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:
-
- Central China Normal Univ., Wuhan (China); Purdue Univ., West Lafayette, IN (United States)
- Univ. of Science and Technology of China, Hefei (China); Purdue Univ., West Lafayette, IN (United States)
- Univ. of Science and Technology of China, Hefei (China)
- Central China Normal Univ., Wuhan (China)
- 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}
}
Web of Science
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