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Automatic stellar spectral classification via sparse representations and dictionary learning

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Abstract

Stellar classification is an important topic in astronomical tasks such as the study of stellar populations. However, stellar classification of a region of the sky is a time-consuming process due to the large amount of objects present in an image. Therefore, automatic techniques to speed up the process are required. In this work, we study the application of a sparse representation and a dictionary learning for automatic spectral stellar classification. Our dataset consist of 529 calibrated stellar spectra of classes B to K, belonging to the Pulkovo Spectrophotometric catalog, in the 3400−5500Å range. These stellar spectra are used for both training and testing of the proposed methodology. The sparse technique is applied by using the greedy algorithm OMP (Orthogonal Matching Pursuit) for finding an approximated solution, and the K-SVD (K-Singular Value Decomposition) for the dictionary learning step. Thus, sparse classification is based on the recognition of the common characteristics of a particular stellar type through the construction of a trained basis. In this work, we propose a classification criterion that evaluates the results of the sparse representation techniques and determines the final classification of the spectra. This methodology demonstrates its ability to achieve levels of classification comparable with automatic methodologies previously reported such as the Maximum Correlation Coefficient (MCC) and Artificial Neural Networks (ANN).

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Acknowledgments

The author H. Peregrina-Barreto wants to thank to the Consejo Nacional de Ciencia y Tecnología (CONACyT, México) for the support through the postdoctoral residency to develop this work. The authors thank CONACyT for the financial support through project CB-2011-01-169755. We thank Saula Tecpanecatl Mani (Plates Laboratory, INAOE, México) for her hard work and invaluable experience, and to Carlos Torres (IPN, México) for his help in data analysis.

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  1. Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Santa María Tonantzintla, San Andrés Cholula, Puebla, C. P. 72840, Mexico

    R. Díaz-Hernández, H. Peregrina-Barreto, L. Altamirano-Robles, J. A. González-Bernal & A. E. Ortiz-Esquivel

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  1. R. Díaz-Hernández
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  2. H. Peregrina-Barreto
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  3. L. Altamirano-Robles
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  4. J. A. González-Bernal
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  5. A. E. Ortiz-Esquivel
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Correspondence to H. Peregrina-Barreto.

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Díaz-Hernández, R., Peregrina-Barreto, H., Altamirano-Robles, L. et al. Automatic stellar spectral classification via sparse representations and dictionary learning. Exp Astron 38, 193–211 (2014). https://doi.org/10.1007/s10686-014-9413-2

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  • DOI: https://doi.org/10.1007/s10686-014-9413-2

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