An equation-of-state-meter for CBM using PointNet

TL;DR

This paper introduces a PointNet-based deep learning approach to classify the equation of state in heavy-ion collisions, achieving high accuracy in distinguishing phase transitions even with realistic experimental effects.

Contribution

The study presents a novel application of PointNet deep learning models for EoS classification in heavy-ion collision data, demonstrating superior performance over traditional methods.

Findings

Models achieve up to 99.8% accuracy in classifying phase transitions.

Performance improves with more central collisions and multiple event analysis.

Deep learning outperforms conventional mean observable methods.

Abstract

A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0-7 fm). However, the performance is found to improve for central collisions (b=0-3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.