Identifying QCD Transition Using Deep Learning

Kai Zhou,Nan Su,Horst Stoecker,Hannah Petersen,Xin-Nian Wang,Long-Gang Pang

doi:10.1051/epjconf/201817116005

Abstract

In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.

Highlights

In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Φ)
The relativistic hydrodynamic models are utilized to generate raw data of final state pion’s spectra ρ(pT, Φ) in heavy ion collisions, where different QCD transition types embedded in EoS can be applied directly
Supervised learning with the above CNN structure is performed on the targeting binary classification problem here — EOSQ (0, 1) or EOSL (1, 0)

Summary

Introduction

DL is shown to be very powerful in exploring pertinent hidden features especially for complex non-linear systems with high-level correlations beyond conventional technique’s capability. This suggests that DL could be adopted to help uncovering hidden physical information from the highly implicit heavy-ion collision experimental raw data. In a recent work [17], we give an exploratory study in directly connecting QCD bulk properties and raw data from heavy-ion collisions using state-of-the-art deep-learning techniques. Supervised learning using convolutional neural networks (CNN) is performed with labeled spectra, through which we reveal unique and exclusive encoders of the bulk EoS inside ρ(pT , Φ) In this proceeding we will review this exploratory study

Training and testing datasets

Convolutional Neural Network

Results and Conclusion