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

doi:10.1093/mnras/stae1870

YNAO OpenIR > 恒星物理研究组

	The application of machine learning in tidal evolution simulation of star-planet systems
	Guo SS(郭帅帅)1,2,3,4 ; Guo JH(郭建恒)1,2,3,4 ; Ji KF(季凯帆)1,5 ; Liu H(刘辉)1,5 ; Xing L(邢磊)1,2,3,4
发表期刊	MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
	2024-08-27
卷号	533 期号:2 页码:2199-2212
DOI	10.1093/mnras/stae1870
产权排序	第1完成单位
收录类别	SCI ; EI
关键词	methods: statistical planet-star interactions stars: low-mass stars: rotation
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.
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
项目资助者	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
语种	英语
学科领域	天文学 ; 恒星与银河系
文章类型	Article
出版者	OXFORD UNIV PRESS
出版地	GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
ISSN	0035-8711
URL	查看原文
WOS记录号	WOS:001299412800007
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
关键词[WOS]	LOW-MASS ; SOLAR-TYPE ; CONVECTIVE BOUNDARIES ; STELLAR ROTATION ; DISSIPATION ; MODULES ; GYROCHRONOLOGY ; BRAKING ; MODELS
EI入藏号	20243516964757
EI主题词	Stars
EI分类号	1101 - 1302.1.2
引用统计
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.ynao.ac.cn/handle/114a53/27575
专题	恒星物理研究组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 Observatories, Chinese Academy of Sciences, P.O. Box 110, Kunming 650011, People’s Republic of China; 2.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China; 3.Key Laboratory for the Structure and Evolution of Celestial Objects, CAS, Kunming 650011, People’s Republic of China; 4.International Centre of Supernovae, Yunnan Key Laboratory, Kunming 650216, P. R. China; 5.Yunnan Key Laboratory of Solar Physics and Space Science, Kunming 650216, China
第一作者单位	中国科学院云南天文台
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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