An equation-of-state-meter of quantum chromodynamics transition from deep learning

• Published in: Nature communications Vol. 9; no. 1; pp. 210 - 6
• Main Authors: Pang, Long-Gang, Zhou, Kai, Su, Nan, Petersen, Hannah, Stöcker, Horst, Wang, Xin-Nian
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.01.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 639/766/189; 639/766/259; 639/766/387/1129; Artificial neural networks; Big bang cosmology; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Computer simulation; Deep learning; Equations of state; Gluons; High energy astronomy; Humanities and Social Sciences; Initial conditions; Ionic collisions; multidisciplinary; Neural networks; Phase transitions; PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Quantum chromodynamics; Quantum theory; Quarks; Science; Science (multidisciplinary); Transverse momentum; Universe
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-017-02726-3

A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations. The large data generated in heavy-ion collision experiments require careful analysis to understand the physics. Here the authors use the deep-learning method to sort equation of states in QCD transition and analyze the simulated data sets mimicking the heavy-ion collision experiments.