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...
Saved in:
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 |
Subjects | |
Online Access | Get full text |
Cover
Loading…
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. |
---|---|
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 |
BookMark | eNp1kl1rFTEQhoNUbD32D3ghi954s5psspvNjVCKH4WCIHod8jE5J4fdpE2yQv-9Od1aTgUDISHzzjOZ4X2JTkIMgNBrgj8QTMePmRE28BaTujveDS19hs46zEhLeEdPju6n6DznPa6LCjIy9gKddoLSnvX8DP24CA3cLqr4GNro2lxUgXaGAqmJrqmRUJa5MbsU52jvgpq9yU1JKmR_yGlcDTQW4KaZQKXgw_YVeu7UlOH84dygX18-_7z81l5__3p1eXHdmoHS0rKOmW4E1WsNFrTqLXEgmFUOW2Gd0MMIAlved8ZioAY05kRbahWjWGFHN-hq5dqo9vIm-VmlOxmVl_cPMW2lSsWbCSQximveOwaascEyJYaRjMRijZ3GilTWp5V1s-gZrIFQW5yeQJ9Ggt_Jbfwtez6Mg8AV8HYFxFy8zMYXMDsTQwBTJGGUkjrzDXr_UCXF2wVykbPPBqZJBYhLlkSMohe4Hw68d_9I93FJoc7zoOIcC0b7qupWlUkx5wTu8ccEy4NN5GoTWW0i720iaU16c9zrY8pfU1QBXQW5hsIW0lHt_2P_ANZGy7c |
CitedBy_id | crossref_primary_10_1051_epjconf_202225910017 crossref_primary_10_1142_S0218301322500975 crossref_primary_10_1103_PhysRevD_101_114025 crossref_primary_10_1016_j_engappai_2022_104904 crossref_primary_10_1103_PhysRevResearch_2_043202 crossref_primary_10_1088_1402_4896_abf214 crossref_primary_10_1007_JHEP03_2021_273 crossref_primary_10_1103_PhysRevD_105_114022 crossref_primary_10_1007_s41781_022_00082_6 crossref_primary_10_1016_j_ppnp_2023_104084 crossref_primary_10_1360_SSPMA_2022_0022 crossref_primary_10_1142_S0218301319500927 crossref_primary_10_1016_j_physletb_2021_136669 crossref_primary_10_1103_PhysRevC_109_024604 crossref_primary_10_1103_PhysRevC_109_044616 crossref_primary_10_1103_PhysRevC_97_064918 crossref_primary_10_1103_PhysRevLett_131_202303 crossref_primary_10_1016_j_nuclphysa_2018_10_077 crossref_primary_10_1007_s41365_021_00956_1 crossref_primary_10_1016_j_chaos_2023_113346 crossref_primary_10_1140_epjc_s10052_022_10718_x crossref_primary_10_1103_RevModPhys_93_035003 crossref_primary_10_1016_j_measurement_2022_110897 crossref_primary_10_1103_PhysRevB_102_224434 crossref_primary_10_1088_2399_6528_abd7c3 crossref_primary_10_1088_1475_7516_2024_01_009 crossref_primary_10_1103_PhysRevD_99_116004 crossref_primary_10_1103_PhysRevD_98_023019 crossref_primary_10_1016_j_physletb_2022_137508 crossref_primary_10_1103_PhysRevD_105_014017 crossref_primary_10_1007_s11433_019_9390_8 crossref_primary_10_1103_PhysRevC_104_044902 crossref_primary_10_1088_0256_307X_39_12_120502 crossref_primary_10_1016_j_nuclphysa_2018_12_020 crossref_primary_10_1016_j_nuclphysa_2020_121891 crossref_primary_10_1088_1402_4896_ad3170 crossref_primary_10_1103_PhysRevC_108_034905 crossref_primary_10_1088_1361_648X_ac2533 crossref_primary_10_1016_j_physletb_2022_137055 crossref_primary_10_1007_JHEP03_2021_206 crossref_primary_10_1103_PhysRevC_99_064307 crossref_primary_10_1140_epja_s10050_023_00949_1 crossref_primary_10_1103_PhysRevD_103_116023 crossref_primary_10_1002_asna_202113998 crossref_primary_10_1103_PhysRevC_106_L051901 crossref_primary_10_1016_j_physletb_2020_135872 crossref_primary_10_1103_PhysRevD_106_L051502 crossref_primary_10_1140_epjp_s13360_022_03597_4 crossref_primary_10_3390_e25111563 crossref_primary_10_1140_epjp_s13360_021_02121_4 crossref_primary_10_1103_PhysRevC_104_064903 crossref_primary_10_1103_PhysRevC_108_064908 crossref_primary_10_1088_1361_6471_abb1f9 crossref_primary_10_1360_SSPMA_2021_0300 crossref_primary_10_1016_j_ppnp_2023_104070 crossref_primary_10_1103_PhysRevD_100_011501 crossref_primary_10_7498_aps_72_20230334 crossref_primary_10_3390_particles4010006 crossref_primary_10_1140_epja_s10050_021_00607_4 crossref_primary_10_1016_j_nuclphysa_2020_121867 crossref_primary_10_3390_universe8090451 crossref_primary_10_1088_1674_1137_ac28f9 crossref_primary_10_1103_PhysRevC_104_034608 crossref_primary_10_1038_s42256_021_00392_1 crossref_primary_10_3390_e24020198 crossref_primary_10_1088_1674_1137_aca5f5 crossref_primary_10_1080_23746149_2020_1797528 crossref_primary_10_1103_RevModPhys_94_031003 crossref_primary_10_1103_PhysRevC_107_014310 crossref_primary_10_1103_PhysRevLett_126_180604 crossref_primary_10_1007_s41365_020_00829_z crossref_primary_10_1103_RevModPhys_91_045002 crossref_primary_10_1088_1742_6596_2586_1_012159 crossref_primary_10_1103_PhysRevC_105_034611 crossref_primary_10_1088_0256_307X_40_12_122101 crossref_primary_10_1103_PhysRevC_100_024907 crossref_primary_10_1007_JHEP12_2019_122 crossref_primary_10_1103_PhysRevC_110_014902 crossref_primary_10_1103_PhysRevC_98_034909 crossref_primary_10_1142_S0217751X20430022 crossref_primary_10_1016_j_rinp_2021_105134 crossref_primary_10_1016_j_nuclphysa_2020_121972 crossref_primary_10_1093_ptep_ptac173 crossref_primary_10_1142_S0218301324300091 crossref_primary_10_1016_j_nuclphysa_2020_121847 crossref_primary_10_1103_PhysRevResearch_3_023256 crossref_primary_10_1109_TMI_2019_2896085 crossref_primary_10_1093_pnasnexus_pgac250 crossref_primary_10_1016_j_rinp_2023_107264 crossref_primary_10_1103_PhysRevD_101_054016 crossref_primary_10_1140_epja_s10050_020_00290_x crossref_primary_10_1007_s41365_023_01345_6 crossref_primary_10_1103_PhysRevC_107_054911 crossref_primary_10_1103_PhysRevD_98_046019 crossref_primary_10_1103_PhysRevD_107_056001 crossref_primary_10_3390_galaxies10010016 crossref_primary_10_1103_PhysRevD_101_094507 crossref_primary_10_1142_S0217751X21300076 crossref_primary_10_1007_s41365_022_01140_9 crossref_primary_10_1088_1361_6471_ad0314 crossref_primary_10_1103_PhysRevD_107_083028 crossref_primary_10_1093_ptep_ptad096 crossref_primary_10_1140_epja_s10050_023_01087_4 crossref_primary_10_1007_s41365_023_01233_z crossref_primary_10_1088_0256_307X_39_11_111201 crossref_primary_10_1103_PhysRevC_106_014904 crossref_primary_10_1007_JHEP10_2022_011 crossref_primary_10_1088_1475_7516_2022_08_071 crossref_primary_10_1039_D3CP01443F crossref_primary_10_1140_epjc_s10052_020_8030_7 crossref_primary_10_1007_s11467_023_1313_3 crossref_primary_10_1002_mrm_29128 crossref_primary_10_1007_JHEP10_2021_184 crossref_primary_10_1016_j_physletb_2022_137001 crossref_primary_10_1016_j_nuclphysa_2018_11_004 crossref_primary_10_1007_JHEP07_2020_133 |
Cites_doi | 10.1016/j.neunet.2014.09.003 10.1126/science.aag2302 10.1140/epja/i2017-12248-y 10.1103/PhysRevC.72.064901 10.1103/PhysRevLett.114.111801 10.1038/ncomms5308 10.1007/JHEP12(2016)153 10.1103/PhysRevLett.114.202301 10.1088/1742-6596/50/1/030 10.1146/annurev-nucl-102711-094910 10.1142/S0218301310014613 10.1016/j.nuclphysa.2004.12.074 10.1103/PhysRevD.93.094033 10.1142/S0217751X13400113 10.1103/PhysRevD.95.014018 10.1103/PhysRevC.55.392 10.1109/TKDE.2007.190734 10.1038/nature14539 10.1007/978-3-642-13293-3_1 10.1088/0954-3899/43/11/114002 10.1088/0034-4885/74/1/014001 10.1016/j.nuclphysa.2010.02.015 10.1038/s41598-017-09098-0 10.1103/PhysRevD.91.074027 10.1016/0370-1573(86)90131-6 10.1103/PhysRevC.86.024911 10.1103/PhysRevC.94.024907 10.1038/nphys4035 10.1214/aoms/1177729694 10.5506/APhysPolB.45.2355 10.1016/j.cpc.2015.08.039 10.1103/PhysRevB.94.165134 10.1103/PhysRevLett.110.012302 10.1103/PhysRevC.78.034915 10.1016/j.nuclphysa.2005.03.085 10.6084/m9.figshare.5457220.v1 10.1109/ICCV.2015.123 |
ContentType | Journal Article |
Copyright | The Author(s) 2018 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: The Author(s) 2018 – notice: 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
CorporateAuthor | Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States) |
CorporateAuthor_xml | – name: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States) |
DBID | C6C NPM AAYXX CITATION 3V. 7QL 7QP 7QR 7SN 7SS 7ST 7T5 7T7 7TM 7TO 7X7 7XB 88E 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. LK8 M0S M1P M7P P5Z P62 P64 PIMPY PQEST PQQKQ PQUKI PRINS RC3 SOI 7X8 OIOZB OTOTI 5PM DOA |
DOI | 10.1038/s41467-017-02726-3 |
DatabaseName | Springer Nature OA Free Journals PubMed CrossRef ProQuest Central (Corporate) Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Environment Abstracts Immunology Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central Advanced Technologies & Aerospace Database (1962 - current) ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database ProQuest Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts Environment Abstracts MEDLINE - Academic OSTI.GOV - Hybrid OSTI.GOV PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef Publicly Available Content Database ProQuest Central Student Oncogenes and Growth Factors Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management Health Research Premium Collection Natural Science Collection Biological Science Collection Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts Technology Collection Technology Research Database ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central Genetics Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) AIDS and Cancer Research Abstracts ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Immunology Abstracts Environment Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | CrossRef PubMed MEDLINE - Academic Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: C6C name: SpringerOpen url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Biology |
EISSN | 2041-1723 |
EndPage | 6 |
ExternalDocumentID | oai_doaj_org_article_1ca7b75f4eb446d4a968181d0b0fb0a1 1433129 10_1038_s41467_017_02726_3 29335457 |
Genre | Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article |
GroupedDBID | --- 0R~ 39C 3V. 53G 5VS 70F 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAHBH AAJSJ ABUWG ACGFO ACGFS ACIWK ACMJI ACPRK ACSMW ADBBV ADFRT ADRAZ AENEX AFKRA AFRAH AHMBA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AMTXH AOIJS ARAPS ASPBG AVWKF AZFZN BAPOH BBNVY BCNDV BENPR BGLVJ BHPHI BPHCQ BVXVI C6C CCPQU DIK EBLON EBS EE. EMOBN F5P FEDTE FYUFA GROUPED_DOAJ HCIFZ HMCUK HVGLF HYE HZ~ KQ8 LK8 M1P M48 M7P M~E NAO O9- OK1 P2P P62 PIMPY PQQKQ PROAC PSQYO RNS RNT RNTTT RPM SNYQT SV3 TSG UKHRP NPM AAYXX CITATION 7QL 7QP 7QR 7SN 7SS 7ST 7T5 7T7 7TM 7TO 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. P64 PQEST PQUKI PRINS RC3 SOI 7X8 AAADF AAPBV AAYJO ADQMX AEDAW OIOZB OTOTI ZA5 5PM |
ID | FETCH-LOGICAL-c633t-424c28ea5bbedeba5d1fe94daf0d9df9b68e90d752cd0e3ceb071bd3da430a0f3 |
IEDL.DBID | RPM |
ISSN | 2041-1723 |
IngestDate | Tue Oct 22 15:09:46 EDT 2024 Tue Sep 17 21:20:22 EDT 2024 Mon Jul 03 03:59:47 EDT 2023 Tue Aug 27 04:43:38 EDT 2024 Thu Oct 10 19:03:19 EDT 2024 Fri Aug 23 00:45:41 EDT 2024 Sat Sep 28 08:37:52 EDT 2024 Fri Oct 11 20:55:17 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Language | English |
License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c633t-424c28ea5bbedeba5d1fe94daf0d9df9b68e90d752cd0e3ceb071bd3da430a0f3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 AC02-05CH11231; ACI-1550228; 11521064; 2014DFG02050; 2015CB856902 Helmholtz Association USDOE Office of Science (SC) National Science Foundation (NSF) National Natural Science Foundation of China (NSFC) |
ORCID | 0000-0002-1279-7008 0000-0002-9734-9967 0000-0002-6213-3613 0000-0001-9859-1758 0000000262133613 0000000297349967 0000000212797008 0000000198591758 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768690/ |
PMID | 29335457 |
PQID | 1987709435 |
PQPubID | 546298 |
PageCount | 6 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_1ca7b75f4eb446d4a968181d0b0fb0a1 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5768690 osti_scitechconnect_1433129 proquest_miscellaneous_1989590560 proquest_journals_1987709435 crossref_primary_10_1038_s41467_017_02726_3 pubmed_primary_29335457 springer_journals_10_1038_s41467_017_02726_3 |
PublicationCentury | 2000 |
PublicationDate | 2018-01-15 |
PublicationDateYYYYMMDD | 2018-01-15 |
PublicationDate_xml | – month: 01 year: 2018 text: 2018-01-15 day: 15 |
PublicationDecade | 2010 |
PublicationPlace | London |
PublicationPlace_xml | – name: London – name: England – name: United States |
PublicationTitle | Nature communications |
PublicationTitleAbbrev | Nat Commun |
PublicationTitleAlternate | Nat Commun |
PublicationYear | 2018 |
Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
References | Adams (CR18) 2005; 757 Kullback, Leibler (CR49) 1951; 22 Pratt, Sangaline, Sorensen, Wang (CR23) 2015; 114 Chaudhuri, Heinz (CR40) 2006; 50 Gale, Jeon, Schenke, Tribedy, Venugopalan (CR37) 2013; 110 CR39 Sollfrank (CR33) 1997; 55 Srivastava (CR45) 2014; 15 Schmidhuber (CR1) 2015; 61 Broecker, Carrasquilla, Melko, Trebst (CR13) 2017; 7 Baldi, Sadowski, Whiteson (CR3) 2014; 5 Pang, Hatta, Wang, Xiao (CR31) 2015; 91 Romatschke (CR26) 2010; 19 Utama, Chen, Piekarewicz (CR8) 2016; 43 Heinz (CR25) 2010; 23 Friman (CR20) 2011; 814 CR9 CR48 CR47 Muller, Schukraft, Wyslouch (CR19) 2012; 62 CR46 Fukushima, Hatsuda (CR17) 2011; 74 Robnik-Sikonja, Kononenko (CR38) 2008; 20 CR44 CR43 CR42 CR41 Carrasquilla, Melko (CR10) 2017; 13 Luzum, Romatschke (CR22) 2008; 78 Stephanov (CR16) 2006; 2006 Ch’ng, Carrasquilla, Melko, Khatami (CR14) 2017; 7 Searcy, Huang, Pleier, Zhu (CR5) 2016; 93 CR52 CR51 Stöcker (CR35) 2005; 750 CR50 Barnard, Dawe, Dolan, Rajcic (CR6) 2017; 95 Gale, Jeon, Schenke (CR28) 2013; 28 Strickland (CR29) 2014; 45 Huovinen, Petreczky (CR32) 2010; 837 Pang, Wang, Wang (CR30) 2012; 86 Ablyazimov (CR21) 2017; 53 Carleo, Troyer (CR11) 2017; 355 Moult, Necib, Thaler (CR7) 2016; 12 LeCun, Bengio, Hinton (CR2) 2015; 521 Shen (CR34) 2016; 199 Baldi, Sadowski, Whiteson (CR4) 2015; 114 CR27 Lin, Ko, Li, Zhang, Pal (CR36) 2005; 72 Torlai, Melko (CR12) 2016; 94 Bernhard, Moreland, Bass, Liu, Heinz (CR24) 2016; 94 Stöcker, Greiner (CR15) 1986; 137 N Srivastava (2726_CR45) 2014; 15 M Robnik-Sikonja (2726_CR38) 2008; 20 J Adams (2726_CR18) 2005; 757 B Muller (2726_CR19) 2012; 62 G Carleo (2726_CR11) 2017; 355 C Gale (2726_CR37) 2013; 110 J Barnard (2726_CR6) 2017; 95 J Carrasquilla (2726_CR10) 2017; 13 R Utama (2726_CR8) 2016; 43 2726_CR27 J Searcy (2726_CR5) 2016; 93 LG Pang (2726_CR30) 2012; 86 B Friman (2726_CR20) 2011; 814 H Stöcker (2726_CR35) 2005; 750 2726_CR51 ZW Lin (2726_CR36) 2005; 72 2726_CR50 I Moult (2726_CR7) 2016; 12 P Baldi (2726_CR3) 2014; 5 UW Heinz (2726_CR25) 2010; 23 2726_CR52 S Pratt (2726_CR23) 2015; 114 P Baldi (2726_CR4) 2015; 114 J Schmidhuber (2726_CR1) 2015; 61 G Torlai (2726_CR12) 2016; 94 K Ch’ng (2726_CR14) 2017; 7 LG Pang (2726_CR31) 2015; 91 2726_CR44 2726_CR43 2726_CR46 P Romatschke (2726_CR26) 2010; 19 2726_CR42 2726_CR41 JE Bernhard (2726_CR24) 2016; 94 T Ablyazimov (2726_CR21) 2017; 53 C Gale (2726_CR28) 2013; 28 J Sollfrank (2726_CR33) 1997; 55 2726_CR9 K Fukushima (2726_CR17) 2011; 74 2726_CR48 M Strickland (2726_CR29) 2014; 45 2726_CR47 MA Stephanov (2726_CR16) 2006; 2006 M Luzum (2726_CR22) 2008; 78 P Huovinen (2726_CR32) 2010; 837 P Broecker (2726_CR13) 2017; 7 C Shen (2726_CR34) 2016; 199 S Kullback (2726_CR49) 1951; 22 H Stöcker (2726_CR15) 1986; 137 AK Chaudhuri (2726_CR40) 2006; 50 Y LeCun (2726_CR2) 2015; 521 2726_CR39 |
References_xml | – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: CR1 article-title: Deep learning in neural networks: an overview publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.09.003 contributor: fullname: Schmidhuber – ident: CR39 – volume: 355 start-page: 602 year: 2017 end-page: 606 ident: CR11 article-title: Solving the quantum many-body problem with artificial neural networks publication-title: Science doi: 10.1126/science.aag2302 contributor: fullname: Troyer – volume: 53 year: 2017 ident: CR21 article-title: Challenges in QCD matter physics–the scientific programme of the Compressed Baryonic Matter experiment at FAIR publication-title: Eur. Phys. J. A doi: 10.1140/epja/i2017-12248-y contributor: fullname: Ablyazimov – ident: CR51 – volume: 72 start-page: 064901 year: 2005 ident: CR36 article-title: A Multi-phase transport model for relativistic heavy ion collisions publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.72.064901 contributor: fullname: Pal – volume: 114 start-page: 111801 year: 2015 ident: CR4 article-title: Enhanced Higgs Boson to search with deep learning publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.111801 contributor: fullname: Whiteson – volume: 2006 start-page: 024 year: 2006 ident: CR16 article-title: QCD phase diagram: an overview publication-title: PoS. LAT contributor: fullname: Stephanov – ident: CR42 – volume: 5 start-page: 4308 year: 2014 ident: CR3 article-title: Searching for exotic particles in high-energy physics with deep learning publication-title: Nat. Commun. doi: 10.1038/ncomms5308 contributor: fullname: Whiteson – volume: 12 year: 2016 ident: CR7 article-title: New angles on energy correlation functions publication-title: J. High Energy Phys. doi: 10.1007/JHEP12(2016)153 contributor: fullname: Thaler – ident: CR46 – volume: 114 start-page: 202301 year: 2015 ident: CR23 article-title: Constraining the Eq. of state of super-hadronic matter from heavy-ion collisions publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.202301 contributor: fullname: Wang – volume: 50 start-page: 251 year: 2006 end-page: 258 ident: CR40 article-title: Hydrodynamical evolution of dissipative QGP fluid publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/50/1/030 contributor: fullname: Heinz – ident: CR50 – ident: CR9 – volume: 62 start-page: 361 year: 2012 end-page: 386 ident: CR19 article-title: First Results from Pb + Pb collisions at the LHC publication-title: Ann. Rev. Nucl. Part. Sci. doi: 10.1146/annurev-nucl-102711-094910 contributor: fullname: Wyslouch – volume: 19 start-page: 1 year: 2010 end-page: 53 ident: CR26 article-title: New Developments in Relativistic Viscous Hydrodynamics publication-title: Int. J. Mod. Phys. E doi: 10.1142/S0218301310014613 contributor: fullname: Romatschke – volume: 750 start-page: 121 year: 2005 end-page: 147 ident: CR35 article-title: Collective flow signals the quark gluon plasma publication-title: Nucl. Phys. A doi: 10.1016/j.nuclphysa.2004.12.074 contributor: fullname: Stöcker – volume: 93 start-page: 094033 year: 2016 ident: CR5 article-title: Determination of the polarization fractions in → using a deep machine learning technique publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.93.094033 contributor: fullname: Zhu – volume: 28 start-page: 1340011 year: 2013 ident: CR28 article-title: Hydrodynamic modeling of heavy-ion collisions publication-title: Int. J. Mod. Phys. A. doi: 10.1142/S0217751X13400113 contributor: fullname: Schenke – volume: 95 start-page: 014018 year: 2017 ident: CR6 article-title: Parton shower uncertainties in jet substructure analyses with deep neural networks publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.95.014018 contributor: fullname: Rajcic – volume: 55 start-page: 392 year: 1997 ident: CR33 article-title: Hydrodynamical description of 200-A/GeV/c S + Au collisions: Hadron and electromagnetic spectra publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.55.392 contributor: fullname: Sollfrank – volume: 20 start-page: 589 year: 2008 end-page: 600 ident: CR38 article-title: Explaining classifications for individual instances publication-title: Knowl. Data Eng. IEEE Trans. doi: 10.1109/TKDE.2007.190734 contributor: fullname: Kononenko – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: CR2 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: Hinton – volume: 814 start-page: 1 year: 2011 end-page: 980 ident: CR20 article-title: The CBM physics book: compressed baryonic matter in laboratory experiments publication-title: Lect. Notes Phys. doi: 10.1007/978-3-642-13293-3_1 contributor: fullname: Friman – volume: 23 start-page: 240 year: 2010 end-page: 292 ident: CR25 article-title: Early collective expansion: relativistic hydrodynamics and the transport properties of QCD matter publication-title: Landolt-Bornstein contributor: fullname: Heinz – ident: CR43 – volume: 43 start-page: 114002 year: 2016 ident: CR8 article-title: Nuclear charge radii: density functional theory meets Bayesian neural networks publication-title: J. Phys. G doi: 10.1088/0954-3899/43/11/114002 contributor: fullname: Piekarewicz – volume: 74 start-page: 014001 year: 2011 ident: CR17 article-title: The phase diagram of dense QCD publication-title: Rept. Prog. Phys. doi: 10.1088/0034-4885/74/1/014001 contributor: fullname: Hatsuda – ident: CR47 – volume: 837 start-page: 26 year: 2010 end-page: 53 ident: CR32 article-title: QCD equation of state and hadron resonance gas publication-title: Nucl. Phys. A doi: 10.1016/j.nuclphysa.2010.02.015 contributor: fullname: Petreczky – volume: 7 year: 2017 ident: CR13 article-title: Machine learning quantum phases of matter beyond the fermion sign problem publication-title: Sci. Rep. doi: 10.1038/s41598-017-09098-0 contributor: fullname: Trebst – volume: 91 start-page: 074027 year: 2015 ident: CR31 article-title: Analytical and numerical Gubser solutions of the second-order hydrodynamics publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.91.074027 contributor: fullname: Xiao – volume: 7 start-page: 031038 year: 2017 ident: CR14 article-title: Machine learning phases of strongly correlated fermions publication-title: Phys. Rev. X contributor: fullname: Khatami – volume: 137 start-page: 277 year: 1986 end-page: 392 ident: CR15 article-title: High-energy heavy ion collisions: probing the equation of state of highly excited hadronic matter publication-title: Phys. Rep. doi: 10.1016/0370-1573(86)90131-6 contributor: fullname: Greiner – volume: 86 start-page: 024911 year: 2012 ident: CR30 article-title: Effects of initial flow velocity fluctuation in event-by-event (3 + 1)D hydrodynamics publication-title: Phys. Rev. C. doi: 10.1103/PhysRevC.86.024911 contributor: fullname: Wang – volume: 15 start-page: 1929 year: 2014 ident: CR45 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. contributor: fullname: Srivastava – volume: 94 start-page: 024907 year: 2016 ident: CR24 article-title: Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.94.024907 contributor: fullname: Heinz – ident: CR27 – ident: CR44 – ident: CR48 – volume: 13 start-page: 431 year: 2017 end-page: 434 ident: CR10 article-title: Machine learning phases of matter publication-title: Nat. Phys. doi: 10.1038/nphys4035 contributor: fullname: Melko – volume: 22 start-page: 79 year: 1951 end-page: 86 ident: CR49 article-title: On information and sufficiency publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177729694 contributor: fullname: Leibler – ident: CR52 – volume: 45 start-page: 2355 year: 2014 ident: CR29 article-title: Anisotropic hydrodynamics: three lectures publication-title: Acta Phys. Pol. B doi: 10.5506/APhysPolB.45.2355 contributor: fullname: Strickland – volume: 199 start-page: 61 year: 2016 end-page: 85 ident: CR34 article-title: The iEBE-VISHNU code package for relativistic heavy-ion collisions publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2015.08.039 contributor: fullname: Shen – volume: 94 start-page: 165134 year: 2016 ident: CR12 article-title: Learning thermodynamics with boltzmann machines publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.94.165134 contributor: fullname: Melko – volume: 110 start-page: 012302 year: 2013 ident: CR37 article-title: Event-by-event anisotropic flow in heavy-ion collisions from combined Yang-Mills and viscous fluid dynamics publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.110.012302 contributor: fullname: Venugopalan – ident: CR41 – volume: 78 start-page: 034915 year: 2008 ident: CR22 article-title: Conformal relativistic viscous hydrodynamics: applications to RHIC results at s(NN)**(1/2) = 200-GeV publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.78.034915 contributor: fullname: Romatschke – volume: 757 start-page: 102 year: 2005 end-page: 183 ident: CR18 article-title: Experimental and theoretical challenges in the search for the quark gluon plasma: the STAR Collaboration’s critical assessment of the evidence from RHIC collisions publication-title: Nucl. Phys. A doi: 10.1016/j.nuclphysa.2005.03.085 contributor: fullname: Adams – ident: 2726_CR41 – volume: 521 start-page: 436 year: 2015 ident: 2726_CR2 publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: Y LeCun – volume: 53 year: 2017 ident: 2726_CR21 publication-title: Eur. Phys. J. A doi: 10.1140/epja/i2017-12248-y contributor: fullname: T Ablyazimov – ident: 2726_CR39 – volume: 22 start-page: 79 year: 1951 ident: 2726_CR49 publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177729694 contributor: fullname: S Kullback – volume: 5 start-page: 4308 year: 2014 ident: 2726_CR3 publication-title: Nat. Commun. doi: 10.1038/ncomms5308 contributor: fullname: P Baldi – ident: 2726_CR9 – volume: 94 start-page: 024907 year: 2016 ident: 2726_CR24 publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.94.024907 contributor: fullname: JE Bernhard – volume: 12 year: 2016 ident: 2726_CR7 publication-title: J. High Energy Phys. doi: 10.1007/JHEP12(2016)153 contributor: fullname: I Moult – volume: 15 start-page: 1929 year: 2014 ident: 2726_CR45 publication-title: J. Mach. Learn. Res. contributor: fullname: N Srivastava – volume: 94 start-page: 165134 year: 2016 ident: 2726_CR12 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.94.165134 contributor: fullname: G Torlai – volume: 20 start-page: 589 year: 2008 ident: 2726_CR38 publication-title: Knowl. Data Eng. IEEE Trans. doi: 10.1109/TKDE.2007.190734 contributor: fullname: M Robnik-Sikonja – volume: 355 start-page: 602 year: 2017 ident: 2726_CR11 publication-title: Science doi: 10.1126/science.aag2302 contributor: fullname: G Carleo – volume: 55 start-page: 392 year: 1997 ident: 2726_CR33 publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.55.392 contributor: fullname: J Sollfrank – ident: 2726_CR50 – volume: 199 start-page: 61 year: 2016 ident: 2726_CR34 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2015.08.039 contributor: fullname: C Shen – volume: 72 start-page: 064901 year: 2005 ident: 2726_CR36 publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.72.064901 contributor: fullname: ZW Lin – volume: 50 start-page: 251 year: 2006 ident: 2726_CR40 publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/50/1/030 contributor: fullname: AK Chaudhuri – ident: 2726_CR44 – volume: 757 start-page: 102 year: 2005 ident: 2726_CR18 publication-title: Nucl. Phys. A doi: 10.1016/j.nuclphysa.2005.03.085 contributor: fullname: J Adams – volume: 86 start-page: 024911 year: 2012 ident: 2726_CR30 publication-title: Phys. Rev. C. doi: 10.1103/PhysRevC.86.024911 contributor: fullname: LG Pang – ident: 2726_CR27 – ident: 2726_CR51 – ident: 2726_CR52 doi: 10.6084/m9.figshare.5457220.v1 – volume: 114 start-page: 202301 year: 2015 ident: 2726_CR23 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.202301 contributor: fullname: S Pratt – ident: 2726_CR48 – volume: 45 start-page: 2355 year: 2014 ident: 2726_CR29 publication-title: Acta Phys. Pol. B doi: 10.5506/APhysPolB.45.2355 contributor: fullname: M Strickland – volume: 93 start-page: 094033 year: 2016 ident: 2726_CR5 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.93.094033 contributor: fullname: J Searcy – volume: 91 start-page: 074027 year: 2015 ident: 2726_CR31 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.91.074027 contributor: fullname: LG Pang – volume: 2006 start-page: 024 year: 2006 ident: 2726_CR16 publication-title: PoS. LAT contributor: fullname: MA Stephanov – volume: 114 start-page: 111801 year: 2015 ident: 2726_CR4 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.111801 contributor: fullname: P Baldi – ident: 2726_CR43 – volume: 750 start-page: 121 year: 2005 ident: 2726_CR35 publication-title: Nucl. Phys. A doi: 10.1016/j.nuclphysa.2004.12.074 contributor: fullname: H Stöcker – ident: 2726_CR46 doi: 10.1109/ICCV.2015.123 – volume: 13 start-page: 431 year: 2017 ident: 2726_CR10 publication-title: Nat. Phys. doi: 10.1038/nphys4035 contributor: fullname: J Carrasquilla – volume: 43 start-page: 114002 year: 2016 ident: 2726_CR8 publication-title: J. Phys. G doi: 10.1088/0954-3899/43/11/114002 contributor: fullname: R Utama – volume: 814 start-page: 1 year: 2011 ident: 2726_CR20 publication-title: Lect. Notes Phys. doi: 10.1007/978-3-642-13293-3_1 contributor: fullname: B Friman – volume: 78 start-page: 034915 year: 2008 ident: 2726_CR22 publication-title: Phys. Rev. C doi: 10.1103/PhysRevC.78.034915 contributor: fullname: M Luzum – volume: 74 start-page: 014001 year: 2011 ident: 2726_CR17 publication-title: Rept. Prog. Phys. doi: 10.1088/0034-4885/74/1/014001 contributor: fullname: K Fukushima – volume: 137 start-page: 277 year: 1986 ident: 2726_CR15 publication-title: Phys. Rep. doi: 10.1016/0370-1573(86)90131-6 contributor: fullname: H Stöcker – ident: 2726_CR47 – volume: 7 start-page: 031038 year: 2017 ident: 2726_CR14 publication-title: Phys. Rev. X contributor: fullname: K Ch’ng – ident: 2726_CR42 – volume: 110 start-page: 012302 year: 2013 ident: 2726_CR37 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.110.012302 contributor: fullname: C Gale – volume: 62 start-page: 361 year: 2012 ident: 2726_CR19 publication-title: Ann. Rev. Nucl. Part. Sci. doi: 10.1146/annurev-nucl-102711-094910 contributor: fullname: B Muller – volume: 61 start-page: 85 year: 2015 ident: 2726_CR1 publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.09.003 contributor: fullname: J Schmidhuber – volume: 837 start-page: 26 year: 2010 ident: 2726_CR32 publication-title: Nucl. Phys. A doi: 10.1016/j.nuclphysa.2010.02.015 contributor: fullname: P Huovinen – volume: 19 start-page: 1 year: 2010 ident: 2726_CR26 publication-title: Int. J. Mod. Phys. E doi: 10.1142/S0218301310014613 contributor: fullname: P Romatschke – volume: 23 start-page: 240 year: 2010 ident: 2726_CR25 publication-title: Landolt-Bornstein contributor: fullname: UW Heinz – volume: 95 start-page: 014018 year: 2017 ident: 2726_CR6 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.95.014018 contributor: fullname: J Barnard – volume: 28 start-page: 1340011 year: 2013 ident: 2726_CR28 publication-title: Int. J. Mod. Phys. A. doi: 10.1142/S0217751X13400113 contributor: fullname: C Gale – volume: 7 year: 2017 ident: 2726_CR13 publication-title: Sci. Rep. doi: 10.1038/s41598-017-09098-0 contributor: fullname: P Broecker |
SSID | ssj0000391844 |
Score | 2.6645436 |
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... |
SourceID | doaj pubmedcentral osti proquest crossref pubmed springer |
SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
StartPage | 210 |
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 |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3LaxYxEA9SELyI9bm2SgRvGprdPHZzbMVSBD2Ihd5CntZDd1u_7cH_3plkv4_v84EXTwubsCQzmZnfbOZByOu29TE43bEcgmQyq8gGkzjzrsUGx1r3BhOFP37SZ-fyw4W62Gr1hTFhtTxwJdxRG1zve5Vl8uC5ROmMBhvTRu559txVx6dVW85U0cHCgOsilywZLoajlSw6AZUyeGKdZmLHEpWC_fCYQLD-BDZ_j5n85eK02KPTB-T-AiTpcd3APrmTxofkbm0t-eMR-Xw80nRT63izKbOSOMSuMPiFTpnCCBibKxouMRwv1rb0Kzqj5SpBXBTzTmhM6ZoujSW-Pibnp--_vDtjS_8EFrQQM5OdDN2QnPI-xeSdim1ORkaXeTQxG6-HZHjsVRciTyIkD3jDRxGdFNzxLJ6QvXEa0zNCnQedCJ6PiUFKIaIXYFsVT1F41XmtGvJmTUt7Xctk2HK9LQZbKW-B8rZQ3oqGnCC5NzOxxHV5AYy3C-PtvxjfkANklgWkgOVuA8YFhRlcGSEAwzTkcM1Du0jlyuIPlh5DKWG9rzbDIE94SeLGNN2WOUYZgIW8IU8ryzfrBGgkAHH2Del3DsPORnZHxm-XpWY3unXawDffro_N1rL-Sqjn_4NQB-QeoDwMWWStOiR78_fb9AKQ1OxfFqH5CXohG_Q priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagCIkLKu-0BRmJG1h14kfiEyqIpUKCA6JSb5afbQ9Ntt30wL9nxskuLK9TpNiKnLFn5hvPi5BXde1jcLphOQTJZFaRdSZx5l2NDY61bg0mCn_-oo9P5KdTdTpfuK3msMq1TCyCOg4B78gP0ThuMQxOvV1eMewahd7VuYXGbXKnxkp4mCm--Li5Y8Hq552Uc64MF93hShbJgKIZ7LFGM7Glj0rZfngMwF5_g5x_Rk7-5j4tWmmxS-7PcJIeTfv_gNxK_UNyd2ow-f0R-XrU03Q1VfNmQ2YlfYhdYggMHTKFEVA5lzScY1BenJrTr-iI-quEclHMPqExpSWd20ucPSYniw_f3h-zuYsCC1qIkclGhqZLTnmfYvJOxTonI6PLPJqYjdddMjy2qgmRJxGSB9Tho4hOCu54Fk_ITj_06RmhzoNkBPvHxCClENEL0LCKpyi8arxWFXm9pqVdTsUybHFyi85OlLdAeVsob0VF3iG5NzOx0HV5MVyf2ZlvbB1c61uVZfJguEbpjAaIUUfuefbc1RXZx82ygBew6G3A6KAwgkEjBCCZihys99DOvLmyP09SRV5uhoGr0FXi-jTclDlGGQCHvCJPpy3frBMAkgDc2Vak3ToMWz-yPdJfnJfK3WjcaQPffLM-Nr8s65-E2vv_X-yTe4DiMCSR1eqA7IzXN-k5IKXRvyjs8AMclRMx priority: 102 providerName: ProQuest – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1ba9VAEF5qRfBFvBtbJYJvurrJXpJ9EKliKUJ9EA_0bdlrK9ikPScF---d2SQHjx7Bp0B2EyazMzvfZOdCyMuqcsFbVdPkvaAiyUBbHRl1tsIGx0o1GhOFj7-oo4X4fCJPdsjc7mhi4Gqra4f9pBbLH29-Xl6_B4V_N6aMt29XIqs77rfgZNWK8hvkZi24QIk_nuB-3pm5BodGTLkz2x_dsE-5jD9celC3bRD070jKP45Ts5U6vEvuTPCyPBjl4R7Zid19cmtsOHn9gHw96Mp4OVb3pn2iOZ2InmNITNmnEkbABJ2X_gyD9MLYrH5VDmjPcmhXidkoZYjxopzaTZw-JIvDT98-HtGpqwL1ivOBilr4uo1WOhdDdFaGKkUtgk0s6JC0U23ULDSy9oFF7qMDFOICD1ZwZlnij8hu13fxCSmtg50S_CEdvBCcB8fB4koWA3eydkoW5NXMS3MxFs8w-dCbt2bkvAHOm8x5wwvyAdm9nomFr_ONfnlqJj0ylbeNa2QS0YEjG4TVCiBHFZhjyTFbFWQPF8sAfsAiuB6jhfwADg7ngGwKsj-voZlFzeBvlwYDLIHeF-th0DI8OrFd7K_yHC01gEVWkMfjkq_pBMDEAYc2BWk2hGHjQzZHuu9nuZI3OntKwztfz2LzG1n_ZNTT_yBzj9wGaIdxirSS-2R3WF7FZwCfBvc868QvLhoYYQ priority: 102 providerName: Scholars Portal – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Jb9UwELagCIlLRVlDS2UkbmDhxEviY3miqpDggKjUm-WVcmhS-tJD_31nnLwnAuXAKVLsRM6MJ_ONZyPkbV37GJxuWA5BMplVZJ1JnHlXY4NjrVuDicJfvuqTU_n5TJ3NZXIwF2bhvxfdh7Usooz_UjCgGs3EffJA1ZrjDl7p1fY8BSudd1LOeTF3P7rQPaVEP1wGEKW74OXfUZJ_uEqLBjp-THZn6EiPJl7vkXupf0IeTs0kb56Sb0c9Tb-myt1syKykCrELDHehQ6YwAurlgoZzDMCLUyP6NR1RV5WwLYqZJjSmdEnnVhI_npHT40_fVyds7pjAghZiZLKRoemSU96nmLxTsc7JyOgyjyZm43WXDI-takLkSYTkAWH4KKKTgjuexXOy0w99ekmo8_AXBFvHxCClENEL0KaKpyi8arxWFXm3oaW9nApj2OLQFp2dKG-B8rZQ3oqKfERyb2diUetyA3htZxmxdXCtb1WWyYORGqUzGuBEHbnn2XNXV2QfmWUBG2CB24CRQGEE40UIQC0VOdjw0M5yuLZ4pNJi8CSs9812GCQI3SKuT8N1mWOUASDIK_JiYvl2nQCGBGDMtiLtYjMsPmQ50v88L1W60ZDTBt75frNtflvWPwn16v-m75NHgOAwHJHV6oDsjFfX6TWgpNEfFvG4BcysC_s priority: 102 providerName: Springer Nature |
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 |
Volume | 9 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9tAEF6SlEIvpe-qSY0KvbUbr7QPaY-OiRsMDiFtwLdlX0oCteTGyqH_vrMrycR9XHqRQCvwemdm5xvtNzMIfcwy46wWOa6sZZhV3OFSeoKNzkKDYyEKGRKFF-fi7IrNl3y5h_iQCxNJ-9bcHtffV8f17U3kVq5XdjzwxMYXi2nAyBDVjffRfkHpgxA9br9UQtTC-gQZQsvxhsXtIOzHEITlAofmOeDmKKCHYscfxbL9cGvAvP4GOf9kTv52fBq90uwZetrDyXTSTfs52vP1C_S4azD58yW6nNSp_9FV88ZNhWP6EF4FCkzaVCmMgMtZpfYmkPJc15x-k7bBf0UqVxqyT1Ln_Trt20tcv0JXs9Nv0zPcd1HAVlDaYpYzm5dec2O880Zzl1VeMqcr4qSrpBGll8QVPLeOeGq9AdRhHHWaUaJJRV-jg7qp_VuUagM7I8Q_0lnGKHWGgoflxDtqeG4ET9CnYS3VuiuWoeIhNy1VJwQFQlBRCIom6CQs9_bNUOg6PmjurlUvbpVZXZiCV8wbCFwd01IAxMgcMaQyRGcJOgzCUoAXQtFbG9hBtoWAhlJAMgk6GmSoetvcqPCZpQiESpjvh-0wWFU4KtG1b-7jO5JLAIckQW86kW_nOWhOgoodZdj5I7sjoMixcnevuAn6PKjNg2n9c6He_fcPHaInAPACWxFn_AgdtHf3_j2AqNaMwHSWBVzL2ZcRejSZzL_O4X5yen5xCU-nYjqKnyfgumDlKJrYL2kOI8g |
link.rule.ids | 230,315,733,786,790,870,891,2115,12083,12792,21416,24346,27955,27956,31752,31753,33406,33407,33777,33778,41153,42222,43343,43633,43838,51609,53825,53827,74100,74390,74657 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB7BVgguiDehBYLEDaw6sZ3EJ9SiVgu0FapaqTfLr7Qcmmy72wP_nhknu7C8TpFiK3LGnplvPC-At0XhgrdVyVrvJZOtCqzRkTNnC2pwXFW1pkThw6Nqeio_n6mz8cJtPoZVLmViEtSh93RHvk3GcU1hcOrD7IpR1yjyro4tNG7DBpXcbCawsbt39PV4dctC9c8bKcdsGS6a7blMsoGEM1pkZcXEmkZKhfvx0SOD_Q10_hk7-ZsDNeml_QdwfwSU-c5wAh7Crdg9gjtDi8nvj-F4p8vj1VDPm_UtSwlE7JKCYPK-zXEElc5l7i8oLC8M7enn-YI0WArmyin_JA8xzvKxwcT5Ezjd3zv5OGVjHwXmKyEWTJbSl020yrkYorMqFG3UMtiWBx1a7aomah5qVfrAo_DRIe5wQQQrBbe8FU9h0vVdfA65dSgb0QLSwUspRHACdaziMQinSlepDN4taWlmQ7kMk9zcojED5Q1S3iTKG5HBLpF7NZNKXacX_fW5GTnHFN7WrlatjA5N1yCtrhBkFIE73jpuiww2abMMIgYqe-spPsgv0KQRArFMBlvLPTQjd87Nz7OUwZvVMPIVOUtsF_ubNEcrjfCQZ_Bs2PLVOhEiCUSedQb12mFY-5H1ke7bRardTeZdpfGb75fH5pdl_ZNQL_7_F6_h7vTk8MAcfDr6sgn3ENNRgCIr1BZMFtc38SXipoV7NTLHD92zF4g |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagCMQF8Sa0QJC4gbVObCfxCZXHqrwqhKi0N8vPlkOTbTc98O-ZcbwLy-sUKbYiZ-yZ-cb-PEPIs6qy3pmmptE5QUWUnnYqMGpNhQWOm6ZVeFH402FzcCTeL-Qi859WmVa5tonJUPvB4R75DIPjFmlwchYzLeLzm_nL5RnFClJ40prLaVwmV8BLMizj0C7azX4LZkLvhMj3ZhjvZiuRrASaaYjN6obyLd-UUvjDYwBV-xv8_JNF-dtRavJQ85vkRoaW5f60Fm6RS6G_Ta5OxSa_3yFf9vsynE2ZvekQabpKRE-RDlMOsYQWcD-npTtBgp6fCtWvyhF9WaJ1lXgTpfQhLMtcauL4Ljmav_36-oDmigrUNZyPVNTC1V0w0trggzXSVzEo4U1kXvmobNMFxXwra-dZ4C5YQCDWc28EZ4ZFfo_s9EMfHpDSWLCSEAsp74Tg3FsO3lay4LmVtW1kQZ6vZamXU-IMnQ68eacnyWuQvE6S17wgr1Dcm56Y9Dq9GM6PddYhXTnT2lZGESwEsV4Y1QDcqDyzLFpmqoLs4mRpwA6YANchU8iNENxwDqimIHvrOdRZT1f656oqyNNNM2gYHpuYPgwXqY-SCoAiK8j9aco34wSwxAGDtgVptxbD1o9st_TfTlIWbwz0GgXffLFeNr8M65-Cevj_v3hCroFW6I_vDj_skusA7pCpSCu5R3bG84vwCADUaB8nzfgBD84aRQ |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+equation-of-state-meter+of+quantum+chromodynamics+transition+from+deep+learning&rft.jtitle=Nature+communications&rft.au=Pang%2C+Long-Gang&rft.au=Zhou%2C+Kai&rft.au=Su%2C+Nan&rft.au=Petersen%2C+Hannah&rft.date=2018-01-15&rft.eissn=2041-1723&rft.volume=9&rft.issue=1&rft.spage=210&rft.epage=210&rft_id=info:doi/10.1038%2Fs41467-017-02726-3&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2041-1723&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2041-1723&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2041-1723&client=summon |