主成分分析在建立重离子碰撞流动研究基础中的应用
Application of Principal Component Analysis to establish proper basis for flow studies in heavy-ion collisions
摘要：
结果表明，主成分分析（PCA）应用于事件的单粒子分布在A-A碰撞允许建立最佳的基础上，从数据本身的各向异性流研究，在人工选择的基础函数。方位角粒子分布的PCA系数与来自常规分析技术的傅立叶系数相同。纵向主成分分析揭示了类似于勒让德多项式级数的最优基。在两个维度上的分析同时允许研究事件的纵向结构与粒子发射的方位各向异性的耦合。
英文摘要：
It is shown that Principal Component Analysis (PCA) applied to event-by-event single-particle distributions in A-A collisions allows establishing the most optimal basis for anisotropic flow studies from data itself, in contrast to manual selection of the basis functions. PCA coefficients for azimuthal particle distributions are identical to Fourier coefficients from a conventional analysis techniques. PCA applied in longitudinal dimension reveals optimal basis that is similar to Legendre polynomial series. Analysis in both dimensions simultaneously allows studying the coupling of the longitudinal structure of events with the azimuthal anisotropy of particle emission.
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