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 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 vn′ are also similar to the traditional vn for n= 2 and 3, but largely deviated from the Fourier ones for 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.