Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Pavla Bromov&#225;; Petr &#352;koda; Jaroslav V&#225;&#382;n&#253;

doi:10.1007/s11633-014-0789-2

June 2014

Article Contents

Pavla Bromová, Petr Škoda and Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265-273, 2014. doi: 10.1007/s11633-014-0789-2

Cite as: Pavla Bromová, Petr Škoda and Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265-273, 2014. doi: 10.1007/s11633-014-0789-2

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Faculty of Information Technology, Brno University of Technology, Božetěchova 1/2, 612 66 Brno, Czech Republic;
Astronomical Institute of the Academy of Sciences of the Czech Republic, Fričova 298, 251 65 Ondřejov, Czech Republic

Received: 2013-08-27

Fund Project:

This work was supported by Czech Science Foundation (No.GACR 13-08195S), the project Central Register of Research Intentions CEZ MSM0021630528 Security-oriented Research in Information Technology, the specific research (No. FIT-S-11-2), the project RVO: 67985815, the Technological agency of the Czech Republic (TACR) project V3C (No.TE01020415), and Grant Agency of the Czech Republic-GACR P103/13/08195S.

Abstract

Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline, astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.
- Be star,
- stellar spectrum,
- feature extraction,
- dimension reduction,
- discrete wavelet transform,
- classification,
- support vector machines (SVM),
- clustering

References

[1]	Y. Zhao, I. Raicu, I. Foster. Scientific workflow systems for 21st century, new bottle or new wine? In Proceedings of the 2008 IEEE Congress on Services, IEEE, Washington, USA, pp. 467-471, 2008.
[2]	Y. X. Zhang, H. W. Zheng, Y. H. Zhao. Knowledge discovery in astronomical data. In Proceedings of SPIE Conference, vol. 7019, 2008.
[3]	K. D. Borne. Astroinformatics: a 21st century approach to astronomy. In Proceedings of ASTRO 2010: The Astronomy and Astrophysics Decadal Survey, 2009.
[4]	J. M. Porter, T. Rivinius. Classical be stars. The Publications of the Astronomical Society of the Pacific, vol. 115, no. 812, pp. 1153-1170, 2003.
[5]	J. Zorec, D. Briot. Critical study of the frequency of Be stars taking into account their outstanding characteristics. Astronomy and Astrophysics, vol. 318, pp. 443-460, 1997.
[6]	F. J. Zickgraf. Kinematical structure of the circumstellar environments of galactic B[e]-type stars. Astronomy and Astrophysics, vol. 408, no. 1, pp. 257-285, 2003.
[7]	R. W. Hanuschik, W. Hummel, E. Sutorius, O. Dietle, G. Thimm. Atlas of high-resolution emission and shell lines in Be stars. Line profiles and short-term variability. Astronomy and Astrophysics Supplement Series, vol. 116, pp. 309-358, 1996.
[8]	P. Škoda, J. Vážný. Searching of new emission-line stars using the astroinformatics approach. Astronomical Data Analysis Software and Systems XXI, Astronomical Society of the Pacific Conference Series, vol.461, pp. 573, 2012.
[9]	P. Jahankhani, K. Revett, V. Kodogiannis. Data mining an EEG dataset with an emphasis on dimensionality reduction. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, IEEE, Honolulu, HI, USA, pp. 405-412, 2007.
[10]	M. Murugappan, R. Nagarajan, S. Yaacob. Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. Journal of Medical and Biological Engineering, vol. 31, no. 1, pp. 45-51, 2011.
[11]	T. Li, Q. Li, S. H. Zhu, M. Ogihara. A survey on wavelet applications in data mining. SIGKDD Explorations Newsletter, vol. 4, pp. 49-68, 2002.
[12]	M. Manteiga, D. Ordóñez, C. Dafonte, B. Arcay. ANNs and wavelets: A strategy for Gaia RVS Low S/N Stellar Spectra Parameterization. Publications of the Astronomical Society of the Pacific, vol. 122, no. 891, pp. 608-617, 2010.
[13]	S. Mallat. A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed., San Diego: Academic Press, 2008.
[14]	I. Daubechies. Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, Philadelphia, PA: Society for Industrial and Applied Mathematics, 1994.
[15]	G. Kaiser. A Friendly Guide to Wavelets, Boston: Birkhäuser, 1994.
[16]	Y. Meyer, D. H. Salinger. Wavelets and Operators, Number sv. 1 in Cambridge Studies in Advanced Mathematics, Cambridge: Cambridge University Press, 1995.
[17]	G. Strang, T. Nguyen. Wavelets and Filter Banks, Cambridge: Wellesley-Cambridge Press, 1996.
[18]	Y. G. Liu, X. S. Liang, R. H. Weisberg. Rectification of the bias in the wavelet power spectrum. Journal of Atmospheric and Oceanic Technology, vol. 24, no. 12, pp. 2093-2102, 2007.
[19]	P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.
[20]	T. Li, S. Ma, M. Ogihara. Wavelet methods in data mining. Data Mining and Knowledge Discovery Handbook, O.Maimon, L. Rokach, Eds., New York: Springer, pp. 553-571, 2010.
[21]	A. Cohen, I. Daubechies, J. C. Feauveau. Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, vol. 45, no. 5, pp. 485-560, 1992.
[22]	C. Cortes, V. Vapnik. Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[23]	C. C. Chang, C. J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1-27, Article 27, 2011.
[24]	F. Pedregosa. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

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Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Faculty of Information Technology, Brno University of Technology, Božetěchova 1/2, 612 66 Brno, Czech Republic;

Astronomical Institute of the Academy of Sciences of the Czech Republic, Fričova 298, 251 65 Ondřejov, Czech Republic

Received: 2013-08-27

Fund Project:

Key words:

Abstract: Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline, astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.

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Reference (24)

[1]	Y. Zhao, I. Raicu, I. Foster. Scientific workflow systems for 21st century, new bottle or new wine? In Proceedings of the 2008 IEEE Congress on Services, IEEE, Washington, USA, pp. 467-471, 2008.
[2]	Y. X. Zhang, H. W. Zheng, Y. H. Zhao. Knowledge discovery in astronomical data. In Proceedings of SPIE Conference, vol. 7019, 2008.
[3]	K. D. Borne. Astroinformatics: a 21st century approach to astronomy. In Proceedings of ASTRO 2010: The Astronomy and Astrophysics Decadal Survey, 2009.
[4]	J. M. Porter, T. Rivinius. Classical be stars. The Publications of the Astronomical Society of the Pacific, vol. 115, no. 812, pp. 1153-1170, 2003.
[5]	J. Zorec, D. Briot. Critical study of the frequency of Be stars taking into account their outstanding characteristics. Astronomy and Astrophysics, vol. 318, pp. 443-460, 1997.
[6]	F. J. Zickgraf. Kinematical structure of the circumstellar environments of galactic B[e]-type stars. Astronomy and Astrophysics, vol. 408, no. 1, pp. 257-285, 2003.
[7]	R. W. Hanuschik, W. Hummel, E. Sutorius, O. Dietle, G. Thimm. Atlas of high-resolution emission and shell lines in Be stars. Line profiles and short-term variability. Astronomy and Astrophysics Supplement Series, vol. 116, pp. 309-358, 1996.
[8]	P. Škoda, J. Vážný. Searching of new emission-line stars using the astroinformatics approach. Astronomical Data Analysis Software and Systems XXI, Astronomical Society of the Pacific Conference Series, vol.461, pp. 573, 2012.
[9]	P. Jahankhani, K. Revett, V. Kodogiannis. Data mining an EEG dataset with an emphasis on dimensionality reduction. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, IEEE, Honolulu, HI, USA, pp. 405-412, 2007.
[10]	M. Murugappan, R. Nagarajan, S. Yaacob. Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. Journal of Medical and Biological Engineering, vol. 31, no. 1, pp. 45-51, 2011.
[11]	T. Li, Q. Li, S. H. Zhu, M. Ogihara. A survey on wavelet applications in data mining. SIGKDD Explorations Newsletter, vol. 4, pp. 49-68, 2002.
[12]	M. Manteiga, D. Ordóñez, C. Dafonte, B. Arcay. ANNs and wavelets: A strategy for Gaia RVS Low S/N Stellar Spectra Parameterization. Publications of the Astronomical Society of the Pacific, vol. 122, no. 891, pp. 608-617, 2010.
[13]	S. Mallat. A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed., San Diego: Academic Press, 2008.
[14]	I. Daubechies. Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, Philadelphia, PA: Society for Industrial and Applied Mathematics, 1994.
[15]	G. Kaiser. A Friendly Guide to Wavelets, Boston: Birkhäuser, 1994.
[16]	Y. Meyer, D. H. Salinger. Wavelets and Operators, Number sv. 1 in Cambridge Studies in Advanced Mathematics, Cambridge: Cambridge University Press, 1995.
[17]	G. Strang, T. Nguyen. Wavelets and Filter Banks, Cambridge: Wellesley-Cambridge Press, 1996.
[18]	Y. G. Liu, X. S. Liang, R. H. Weisberg. Rectification of the bias in the wavelet power spectrum. Journal of Atmospheric and Oceanic Technology, vol. 24, no. 12, pp. 2093-2102, 2007.
[19]	P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.
[20]	T. Li, S. Ma, M. Ogihara. Wavelet methods in data mining. Data Mining and Knowledge Discovery Handbook, O.Maimon, L. Rokach, Eds., New York: Springer, pp. 553-571, 2010.
[21]	A. Cohen, I. Daubechies, J. C. Feauveau. Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, vol. 45, no. 5, pp. 485-560, 1992.
[22]	C. Cortes, V. Vapnik. Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[23]	C. C. Chang, C. J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1-27, Article 27, 2011.
[24]	F. Pedregosa. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

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通讯作者: 陈斌, bchen63@163.com

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