Survey of Stellar Atmosphere Parameter Estimation

Yuan Hailong; Zhang Yanxia; Zhang Haotong; Zhao Yongheng

Astronomical Research and Technology > 2018 > 15(3): 257-265.

Yuan Hailong, Zhang Yanxia, Zhang Haotong, Zhao Yongheng. Survey of Stellar Atmosphere Parameter Estimation[J]. Astronomical Research and Technology, 2018, 15(3): 257-265.

Citation:

Yuan Hailong, Zhang Yanxia, Zhang Haotong, Zhao Yongheng. Survey of Stellar Atmosphere Parameter Estimation[J]. Astronomical Research and Technology, 2018, 15(3): 257-265.

Citation:

Yuan Hailong, Zhang Yanxia, Zhang Haotong, Zhao Yongheng. Survey of Stellar Atmosphere Parameter Estimation[J]. Astronomical Research and Technology, 2018, 15(3): 257-265.

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Survey of Stellar Atmosphere Parameter Estimation

Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China

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Received Date: November 24, 2017
Revised Date: January 30, 2018
Available Online: November 20, 2023

Graphical Abstract

Abstract

Abstract

Starting with the significance of the stellar research, the basic conception and importance of stellar atmosphere parameters are introduced. The two major classes of estimation approaches of stellar atmosphere parameters, the direct technique and the indirect technique, are discussed. This paper focuses on the indirect technique, which can be further divided into several methods:the photometric method, the infrared flux method, the Balmer profile method, the spectral line ratio method, the line index method, the metal line diagnostics method, the spectral template fitting method and the machine learning method. While the spectral template fitting method is widely used in current large spectroscopic survey projects, such as SDSS, RAVE and LAMOST, the machine learning method has shown its advantages in many specific situations. Among various machine learning methods and technologies, the most popular ones are principal component analysis (PCA), k-nearest neighbors (KNN), support vector machine (SVM), and all kinds of artificial neural network (ANN) methods. On the other hand, traditional astrophysical methods still play prominent roles. The metal line diagnostics method is still favored by astronomers when analyzing high resolution spectra. The experimental results from infrared flux method are generally used as calibration.
- Parameter estimation,
- Stellar,
- Spectroscope,
- Machine learning

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References (67)

References

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