Principal component analysis of collective flow in relativistic heavy-ion collisions
Abstract In this paper, we implement principal component analysis (PCA) to study the single particle distributions generated from thousands of $$\mathtt {VISH2+1}$$ VISH2+1 hydrodynamic simulations with an aim to explore if a machine could directly discover flow from the huge amount of data without...
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2019-10-01
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Series: | European Physical Journal C: Particles and Fields |
Online Access: | http://link.springer.com/article/10.1140/epjc/s10052-019-7379-y |
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doaj-24056d8d2dcf486993a5b1e0a935ac722020-11-25T03:36:57ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60441434-60522019-10-0179101810.1140/epjc/s10052-019-7379-yPrincipal component analysis of collective flow in relativistic heavy-ion collisionsZiming Liu0Wenbin Zhao1Huichao Song2Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking UniversityDepartment of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking UniversityDepartment of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking UniversityAbstract In this paper, we implement principal component analysis (PCA) to study the single particle distributions generated from thousands of $$\mathtt {VISH2+1}$$ VISH2+1 hydrodynamic simulations with an aim to explore if a machine could directly discover flow from the huge amount of data without explicit instructions from human-beings. We found that the obtained PCA eigenvectors are similar to but not identical with the traditional Fourier bases. Correspondingly, the PCA defined flow harmonics $$v_n^\prime $$ vn′ are also similar to the traditional $$v_n$$ vn for $$n=2$$ n=2 and 3, but largely deviated from the Fourier ones for $$n\ge 4$$ n≥4 . A further study on the symmetric cumulants and the Pearson coefficients indicates that mode-coupling effects are reduced for these flow harmonics defined by PCA.http://link.springer.com/article/10.1140/epjc/s10052-019-7379-y |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ziming Liu Wenbin Zhao Huichao Song |
spellingShingle |
Ziming Liu Wenbin Zhao Huichao Song Principal component analysis of collective flow in relativistic heavy-ion collisions European Physical Journal C: Particles and Fields |
author_facet |
Ziming Liu Wenbin Zhao Huichao Song |
author_sort |
Ziming Liu |
title |
Principal component analysis of collective flow in relativistic heavy-ion collisions |
title_short |
Principal component analysis of collective flow in relativistic heavy-ion collisions |
title_full |
Principal component analysis of collective flow in relativistic heavy-ion collisions |
title_fullStr |
Principal component analysis of collective flow in relativistic heavy-ion collisions |
title_full_unstemmed |
Principal component analysis of collective flow in relativistic heavy-ion collisions |
title_sort |
principal component analysis of collective flow in relativistic heavy-ion collisions |
publisher |
SpringerOpen |
series |
European Physical Journal C: Particles and Fields |
issn |
1434-6044 1434-6052 |
publishDate |
2019-10-01 |
description |
Abstract In this paper, we implement principal component analysis (PCA) to study the single particle distributions generated from thousands of $$\mathtt {VISH2+1}$$ VISH2+1 hydrodynamic simulations with an aim to explore if a machine could directly discover flow from the huge amount of data without explicit instructions from human-beings. We found that the obtained PCA eigenvectors are similar to but not identical with the traditional Fourier bases. Correspondingly, the PCA defined flow harmonics $$v_n^\prime $$ vn′ are also similar to the traditional $$v_n$$ vn for $$n=2$$ n=2 and 3, but largely deviated from the Fourier ones for $$n\ge 4$$ n≥4 . A further study on the symmetric cumulants and the Pearson coefficients indicates that mode-coupling effects are reduced for these flow harmonics defined by PCA. |
url |
http://link.springer.com/article/10.1140/epjc/s10052-019-7379-y |
work_keys_str_mv |
AT zimingliu principalcomponentanalysisofcollectiveflowinrelativisticheavyioncollisions AT wenbinzhao principalcomponentanalysisofcollectiveflowinrelativisticheavyioncollisions AT huichaosong principalcomponentanalysisofcollectiveflowinrelativisticheavyioncollisions |
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1724547987361759232 |