Identifying the nature of the QCD transition in heavy-ion collisions with deep learning

Abstract

In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade "after-burner". As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.

arXiv comment4 pages, 4 figures, Quark Matter 2019, will be published in Nucl. Phys. A

References in corpus (10)

• Microscopic Models for Ultrarelativistic Heavy Ion Collisions
• Relativistic Hadron-Hadron Collisions in the Ultra-Relativistic Quantum Molecular Dynamics Model (UrQMD)
• QCD Equation of State and Hadron Resonance Gas
• Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
• Color Glass Condensate at the LHC: hadron multiplicities in $pp$, $pA$ and $AA$ collisions
• Hydrodynamical Description of 200 A GeV/c S+Au Collisions: Hadron and Electromagnetic Spectra
• GLISSANDO: GLauber Initial-State Simulation AND mOre
• Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions
• The onset of classical QCD dynamics in relativistic heavy ion collisions
• The PHOBOS Glauber Monte Carlo

Cited by in corpus (3)

• Exploring QCD matter in extreme conditions with Machine Learning
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Shared Data and Algorithms for Deep Learning in Fundamental Physics