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 {\tt 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 $v_n^{\prime }$ are also similar to the traditional $v_n$ for $n=2$ and 3, but largely deviated from the Fourier ones for $n\ge 4$. 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.

arXiv comment8 pages, 6 figures

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• Applications of deep learning to relativistic hydrodynamics
• Detecting Chiral Magnetic Effect via Deep Learning
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• Impact of Multiplicity Fluctuations on Entropy Scaling Across System Size
• Principal Component Analysis of Azimuthal Flow in Intermediate-Energy Heavy-Ion Reactions
• Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
• Functional Renormalization Group Approach for Signal Detection
• On feasibility of azimuthal flow studies with Principal Component Analysis
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• Searching for initial state fluctuations in heavy ion collisions at FAIR energy using Principal Component Analysis
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• Transverse momentum dependent decorrelation in Pb-Pb collisions at LHC