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Fast Derivation of Contact Binary Parameters for Large Photometric Surveys
Ding X(丁旭)1,2,3,4; Ji KF(季凯帆)1,2,3,4; Li, XuZhi5,6; Xiong, JianPing4,7; Cheng QY(程其原)1,2,3,4; Wang JL(王锦良)1,2,3,4; Liu H(刘辉)1,2,3,4
发表期刊ASTRONOMICAL JOURNAL
2022-11-01
卷号164期号:5
DOI10.3847/1538-3881/ac8e66
产权排序第1完成单位
收录类别SCI
摘要

Thanks to an enormous release of light curves of contact binaries, it is a challenge to derive the parameters of contact binaries using the Phoebe program and the Wilson-Devinney program with the Markov chain Monte Carlo (MCMC) algorithm. In this paper, we use neural network (NN) and MCMC algorithm to derive the parameters of contact binaries. The fitting of models is still done with the MCMC algorithm, but that the neural network is used to establish the mapping relationship between the parameters and the light curves generated beforehand by Phoebe. The NN model is trained with a set of Phoebe-generated light curves with known input parameters, and then combined with the MCMC algorithm to quickly obtain the posterior distribution of the parameters. Two NN models without and with the influence of third light are established, which can generate light curves with 100 points faster than Phoebe by about four orders of magnitude under the same running condition. In addition, the two models can generate the light curves with an error of less than a millimagnitude. The feasibility of NN and MCMC algorithm is also verified by the synthetic light curves generated by Phoebe and the light curves from Kepler survey data. NN and MCMC algorithms can quickly derive the parameters and the corresponding parameter errors of contact binaries from sky survey. These parameters can also be used as more precise initial input values for the objectives of individual detailed studies.

资助项目Chinese Natural Science Foundation[12103088] ; China Postdoctoral Science Foundation[2021M703099] ; China Manned Space Project[CMS-CSST-2021-A10] ; China Manned Space Project[CMS-CSST-2021-B10]
项目资助者Chinese Natural Science Foundation[12103088] ; China Postdoctoral Science Foundation[2021M703099] ; China Manned Space Project[CMS-CSST-2021-A10, CMS-CSST-2021-B10]
语种英语
学科领域天文学 ; 恒星与银河系 ; 计算机科学技术 ; 人工智能 ; 计算机应用
学科门类理学 ; 理学::天文学 ; 工学 ; 工学::计算机科学与技术(可授工学、理学学位)
文章类型Article
出版者IOP Publishing Ltd
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
ISSN0004-6256
URL查看原文
WOS记录号WOS:000869456400001
WOS研究方向Astronomy & Astrophysics
WOS类目Astronomy & Astrophysics
关键词[WOS]LIGHT CURVES
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
版本出版稿
条目标识符http://ir.ynao.ac.cn/handle/114a53/25602
专题南方基地
中国科学院天体结构与演化重点实验室
天文技术实验室
通讯作者Ding X(丁旭); Ji KF(季凯帆)
作者单位1.Yunnan Observatories, Chinese Academy of Sciences (CAS), P.O. Box 110, 650216 Kunming, People's Republic of China; dingxu@ynao.ac.cn, jkf@ynao.ac.cn;
2.Key Laboratory of the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, P.O. Box 110, 650216 Kunming, People's Republic of China;
3.Center for Astronomical Mega-Science, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012, People's Republic of China;
4.University of the Chinese Academy of Sciences, Yuquan Road 19#, Shijingshan Block, 100049 Beijing, People's Republic of China;
5.CAS Key Laboratory for Research in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China, Hefei 230026, People's Republic of China;
6.School of Astronomy and Space Sciences, University of Science and Technology of China, Hefei 230026, People's Republic of China;
7.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences Beijing 100101, People's Republic of China
第一作者单位中国科学院云南天文台
通讯作者单位中国科学院云南天文台
推荐引用方式
GB/T 7714
Ding X,Ji KF,Li, XuZhi,et al. Fast Derivation of Contact Binary Parameters for Large Photometric Surveys[J]. ASTRONOMICAL JOURNAL,2022,164(5).
APA Ding X.,Ji KF.,Li, XuZhi.,Xiong, JianPing.,Cheng QY.,...&Liu H.(2022).Fast Derivation of Contact Binary Parameters for Large Photometric Surveys.ASTRONOMICAL JOURNAL,164(5).
MLA Ding X,et al."Fast Derivation of Contact Binary Parameters for Large Photometric Surveys".ASTRONOMICAL JOURNAL 164.5(2022).
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