3 papers
hep-ph2023
Machine Learning based KNO-scaling of charged hadron multiplicities with Hijing++
Gábor Bíró, Gergely Gábor Barnaföldi
The scaling properties of the final state charged hadron and mean jet multiplicity distributions, calculated by deep residual neural network architectures with different complexiti…
cs.DC2023
Dedicated Analysis Facility for HEP Experiments
Gábor Bíró, Gergely Gábor Barnaföldi, Péter Lévai
High-energy physics (HEP) provides ever-growing amount of data. To analyse these, continuously-evolving computational power is required in parallel by extending the storage capacit…
hep-ph2022
Testing of KNO-scaling of charged hadron multiplicities within a Machine Learning based approach
Gábor Bíró, Bence Tankó-Bartalis, Gergely Gábor Barnaföldi
The results of a Machine Learning-based method is presented here to investigate the scaling properties of the final state charged hadron and mean jet multiplicity distributions. De…