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Stellar Spectrum Analysis for Automated Estimation of Atmospheric Parameter

This research explores automated estimation of atmospheric parameters using stellar spectrum analysis techniques like sparse feature extraction and linearly supporting features extraction. The study focuses on the detection, description, and estimation of atmospheric parameters through various methods like LASSO and statistical index schemes. Experimental results on real and synthetic spectra demonstrate significant accuracy.

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Stellar Spectrum Analysis for Automated Estimation of Atmospheric Parameter

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  1. Stellar Spectrum Analysis for Automated Estimation of Atmospheric Parameter 李乡儒 2015. 11.28 Collaborators： Ali Luo , Yongheng Zhao, Georges Comte , Fang Zuo, Q.M. Jonathan Wu, Tan Yang, Yongjun Wang, Yu Lu

  2. Contents • Problem, Available Schemes and Objective • Sparse Feature Extraction • Linearly Supporting Features Extraction • Adaptive Basic Structure Elements and Spectral Feature Extraction Xiangru Li, Q.M. Jonathan Wu, Ali Luo, Yongheng Zhao, Yu Lu, Fang Zuo, Tan Yang, Yongjun Wang, 2014, ApJ, 790, 105 X. Li, Yu Lu, G. Comte, Ali Luo, Yongheng Zhao, Yongjun Wang, 2015, ApJS, 218,3 Yu Lu, X. Li, 2015, MNRAS, 452(2): 1394 Tan Yang, X. Li, 2015, MNRAS, 2015, 452, 158

  3. Problem

  4. Available Schemes and Objective • Template Matching Method • Statistical Index Scheme • Line Index Method • Physical Interpretability • Robustness • local, sparse

  5. Problem and Objective • Detection • Description • Estimation

  6. SDSS Data 50000, [4088, 9740]K for Teff, [1.015, 4.998] dex for log g, [-3.497 0.268]dex for [Fe/H]

  7. LAMOST Data 33963 [3853.2, 9927] K for Teff, [0.8920, 4.9959] dex for log g, [-2.3280 0.9360] dex for [Fe/H]

  8. Synthetic Data Kurucz’s NEWODF models, SPECTRUM package 18969 [4000, 9750] K for Teff, 45 values, step sizes of 100 K between 4000 and 7500, 205 K between 7750 and 9750 K [1, 5] dex for log g, 17 values, step size of 0.25 dex [-3.6 0.3]dex for [Fe/H], 27 values , step size of 0.2 dex between -3.6 and -1 dex, and 0.1 dex between -1 and 0.3 dex

  9. Sparse Feature Extraction Xiangru Li, Q.M. Jonathan Wu, Ali Luo, Yongheng Zhao, Yu Lu, Fang Zuo, Tan Yang, Yongjun Wang, 2014, ApJ, 790, 105

  10. Detection • LASSO (least absolute shrinkage and selection operator)

  11. Detection

  12. Detection

  13. Detection 99.74% Re Fiorentin, P., et al. 2007, A&A, 467, 1373

  14. Detection

  15. Detection

  16. Detection

  17. Detection

  18. Detection

  19. Detection

  20. Description and Estimation Point Description (PD) Local Integration (LI)

  21. Experimental Results • On Real Spectra Re Fiorentin, P., et al. 2007, A&A, 467, 1373

  22. Experimental Results • On Synthetic Spectra

  23. Compactness • On Real Spectra 99.74% Re Fiorentin, P., et al. 2007, A&A, 467, 1373

  24. Linearity v.s. nonlinearity

  25. Other typical non-linear estimators Feedforward neural network Generalized Additive Models Multivariate Adaptive Regression Splines Random Forest

  26. Linearly Supporting Features Extraction X. Li, Yu Lu, G. Comte, Ali Luo, Yongheng Zhao, Yongjun Wang, 2015, ApJS, 218,3 Yu Lu, X. Li, 2015, MNRAS, 452(2): 1394

  27. Linearly Supporting Feature Extraction

  28. Dissolution of nonlinearity Dependeny of effectiveness on wavelength and frequency

  29. For log g, [Fe/H] …

  30. Adaptive Basic Structure Elements and Spectral Feature Extraction Tan Yang, X. Li, 2015, MNRAS, 2015, 452, 158

  31. 谢谢大家！

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