Machine Learning based KNO-scaling of charged hadron multiplicities with Hijing++
Abstract
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.
Fields of Study & Subjects
Particle physics theoretical and experimental studies
High-Energy Particle Collisions Research
Computational Physics and Python Applications
Publication details
- Journal / source
- Journal of Physics Conference Series
- Publisher
- IOP Publishing
- Volume
- 3206
- Issue
- 1
- Pages
- 012129–012129
- Publication date
- 1 April 2026
- Language
- EN