Book ; Online: Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
2020
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 ... ...
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 $\sqrt{s_{NN}} = 11$ 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. Comment: LATEX, 14 pages, 8 figures |
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Keywords | Nuclear Theory ; Computer Science - Machine Learning ; High Energy Physics - Phenomenology ; Nuclear Experiment |
Publishing date | 2020-04-29 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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