550/arxiv.2303.05422

Abstract

The scaling properties of the final state charged hadron and mean jet multiplicity distributions, calculated by deep residual neural network architectures with different complexities are presented. The parton-level input of the neural networks are generated by the Hijing++ Monte Carlo event generator. Hadronization neural networks, trained with √(s)=7 TeV events are utilized to perform predictions for various LHC energies from √(s)=0.9 TeV to 13 TeV. KNO-scaling properties were adopted by the networks at hadronic level.

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