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arXiv · 1902.06027

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

Liang Zhang·Kun Jiang·Cheng Li·Feng Liu·Fuqiang Wang
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Abstract

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 ($η$) 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 $η$ 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.

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Liang Zhang, Kun Jiang, Cheng Li, Feng Liu, Fuqiang Wang. 2019-07-23. Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions. https://doi.org/10.1103/physrevc.100.014903

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