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Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
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Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
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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.
Forward citations
Cited by 1 Pith paper
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Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM
A LightGBM model trained on pion observables from one transport model predicts impact parameters in Au+Au collisions at 4 and 11 GeV with 0.2-0.4 fm error, generalizing to data from other models where polynomial fits fail.
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