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

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. trations of the usage of iEBE-VISHNU package and Volodymyr Vovchenko for helpful discussions. This work is supported by the Helmholtz Graduate School HIRe for FAIR (Y. D. and A. M.) , by the F&E Programme of GSI Helmholtz Zentrum für Schwerionenforschung GmbH, Darmstadt (Y. D.), by the Giersch Science Center (Y. D.), by the Walter Greiner Gesellschaft zur Förderung der physikalischen Grundlagenforschung e.V., Frankfurt (Y. D.), by the AI grant of SAMSON AG,

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

“…DL methods are reliable and accurate in identifying QCD transitions in heavy-ion collisions. However, as reported in [20], the performance depend largely on the fluctuation in the final state spectra. Therefore, if such a DL based EoS-meter is to be used on the direct output of a heavy ion experiment, an extensive analysis on the response of the DL model on additional uncertainties introduced by e.g.…”

“…The study performed on the hydrodynamic output showed an average prediction accuracy greater than 95%. A follow up study was presented in [20] where a hadronic cascade model was employed after hydrodynamic evolution in the simulations to achieve a realistic freeze-out as well as including the effect of having a finite number of measurable particles in single events. The hadronic cascade "after-burner" introduces uncertainties in the final state spectra due to resonance decays and hadron rescatterings.…”

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“…It is worth mentioning that the performance of trained CNN routine is very robust against initial state fluctuations and final state interactions, such as decays of resonances. However, similar to other applications of DL technique to high energy physics [22][23][24], the program has to decide between two discrete choices: calculations either with soft or stiff equation of state. In other words, this is a one-dimensional discrete space.…”

“…Review of possibilities of application of ML and DL methods to high energy physics can be found elsewhere [19][20][21]. Prominent results of application of DL to problems of high energy nuclear physics and heavy-ion collisions are described in [22][23][24].…”

Generative Model Study for 1+1d-Complex Scalar Field Theory

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