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

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 (Px) within a given pseudorapidity (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 Px to subtract the anisotropic flow background. Here, we use a toy model which contains only anisotropic flow and pythia8 which has jets, to demonstrate the validity of our data-driven method. The results indicate that the data-driven method can subtract anisotropic flow effectively.

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5]
  1. Central China Normal Univ., Wuhan (China); Purdue Univ., West Lafayette, IN (United States)
  2. Univ. of Science and Technology of China, Hefei (China); Purdue Univ., West Lafayette, IN (United States)
  3. Univ. of Science and Technology of China, Hefei (China)
  4. Central China Normal Univ., Wuhan (China)
  5. Purdue Univ., West Lafayette, IN (United States); Huzhou Univ., Zhejiang (China)
Publication Date:
Research Org.:
Purdue Univ., West Lafayette, IN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1611955
Alternate Identifier(s):
OSTI ID: 1563042
Grant/Contract Number:  
SC0012910; de-sc0012910
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review C
Additional Journal Information:
Journal Volume: 100; Journal Issue: 1; Journal ID: ISSN 2469-9985
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Physics; Jets & heavy flavor physics; Particle correlations & fluctuations; Quark-gluon plasma

Citation Formats

Zhang, Liang, Jiang, Kun, Li, Cheng, Liu, Feng, and Wang, Fuqiang. Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions. United States: N. p., 2019. Web. doi:10.1103/physrevc.100.014903.
Zhang, Liang, Jiang, Kun, Li, Cheng, Liu, Feng, & Wang, Fuqiang. Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions. United States. https://doi.org/10.1103/physrevc.100.014903
Zhang, Liang, Jiang, Kun, Li, Cheng, Liu, Feng, and Wang, Fuqiang. Thu . "Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions". United States. https://doi.org/10.1103/physrevc.100.014903. https://www.osti.gov/servlets/purl/1611955.
@article{osti_1611955,
title = {Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions},
author = {Zhang, Liang and Jiang, Kun and Li, Cheng and Liu, Feng and Wang, Fuqiang},
abstractNote = {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 (Px) within a given pseudorapidity (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 Px to subtract the anisotropic flow background. Here, we use a toy model which contains only anisotropic flow and pythia8 which has jets, to demonstrate the validity of our data-driven method. The results indicate that the data-driven method can subtract anisotropic flow effectively.},
doi = {10.1103/physrevc.100.014903},
journal = {Physical Review C},
number = 1,
volume = 100,
place = {United States},
year = {Thu Jul 11 00:00:00 EDT 2019},
month = {Thu Jul 11 00:00:00 EDT 2019}
}

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