Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies

Published:2023-05-02 Issue:9 Volume:107 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Mallick Neelkamal^ORCID,Prasad Suraj^ORCID,Mishra Aditya Nath^ORCID,Sahoo Raghunath^ORCID,Barnaföldi Gergely Gábor^ORCID

Funder

University Grants Commission

Department of Atomic Energy, Government of India

Department of Science and Technology, Ministry of Science and Technology, India

CERN

Nemzeti Kutatási Fejlesztési és Innovációs Hivatal

Indian Institute of Technology Indore

Wigner Fizikai Kutatóközpont, Magyar Tudományos Akadémia

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.107.094001/fulltext

Reference55 articles.

1. Signatures of quark-gluon plasma formation in high energy heavy-ion collisions: a critical review

2. Collective Flow and Viscosity in Relativistic Heavy-Ion Collisions

3. Particle-Type Dependence of Azimuthal Anisotropy and Nuclear Modification of Particle Production inAu+AuCollisions atsNN=200 GeV

4. Elliptic Flow of Charged Particles in Pb-Pb Collisions atsNN=2.76 TeV

5. Higher Harmonic Anisotropic Flow Measurements of Charged Particles in Pb-Pb Collisions atsNN=2.76 TeV

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider;Physical Review D;2024-08-13

2. Inclusive, prompt and nonprompt J/ψ identification in proton-proton collisions at the Large Hadron Collider using machine learning;Physical Review D;2024-01-05

3. Toward unbiased flow measurements in pp collisions at the CERN Large Hadron Collider;Physical Review C;2023-09-18

4. Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning;The European Physical Journal A;2023-07-29

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3