Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

Abstract

$\hat{I}\pm$-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and three-$\hat{I}\pm$ triangular (four-$\hat{I}\pm$ tetrahedral) structure for ${}^{12}$C (${}^{16}$O), from heavy-ion collision events generated within a multi-phase transport (AMPT) model. Azimuthal angle and transverse momentum distributions of charged pions are taken as inputs to train the classifier. On multiple-event basis, the overall classification accuracy can reach $95\%$ for ${}^{12}$C/${}^{16}$O + ${}^{197}$Au events at $\sqrt{S_{NN}}=$ 200 GeV. With proper constructions of samples, the predicted deviations on mixed samples with different proportions of both configurations could be within $5\%$. In addition, setting a simple confidence threshold can further improve the predictions on the mixed dataset. Our results indicate promising and extensive possibilities of application of machine-learning-based techniques to real data and some other problems in physics of heavy-ion collisions.

12 pages, 11 figures; Accepted by Physical Review C