标题： 数据驱动的各向异性流在类喷注关联研究中的减除方法 显示英文标题

标题： Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions

摘要： 测量双粒子方位角角度相关性是一种研究喷流能量损失分布的有用工具，然而由于显著的各向异性流背景，它们较为复杂。 我们设计了一种数据驱动的方法，用于在喷流类似相关性分析中减去各向异性流背景。 我们首先要求一个高横动量粒子在给定的伪快度（$\eta$）范围内具有较大的反冲动量（$P_x$），以增强远离侧喷流类似相关性的接受范围内的数量。 然后，我们通过相对于$P_x$的$\eta$区域，计算近区和远区的双粒子相关性的差异，以减去各向异性流背景。 我们使用一个仅包含各向异性流的玩具模型以及包含喷流的 PYTHIA8 来证明我们数据驱动方法的有效性。 结果表明，数据驱动方法可以有效地减去各向异性流。 显示英文摘要

摘要： Measurements of two-particle azimuthal angle correlations are a useful tool to study the distribution of jet energy loss, however, they are complicated because of the significant anisotropic flow background. We devise a data-driven method for subtracting anisotropic flow background in jet-like correlation analysis. We first require a large recoil momentum ($P_x$) within a given pseudo-rapidity ($\eta$) range from a high-transverse momentum particle to enhance in-acceptance population of away-side jet-like correlations. Then we take the difference of two-particle correlations in the close-region and far-region with respect to the $\eta$ region of $P_x$ to subtract the anisotropic flow background. We use a toy model which contains only anisotropic flow and PYTHIA8 which have jets to demonstrate the validity of our data-driven method. The results indicate that the data-driven method can subtract anisotropic flow effectively.