An equation-of-state-meter of QCD transition from deep learning
High Energy Physics - Phenomenology
2017-08-03 v3 Machine Learning
High Energy Physics - Theory
Nuclear Theory
Machine Learning
Abstract
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra . High-level correlations of learned by the neural network act as an effective "EoS-meter" in detecting the nature of the QCD transition. The EoS-meter is model independent and insensitive to other simulation inputs, especially the initial conditions. Thus it provides a powerful direct-connection of heavy-ion collision observables with the bulk properties of QCD.
Keywords
Cite
@article{arxiv.1612.04262,
title = {An equation-of-state-meter of QCD transition from deep learning},
author = {Long-Gang Pang and Kai Zhou and Nan Su and Hannah Petersen and Horst Stöcker and Xin-Nian Wang},
journal= {arXiv preprint arXiv:1612.04262},
year = {2017}
}