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

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.

High-energy nuclear physics meets machine learning

“…Later, this strategy was deepened in a series of studies for more realistic scenarios, e.g., to take into account the afterburner hadronic cascade by incorporating UrQMD following the hydrodynamics evolution [85,86]; to consider non-equilibrium dynamics of the phase transition's influence, e.g., spinodal decomposition [18,87] or Langevin dynamics [88]; to include more realistic experimental detector effects through detector simulation with hits or tracks as the input [48,89]; to perform unsupervised outlier Fig. 9 Evolution history of QGP simulated using the relativistic hydrodynamic model CLVisc, starting from the same initial condition with four different parameter combinations.…”

Phase Transition Study Meets Machine Learning

“…Moreover, the analysis was recently extended to directly use the experimental detector readout, where the PointNet-based model was accordingly explored to identify the QCD transition type for the Compressed Baryonic Matter experiment. [50] 6. EoS Study from HICs.…”

Neural network reconstruction of the dense matter equation of state from neutron star observables

“…Due to their abilities to capture complex nonlinear correlations in data, deep learning techniques have been proven useful in solving a number of physical problems, e.g. determining the parton distribution function [39,40], reconstructing the spectral function [41][42][43], identifying phase transitions [44][45][46][47][48][49], assisting lattice field theory calculations [50][51][52][53], evaluating centrality distributions for heavy ion collisions [54][55][56], parameter estimation under detector effects [57,58], and speeding up hydrodynamic simulations [59]. Earlier works that incorporated DL methods have shown that DNNs can potentially surpass traditional methods in solving inverse design problems [60][61][62].…”