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| Paper: |
Classification of Spectra of Emission-line Stars Using Feature Extraction Based on Wavelet Transform |
| Volume: |
485, Astronomical Data Analysis Software and Systems XXIII |
| Page: |
177 |
| Authors: |
Bromová P.; Bařina, D.; Škoda, P.; Vážný J.; Zendulka, J. |
| Abstract: |
Our goal is to automatically identify spectra of emission (Be) stars in
large archives and classify their types based on a typical shape of the
Hα emission line. Due to the length of spectra,
of the original data is very time-consuming. In order to
lower computational requirements and enhance the separability of the
classes, we have to find a reduced representation of spectral features,
however conserving most of the original information content. As the Be
stars show a number of different shapes of emission lines, it is not
easy to construct simple criteria (like e.g. Gaussian fits) to
distinguish the emission lines in an automatic manner. We proposed to
perform the wavelet transform of the spectra, calculate statistical
metrics from the wavelet coefficients, and use them as feature vectors
for classification. In this paper, we compare different wavelet
transforms, different wavelets, and different statistical metrics in an
attempt to identify the best method. |
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