Abstract
In this paper we report on a joint research project between astronomers and philosophers of science. The philosophical and the astronomical goal are described and the astronomical background is shortly reviewed. We present the current status of our development of methods for tackling the relevant classification problems, i.e.: (1) application of Bayes’ decision rule for “simple” classification of all spectra in the data base; (2) minimum cost rule classification for compilation of complete samples of rare stellar objects and (3) Bayes classification with application of an atypicality index reject criterion for the detection of non-stellar spectra. We report on the discovery of an extremely metal poor halo star by application of method (2) to a small fraction of our data. A method for adequate handling of low signal-to-noise ratio spectra is presented. The classification methods presented are currently applied to a large data base of digital spectra.
Keywords
- Multivariate Normal Distribution
- Learning Sample
- Joint Research Project
- Metal Poor Star
- Digital Spectrum
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
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WISOTZKI, L., et al. (1996), The Hamburg/ESO survey for bright QSOs. I. Survey design and candidate selection procedure. Astronomy & Astrophysics Supplement Series, 115, 227–233.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Christlieb, N., Graßhoff, G., Nelke, A., Schlemminger, A., Wisotzki, L. (1998). Automatic Spectral Classification. In: Balderjahn, I., Mathar, R., Schader, M. (eds) Classification, Data Analysis, and Data Highways. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72087-1_2
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DOI: https://doi.org/10.1007/978-3-642-72087-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63909-1
Online ISBN: 978-3-642-72087-1
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