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.
Highlights
Recent success of methods of Artificial Intelligence (AI), such as Machine Learning (ML) and Deep Learning (DL), in approximating highly obscure dependencies gives us a justifiable hope that they can benefit in heavy-ion physics objectives
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 ultrarelativistic quantum molecular dynamics (UrQMD) model
√ 14 GeV and 94% for s = 7 GeV, using the data set of primitive experimental observables, such as particle transverse momentum and azimuthal angle
Summary
The Ultra-relativistic Quantum Molecular Dynamics model [25, 26] is a Monte-Carlo event generator designed for the description of hh, hA, and A + A collisions in a broad energy range from hundred MeV up to several TeV. At energies below few GeV, the interaction dynamics of hh or A + A collisions can be described via interactions between. At higher energies new processes of multiparticle production come into play. The UrQMD treats the production of new particles via formation and fragmentation of specific colored objects, strings. Strings are uniformly stretched between the quarks, diquarks and their antistates with constant string tension κ ≈ 1 GeV/fm. The excited strings are fragmenting into pieces via the Schwinger mechanism of qq-pair production, and the distribution of newly produced hadrons is uniform in the rapidity space. The model utilizes Hamiltonian dynamics of particle motions and incorporates particle interaction via geometric cross sections, taken from available experimental data or from quark models
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