Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

TL;DR

This paper demonstrates that deep convolutional neural networks can effectively identify the nature of the QCD transition from heavy-ion collision data, achieving high accuracy with different spectral inputs, and highlights the potential of deep learning in analyzing complex nuclear physics phenomena.

Contribution

The study introduces a CNN-based method to classify the QCD transition type from simulated pion spectra, showing high prediction accuracy and systematic analysis of input data scenarios.

Findings

Prediction accuracy reaches up to 99% with event-fine-averaged spectra.

Deep neural networks can distinguish QCD transition types using spectral features.

High-level spectral features are effective in near-experimental scenarios.

Abstract

Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade "after-burner". Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.