Abstract
The IRAS Low Resolution Spectrometer (LRS) covered the spectral region from 7µm to 23µm, and an Atlas was produced containing 5425 spectra. Most of the spectra were associated with evolved stars, including over 3000 spectra from the dust shells around O-rich stars. When Artificial Intelligence techniques were applied to the dataset, a new classification was derived. A scheme with 77 classes, grouped into 9 metaclasses, resulted, and for those types of spectra which were well represented in the initial dataset (i.e. the evolved stars) a very subtle classification was derived, often using line shapes, relative line strengths, or the presence of additional weak features.
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References
Cheeseman, P., Stutz, J., Self, M., Taylor, W., Goebel, J., Volk, K., and Walker, H.: 1989, NASA R,,,,Printer,rror,,,ar,in,,up,,,,,,ef. Publ 1217,
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IRAS Catalogs and Atlases: Atlas of Low Resolution Spectra: 1986, IRAS Science Team, prepared by F. M. Olnon and E. Raimond. (Astron. Astrophys. Suppl 65, 607)
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© 1994 Springer Science+Business Media Dordrecht
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Walker, H.J. (1994). Using Artificial Intelligence Techniques to Classify IRAS LRS Spectra of Evolved Stars. In: Epchtein, N., Omont, A., Burton, B., Persi, P. (eds) Science with Astronomical Near-Infrared Sky Surveys. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0946-8_28
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DOI: https://doi.org/10.1007/978-94-011-0946-8_28
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-4408-0
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