NAOC Open IR
Stellar spectral interpolation using machine learning
Sharma, Kaushal1; Singh, Harinder P.2; Gupta, Ranjan1; Kembhavi, Ajit1; Vaghmare, Kaustubh1,3; Shi, Jianrong4; Zhao, Yongheng4; Zhang, Jiannan4; Wu, Yue4
2020-08-01
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN0035-8711
Volume496Issue:4Pages:5002-5016
AbstractTheoretical stellar spectra rely on model stellar atmospheres computed based on our understanding of the physical laws at play in the stellar interiors. These models, coupled with atomic and molecular line databases, are used to generate theoretical stellar spectral libraries (SSLs) comprising of stellar spectra over a regular grid of atmospheric parameters (temperature, surface gravity, abundances) at any desired resolution. Another class of SSLs is referred to as empirical spectral libraries; these contain observed spectra at limited resolution. SSLs play an essential role in deriving the properties of stars and stellar populations. Both theoretical and empirical libraries suffer from limited coverage over the parameter space. This limitation is overcome to some extent by generating spectra for specific sets of atmospheric parameters by interpolating within the grid of available parameter space. In this work, we present a method for spectral interpolation in the optical region using machine learning algorithms that are generic, easily adaptable for any SSL without much change in the model parameters, and computationally inexpensive. We use two machine learning techniques, Random Forest (RF) and Artificial Neural Networks (ANN), and train the models on the MILES library. We apply the trained models to spectra from the CFLIB for testing and show that the performance of the two models is comparable. We show that both the models achieve better accuracy than the existing methods of polynomial based interpolation and the Gaussian radial basis function (RBF) interpolation.
Keywordmethods: data analysis techniques: spectroscopic astronomical data bases: miscellaneous stars: fundamental parameters stars: general
Funding OrganizationRaja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission
DOI10.1093/mnras/staa1809
WOS KeywordDIGITAL SKY SURVEY ; NEWTON-TELESCOPE LIBRARY ; NEURAL-NETWORKS ; UNSUPERVISED CLASSIFICATION ; GALAXIES ; POPULATIONS ; EVOLUTION ; DWARFS ; STARS
Language英语
Funding ProjectRaja Ramanna Fellowship - Department of Atomic Energy, Government of India[10/1(16)/2016/RRF-RD-II/630] ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR)[03(1428)/18/EMR-II] ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission
Funding OrganizationRaja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; Raja Ramanna Fellowship - Department of Atomic Energy, Government of India ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; National Astronomical Observatories, Chinese Academy of Sciences, Beijing ; Council of Scientific & Industrial Research (CSIR) ; Council of Scientific & Industrial Research (CSIR) ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Knowledge Network's data driven initiatives in Astronomy and Biology grant ; National Development and Reform Commission ; National Development and Reform Commission
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000574923200070
PublisherOXFORD UNIV PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/81858
Collection中国科学院国家天文台
Corresponding AuthorSharma, Kaushal
Affiliation1.Inter Univ Ctr Astron & Astrophys, Pune 411007, Maharashtra, India
2.Univ Delhi, Dept Phys & Astrophys, Delhi 110007, India
3.Persistent Syst Ltd, Pune 411004, Maharashtra, India
4.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Sharma, Kaushal,Singh, Harinder P.,Gupta, Ranjan,et al. Stellar spectral interpolation using machine learning[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2020,496(4):5002-5016.
APA Sharma, Kaushal.,Singh, Harinder P..,Gupta, Ranjan.,Kembhavi, Ajit.,Vaghmare, Kaustubh.,...&Wu, Yue.(2020).Stellar spectral interpolation using machine learning.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,496(4),5002-5016.
MLA Sharma, Kaushal,et al."Stellar spectral interpolation using machine learning".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 496.4(2020):5002-5016.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sharma, Kaushal]'s Articles
[Singh, Harinder P.]'s Articles
[Gupta, Ranjan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sharma, Kaushal]'s Articles
[Singh, Harinder P.]'s Articles
[Gupta, Ranjan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sharma, Kaushal]'s Articles
[Singh, Harinder P.]'s Articles
[Gupta, Ranjan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.