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Title: A machine learning study to identify spinodal clumping in high energy nuclear collisions

Abstract

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differentiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. Finally, in such scenarios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (E lab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.

Authors:
 [1];  [2];  [1];  [3];  [3];  [4]
  1. Frankfurt Inst for Advanced Studies (Germany)
  2. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Frankfurt Inst for Advanced Studies (Germany); Goethe Univ., Frankfurt am Main (Germany); GSI Helmholtzzentrum fur Schwerionenforschung, Darmstadt (Germany)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26); National Science Foundation (NSF)
OSTI Identifier:
1603550
Grant/Contract Number:  
[AC02-05CH11231; ACI-1550228]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
[Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2019; Journal Issue: 12]; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; heavy ion phenomenology; QCD phenomenology

Citation Formats

Steinheimer, Jan, Pang, Long-Gang, Zhou, Kai, Koch, Volker, Randrup, Jørgen, and Stoecker, Horst. A machine learning study to identify spinodal clumping in high energy nuclear collisions. United States: N. p., 2019. Web. doi:10.1007/JHEP12(2019)122.
Steinheimer, Jan, Pang, Long-Gang, Zhou, Kai, Koch, Volker, Randrup, Jørgen, & Stoecker, Horst. A machine learning study to identify spinodal clumping in high energy nuclear collisions. United States. doi:10.1007/JHEP12(2019)122.
Steinheimer, Jan, Pang, Long-Gang, Zhou, Kai, Koch, Volker, Randrup, Jørgen, and Stoecker, Horst. Mon . "A machine learning study to identify spinodal clumping in high energy nuclear collisions". United States. doi:10.1007/JHEP12(2019)122. https://www.osti.gov/servlets/purl/1603550.
@article{osti_1603550,
title = {A machine learning study to identify spinodal clumping in high energy nuclear collisions},
author = {Steinheimer, Jan and Pang, Long-Gang and Zhou, Kai and Koch, Volker and Randrup, Jørgen and Stoecker, Horst},
abstractNote = {The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differentiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. Finally, in such scenarios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.},
doi = {10.1007/JHEP12(2019)122},
journal = {Journal of High Energy Physics (Online)},
number = [12],
volume = [2019],
place = {United States},
year = {2019},
month = {12}
}

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