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arXiv · 2004.14409

Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning

Yu. Kvasiuk·E. Zabrodin·L. Bravina·I. Didur·M. Frolov
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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.

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Yu. Kvasiuk, E. Zabrodin, L. Bravina, I. Didur, M. Frolov. 2020-04-29. Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning. https://doi.org/10.1007/jhep07(2020)133

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