Cited By
View all- Yi ZChen ZPan JYue LLu YLi JLuo A(2019)An Efficient Spectral Selection of M Giants Using XGBoostThe Astrophysical Journal10.3847/1538-4357/ab54d0887:2(241)Online publication date: 23-Dec-2019
Spectral Recognition of M Giant Based on Deep Convolutional Neural Network
Pages 150 - 153
Abstract
Because of its similarity with the M dwarf spectrum, the M giant spectrum is prone to be mixed in M dwarfs spectra, which leads to the difficulty to select pure M giant or M dwarf samples. Convolutional Neural Network(CNN) is an effective technique to solve this problem. In this study, two CNN models, CNN_1dim and CNN_2dim, were constructed to classify M giants and M dwarfs. The experimental results show that the accuracy of the two models can reach 98.910% and 98.956% respectively, both are better than the performance of Random Forest (RF) and Gradient Boosting Decision Tree (GBDT), while using the same spectral data. This indicates that the deep convolutional neural network has an obvious advantage over the problem of identifying M giants from the M Type spectra.
References
[1]
Cui X Q, Zhao Y H, Chu Y Q, et al. The large sky area multi-object fiber spectroscopic telescope (LAMOST){J}. Research in Astronomy and Astrophysics, 2012, 12(9): 1197.
[2]
Bochanski J J, Willman B, West A A, et al. Hunting the most distant stars in the Milky Way: methods and initial results{J}. The Astronomical Journal, 2014, 147(4): 76.
[3]
Bochanski J J, Willman B, Caldwell N, et al. Hunting the Most Distant Stars in the Milky Way{C}//American Astronomical Society Meeting Abstracts. 2015, 225.
[4]
Zhong J, Lepine S, Li J, et al. The M-giant star candidates identified in the LAMOST data release 1{J}. arXiv preprint arXiv:1505.07932, 2015.
[5]
Mann A W, Gaidos E, Lépine S, et al. THEY MIGHT BE GIANTS: LUMINOSITY CLASS, PLANET OCCURRENCE, AND PLANET-METALLICITY RELATION OF THE COOLEST KEPLER TARGET STARS{J}. The Astrophysical Journal, 2012, 753(1): 90.
[6]
Gulati R K, Gupta R, Gothoskar P, et al. Stellar spectral classification using automated schemes{J}. The Astrophysical Journal, 1994, 426: 340--344.
[7]
Von Hippel T, Storrie-Lombardi L J, Storrie-Lombardi M C, et al. Automated classification of stellar spectra-I. Initial results with artificial neural networks{J}. Monthly Notices of the Royal Astronomical Society, 1994, 269(1): 97--104.
[8]
Fuentes O, Gulati R K. Prediction of stellar atmospheric parameters from spectra, spectral indices and spectral lines using machine learning{C}//Revista Mexicana de Astronomia y Astrofisica Conference Series. 2001, 10: 209--213.
[9]
Yi Z, Luo A, Song Y, et al. M Dwarf catalog of the LAMOST Pilot Survey{J}. The Astronomical Journal, 2014, 147(2): 33.
[10]
LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition{J}. Proceedings of the IEEE, 1998, 86(11): 2278--2324.
[11]
Luo A L, Zhao Y H, Zhao G, et al. The first data release (DR1) of the LAMOST regular survey{J}. Research in Astronomy and Astrophysics, 2015, 15(8): 1095.
[12]
Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets{C}// International Conference on Neural Information Processing Systems. MIT Press, 2014:2672--2680.
[13]
Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks{C}// ICLR. 2016.
[14]
Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks{M}// Computer Vision - ECCV 2014. Springer International Publishing, 2014:818--833.
Index Terms
- Spectral Recognition of M Giant Based on Deep Convolutional Neural Network
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Published In
October 2018
221 pages
ISBN:9781450364768
DOI:10.1145/3291801
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In-Cooperation
- Shandong Univ.: Shandong University
- University of Queensland: University of Queensland
- Dalian Maritime University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 October 2018
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- Research-article
- Research
- Refereed limited
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- Shandong University Research Fund
- the National Natural Science Foundation of China
- Shandong University Young Scholars Future Plans
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ICBDR 2018
ICBDR 2018: 2018 The 2nd International Conference on Big Data Research
October 27 - 29, 2018
Weihai, China
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View all- Yi ZChen ZPan JYue LLu YLi JLuo A(2019)An Efficient Spectral Selection of M Giants Using XGBoostThe Astrophysical Journal10.3847/1538-4357/ab54d0887:2(241)Online publication date: 23-Dec-2019
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