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• W3100761704 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 $ngeq 4$. 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." @default.
• W3100761704 created "2020-11-23" @default.
• W3100761704 creator A5064541734 @default.
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• W3100761704 date "2019-10-01" @default.
• W3100761704 modified "2023-10-14" @default.
• W3100761704 title "Principal component analysis of collective flow in relativistic heavy-ion collisions" @default.
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• W3100761704 doi "https://doi.org/10.1140/epjc/s10052-019-7379-y" @default.
• W3100761704 hasPublicationYear "2019" @default.
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