Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
文献类型：期刊论文
| 作者 | Wang, Yongjia3; Li, Fupeng2,3; Li, Qingfeng1,3; Lu, Hongliang5; Zhou, Kai4 |
| 刊名 | PHYSICS LETTERS B
 |
| 出版日期 | 2021-11-10 |
| 卷号 | 822页码:5 |
| ISSN号 | 0370-2693 |
| DOI | 10.1016/j.physletb.2021.136669 |
| 通讯作者 | Wang, Yongjia(wangyongjia@zjhu.edu.cn) ; Li, Qingfeng(liqf@zjhu.edu.cn) |
| 英文摘要 | A deep convolutional neural network (CNN) is developed to study symmetry energy (E-sym(rho)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff E-sym(rho) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different E-sym(rho)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of E-sym(rho)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm. (C) 2021 The Author(s). Published by Elsevier B.V. |
| WOS关键词 | IMPACT PARAMETER DETERMINATION ; QUANTUM MOLECULAR-DYNAMICS ; NEURAL-NETWORKS ; EQUATION ; STATE ; PROGRESS ; PHYSICS |
| 资助项目 | National Natural Science Foundation of China[U2032145] ; National Natural Science Foundation of China[11875125] ; National Natural Science Foundation of China[12047568] ; National Key Research and Development Program of China[2020YFE0202002] ; BMBF under the ErUM-Data project ; Ten Thousand Talents Program of Zhejiang province[2018R52017] ; AI grant at FIAS of SAMSON AG, Frankfurt |
| WOS研究方向 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000703669100018 |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; BMBF under the ErUM-Data project ; Ten Thousand Talents Program of Zhejiang province ; AI grant at FIAS of SAMSON AG, Frankfurt |
| 源URL | [http://119.78.100.186/handle/113462/136294]  |
| 专题 | 中国科学院近代物理研究所 |
| 通讯作者 | Wang, Yongjia; Li, Qingfeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China 2.Zhejiang Univ Technol, Sch Sci, Hangzhou 310014, Peoples R China 3.Huzhou Univ, Sch Sci, Huzhou 313000, Peoples R China 4.Frankfurt Inst Adv Studies, Ruth Moufang Str 1, D-60438 Frankfurt, Germany 5.Huawei Technol Co Ltd, HiSilicon Res Dept, Shenzhen 518000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yongjia,Li, Fupeng,Li, Qingfeng,et al. Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning[J]. PHYSICS LETTERS B,2021,822:5. |
| APA | Wang, Yongjia,Li, Fupeng,Li, Qingfeng,Lu, Hongliang,&Zhou, Kai.(2021).Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning.PHYSICS LETTERS B,822,5. |
| MLA | Wang, Yongjia,et al."Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning".PHYSICS LETTERS B 822(2021):5. |
入库方式： OAI收割
来源：近代物理研究所
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