An equation-of-state-meter of quantum chromodynamics transition from deep learning
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-en...
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| Published in | Nature communications Vol. 9; no. 1; pp. 210 - 6 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
15.01.2018
Nature Publishing Group Nature Portfolio |
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| Online Access | Get full text |
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| 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.
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. |
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| AbstractList | 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. Finally, such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations. 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. 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. 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. 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. |
| ArticleNumber | 210 |
| Author | Su, Nan Wang, Xin-Nian Stöcker, Horst Pang, Long-Gang Zhou, Kai Petersen, Hannah |
| Author_xml | – sequence: 1 givenname: Long-Gang orcidid: 0000-0002-1279-7008 surname: Pang fullname: Pang, Long-Gang email: lgpang.1984@berkeley.edu organization: Frankfurt Institute for Advanced Studies, Department of Physics, University of California, Nuclear Science Division, Lawrence Berkeley National Laboratory – sequence: 2 givenname: Kai orcidid: 0000-0001-9859-1758 surname: Zhou fullname: Zhou, Kai email: zhou@fias.uni-frankfurt.de organization: Frankfurt Institute for Advanced Studies, Institut für Theoretische Physik, Goethe Universität – sequence: 3 givenname: Nan surname: Su fullname: Su, Nan email: nansu@fias.uni-frankfurt.de organization: Frankfurt Institute for Advanced Studies – sequence: 4 givenname: Hannah orcidid: 0000-0002-6213-3613 surname: Petersen fullname: Petersen, Hannah organization: Frankfurt Institute for Advanced Studies, Institut für Theoretische Physik, Goethe Universität, GSI Helmholtzzentrum für Schwerionenforschung – sequence: 5 givenname: Horst surname: Stöcker fullname: Stöcker, Horst organization: Frankfurt Institute for Advanced Studies, Institut für Theoretische Physik, Goethe Universität, GSI Helmholtzzentrum für Schwerionenforschung – sequence: 6 givenname: Xin-Nian orcidid: 0000-0002-9734-9967 surname: Wang fullname: Wang, Xin-Nian organization: Nuclear Science Division, Lawrence Berkeley National Laboratory, Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29335457$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1433129$$D View this record in Osti.gov |
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| Snippet | 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... 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... The large data generated in heavy-ion collision experiments require careful analysis to understand the physics. Here the authors use the deep-learning method... |
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| SubjectTerms | 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 |
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| Title | An equation-of-state-meter of quantum chromodynamics transition from deep learning |
| URI | https://link.springer.com/article/10.1038/s41467-017-02726-3 https://www.ncbi.nlm.nih.gov/pubmed/29335457 https://www.proquest.com/docview/1987709435 https://search.proquest.com/docview/1989590560 https://www.osti.gov/servlets/purl/1433129 https://pubmed.ncbi.nlm.nih.gov/PMC5768690 https://doaj.org/article/1ca7b75f4eb446d4a968181d0b0fb0a1 |
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