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The application of machine learning in tidal evolution simulation of star-planet systems

文献类型：期刊论文

作者Guo SS(郭帅帅)2,3,4,5; Guo JH(郭建恒)2,3,4,5; Ji KF(季凯帆)1,5; Liu H(刘辉)1,5; Xing L(邢磊)2,3,4,5
刊名MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
出版日期2024-08-27
卷号533期号:2页码:2199-2212
关键词methods: statistical planet-star interactions stars: low-mass stars: rotation
ISSN号0035-8711
DOI10.1093/mnras/stae1870
产权排序第1完成单位
文献子类Article
英文摘要With the release of a large amount of astronomical data, an increasing number of close-in hot Jupiters have been discovered. Calculating their evolutionary curves using star-planet interaction models presents a challenge. To expedite the generation of evolutionary curves for these close-in hot Jupiter systems, we utilized tidal interaction models established on mesa to create 15 745 samples of star-planet systems and 7500 samples of stars. Additionally, we employed a neural network (Multilayer Perceptron - MLP) to predict the evolutionary curves of the systems, including stellar effective temperature, radius, stellar rotation period, and planetary orbital period. The median relative errors of the predicted evolutionary curves were found to be 0.15 per cent, 0.43 per cent, 2.61 per cent, and 0.57 per cent, respectively. Furthermore, the speed at which we generate evolutionary curves exceeds that of model-generated curves by more than four orders of magnitude. We also extracted features of planetary migration states and utilized lightgbm to classify the samples into six categories for prediction. We found that by combining three types that undergo long-term double synchronization into one label, the classifier effectively recognized these features. Apart from systems experiencing long-term double synchronization, the median relative errors of the predicted evolutionary curves were all below 4 per cent. Our work provides an efficient method to save significant computational resources and time with minimal loss in accuracy. This research also lays the foundation for analysing the evolutionary characteristics of systems under different migration states, aiding in the understanding of the underlying physical mechanisms of such systems. Finally, to a large extent, our approach could replace the calculations of theoretical models.
学科主题天文学 ; 恒星与银河系
URL标识查看原文
出版地GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
WOS关键词LOW-MASS ; SOLAR-TYPE ; CONVECTIVE BOUNDARIES ; STELLAR ROTATION ; DISSIPATION ; MODULES ; GYROCHRONOLOGY ; BRAKING ; MODELS
资助项目Chinese Academy of Sciences; Strategic Priority Research Program of Chinese Academy of Sciences[12288102]; XDB 41000000and National Natural Science Foundation of China[11973082]; XDB 41000000and National Natural Science Foundation of China[12433009]; National Natural Science Foundation of China[2021YFA1600400/2021YFA1600402]; National Key R&D Program of China[202201AT070158]; Natural Science Foundation of Yunnan Province[202302AN360001]; International Centre of Supernovae, Yunnan Key Laboratory; Stellar Astrophysics Group at Yunnan Observatories, Chinese Academy of Sciences[202205AG070009]; Yunnan Key Laboratory of Solar Physics and Space Science
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001299412800007
出版者OXFORD UNIV PRESS
资助机构Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences[12288102] ; XDB 41000000and National Natural Science Foundation of China[11973082, 12433009] ; National Natural Science Foundation of China[2021YFA1600400/2021YFA1600402] ; National Key R&D Program of China[202201AT070158] ; Natural Science Foundation of Yunnan Province[202302AN360001] ; International Centre of Supernovae, Yunnan Key Laboratory ; Stellar Astrophysics Group at Yunnan Observatories, Chinese Academy of Sciences[202205AG070009] ; Yunnan Key Laboratory of Solar Physics and Space Science
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27575]  
专题云南天文台_恒星物理研究组
云南天文台_中国科学院天体结构与演化重点实验室
天文技术实验室
作者单位1.Yunnan Key Laboratory of Solar Physics and Space Science, Kunming 650216, China
2.International Centre of Supernovae, Yunnan Key Laboratory, Kunming 650216, P. R. China;
3.Key Laboratory for the Structure and Evolution of Celestial Objects, CAS, Kunming 650011, People’s Republic of China;
4.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China;
5.Yunnan Observatories, Chinese Academy of Sciences, P.O. Box 110, Kunming 650011, People’s Republic of China;
推荐引用方式
GB/T 7714		 Guo SS,Guo JH,Ji KF,et al. The application of machine learning in tidal evolution simulation of star-planet systems[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2024,533(2):2199-2212.
APA 郭帅帅,郭建恒,季凯帆,刘辉,&邢磊.(2024).The application of machine learning in tidal evolution simulation of star-planet systems.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,533(2),2199-2212.
MLA 郭帅帅,et al."The application of machine learning in tidal evolution simulation of star-planet systems".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 533.2(2024):2199-2212.

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来源：云南天文台

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