Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
Nuclear Theory
2020-07-23 v2 Machine Learning
High Energy Physics - Phenomenology
Nuclear Experiment
Abstract
Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle distributions of protons are used to train a classifier. An overall accuracy of classification of 98\% is reached for Au+Au events at GeV. Performance of classifiers, trained on events at different colliding energies, is investigated. Obtained results indicate extensive possibilities of application of Deep Learning methods to other problems in physics of heavy-ion collisions.
Keywords
Cite
@article{arxiv.2004.14409,
title = {Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning},
author = {Yu. Kvasiuk and E. Zabrodin and L. Bravina and I. Didur and M. Frolov},
journal= {arXiv preprint arXiv:2004.14409},
year = {2020}
}
Comments
matches published version