Abstract
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.
Highlights
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 expected to form in high-energy heavy-ion collisions
Strong interaction in nuclear matter is governed by the theory of quantum chromodynamics (QCD)
Though it is believed that strongly coupled QCD matter can be formed in heavy-ion collisions at the Relativistic Heavy Ion Collider (RHIC, Brookhaven National Laboratory, USA)[18], Large Hadron Collider (LHC, European Organization for Nuclear Research, Switzerland)[19], and at the forthcoming Facility for Anti-proton and Ion Research (FAIR, GSI Helmholtz Centre for Heavy Ion Research, Germany)[20,21], a direct access to the bulk properties of the matter such as the equation of state (EoS) and transport coefficients is impossible due to the highly dynamical nature of the collisions
Summary
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 expected to form in high-energy heavy-ion collisions. The training data set of ρ(pT, φ) (labeled with EOSL or EOSQ) is generated by event-by-event hydrodynamic package CLVisc[30,31] with fluctuating AMPT initial conditions[36]. After training and validating the network, it is tested on the testing data set of ρ(pT, φ) events (see Sec. 4 for the details of our neural-network model).
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