Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics
Abstract
Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks. The latter can be characterized by the temperature (T) dependent potential, with real (VR(T, r)) and imaginary (VI (T, r)) parts, as a function of the spatial separation (r). Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network, here, we obtain VR(T, r) and VI (T, r) in a model independent fashion. The temperature dependence of VR(T, r) was found to be very mild between T ≈ 0 -334 MeV. For T = 151- 334 MeV, VI (T, r) shows a rapid increase with T and r, which is much larger than the perturbation-theory-based expectations
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Natural Science Foundation of China (NSFC); Guangdong Major Project of Basic and Applied Basic Research
- OSTI Identifier:
- 1841594
- Alternate Identifier(s):
- OSTI ID: 1842807
- Report Number(s):
- BNL-222684-2022-JAAM
Journal ID: ISSN 2470-0010; PRVDAQ; 014017
- Grant/Contract Number:
- SC0012704; 2020B0301030008; 11890712; 12075129
- Resource Type:
- Published Article
- Journal Name:
- Physical Review D
- Additional Journal Information:
- Journal Name: Physical Review D Journal Volume: 105 Journal Issue: 1; Journal ID: ISSN 2470-0010
- Publisher:
- American Physical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 73 NUCLEAR PHYSICS AND RADIATION PHYSICS
Citation Formats
Shi, Shuzhe, Zhou, Kai, Zhao, Jiaxing, Mukherjee, Swagato, and Zhuang, Pengfei. Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics. United States: N. p., 2022.
Web. doi:10.1103/PhysRevD.105.014017.
Shi, Shuzhe, Zhou, Kai, Zhao, Jiaxing, Mukherjee, Swagato, & Zhuang, Pengfei. Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics. United States. https://doi.org/10.1103/PhysRevD.105.014017
Shi, Shuzhe, Zhou, Kai, Zhao, Jiaxing, Mukherjee, Swagato, and Zhuang, Pengfei. Fri .
"Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics". United States. https://doi.org/10.1103/PhysRevD.105.014017.
@article{osti_1841594,
title = {Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics},
author = {Shi, Shuzhe and Zhou, Kai and Zhao, Jiaxing and Mukherjee, Swagato and Zhuang, Pengfei},
abstractNote = {Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks. The latter can be characterized by the temperature (T) dependent potential, with real (VR(T, r)) and imaginary (VI (T, r)) parts, as a function of the spatial separation (r). Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network, here, we obtain VR(T, r) and VI (T, r) in a model independent fashion. The temperature dependence of VR(T, r) was found to be very mild between T ≈ 0 -334 MeV. For T = 151- 334 MeV, VI (T, r) shows a rapid increase with T and r, which is much larger than the perturbation-theory-based expectations},
doi = {10.1103/PhysRevD.105.014017},
journal = {Physical Review D},
number = 1,
volume = 105,
place = {United States},
year = {Fri Jan 21 00:00:00 EST 2022},
month = {Fri Jan 21 00:00:00 EST 2022}
}
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