Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
文献类型：期刊论文
| 作者 | Li,Fupeng3,4; Wang,Yongjia4; Lü,Hongliang5; Li,Pengcheng1,4; Li,Qingfeng2,4; Liu,Fanxin3 |
| 刊名 | Journal of Physics G: Nuclear and Particle Physics
 |
| 出版日期 | 2020-09-29 |
| 卷号 | 47期号:11 |
| 关键词 | heavy ion collision impact parameter artificial intelligence intermediate energies transport model |
| ISSN号 | 0954-3899 |
| DOI | 10.1088/1361-6471/abb1f9 |
| 英文摘要 | AbstractThe impact parameter is one of the crucial physical quantities of heavy-ion collisions, and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the convolutional neural networks (CNN) and light gradient boosting machine (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au + Au collisions with the impact parameter of 0 ? b ? 10 fm at intermediate energies (Elab = 0.2–1.0 GeV/nucleon) are simulated with the ultrarelativistic quantum molecular dynamics model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM’s visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments. |
| 语种 | 英语 |
| WOS记录号 | IOP:0954-3899-47-11-ABB1F9 |
| 出版者 | IOP Publishing |
| 源URL | [http://119.78.100.186/handle/113462/139489]  |
| 专题 | 中国科学院近代物理研究所 |
| 作者单位 | 1.School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, People’s Republic of China 2.Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, People’s Republic of China 3.College of Science, Zhejiang University of Technology, Hangzhou 310014, People’s Republic of China 4.School of Science, Huzhou University, Huzhou 313000, People’s Republic of China 5.HiSilicon Research Department, Huawei Technologies Co., Ltd., Shenzhen 518000, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | Li,Fupeng,Wang,Yongjia,Lü,Hongliang,et al. Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies[J]. Journal of Physics G: Nuclear and Particle Physics,2020,47(11). |
| APA | Li,Fupeng,Wang,Yongjia,Lü,Hongliang,Li,Pengcheng,Li,Qingfeng,&Liu,Fanxin.(2020).Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies.Journal of Physics G: Nuclear and Particle Physics,47(11). |
| MLA | Li,Fupeng,et al."Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies".Journal of Physics G: Nuclear and Particle Physics 47.11(2020). |
入库方式： OAI收割
来源：近代物理研究所
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