Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
This work addresses the challenge of analyzing heavy-ion collision data for physicists, but it is incremental as it applies an existing deep learning method to a new dataset in this domain.
The researchers tackled the problem of classifying the equation of state in relativistic heavy-ion collisions by applying convolutional neural networks to event data, achieving an overall accuracy of 98% for Au+Au events at a specific energy.
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.