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

Abstract

$\ensuremath{\alpha}$-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial nonclustered and clustered configurations, namely Woods-Saxon distribution and three-$\ensuremath{\alpha}$ triangular (four-$\ensuremath{\alpha}$ tetrahedral) structure for $^{12}\mathrm{C}$ $(^{16}\mathrm{O})$, from heavy-ion collision events generated within a multiphase transport (AMPT) model. Azimuthal angle and transverse momentum distributions of charged pions are taken as inputs to train the classifier. On a multiple-event basis, the overall classification accuracy can reach $95%$ for $^{12}\mathrm{C}/^{16}\mathrm{O}+^{197}\mathrm{Au}$ events at $\sqrt{{S}_{NN}}=200\phantom{\rule{4pt}{0ex}}\mathrm{GeV}$. 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.