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Quantitative Stellar Classification with Low-Resolution Spectroscopy

Published online by Cambridge University Press:  29 April 2014

Matthias Ammler-von Eiff
Affiliation:
Thüringer Landessternwarte Sternwarte 5, 07778 Tautenburg, Germany email: ammler@tls-tautenburg.de, sebastian@tls-tautenburg.de, guenther@tls-tautenburg.de
Daniel Sebastian
Affiliation:
Thüringer Landessternwarte Sternwarte 5, 07778 Tautenburg, Germany email: ammler@tls-tautenburg.de, sebastian@tls-tautenburg.de, guenther@tls-tautenburg.de
Eike W. Guenther
Affiliation:
Thüringer Landessternwarte Sternwarte 5, 07778 Tautenburg, Germany email: ammler@tls-tautenburg.de, sebastian@tls-tautenburg.de, guenther@tls-tautenburg.de
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Abstract

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Low-resolution spectroscopy (R ≈ 1000) is used to efficiently characterize faint stars suspected to host planets. Stellar parameters, i.e. effective temperature, surface gravity, and metallicity can be assessed from these spectra by methods of quantitative classification. For this purpose, more than 130 template stars have been observed with the faint object spectrograph at the Tautenburg 2m telescope, Germany. A large number of lines are measured and the dependence of line depths on stellar parameters is studied.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2014 

References

Bailer-Jones, C. A. L., 2002, in “Automated Data Analysis in Astronomy”, eds. Gupta, Singh & Bailer-Jones, p. 83Google Scholar
Cayrel, R., Perrin, M.-N., Barbuy, B., & Buser, R., 1991a, A&A, 247, 108Google Scholar
Cayrel, R., Perrin, M. N., Buser, R., Barbuy, B., & Coupry, M. F., 1991b, A&A, 247, 122Google Scholar
Döllinger, M., 2008, PhD thesis, University of MunichGoogle Scholar
Gray, D. F. & Johanson, H. L., 1991, PASP, 103, 439Google Scholar
Gray, R. O. & Corbally, J. C., “Stellar Spectral Classification”, 2009, Princeton University PressGoogle Scholar
Fuhrmann, K., 1998, A&A, 338, 161Google Scholar
Fuhrmann, K., 2004, AN, 325, 3Google Scholar
Fuhrmann, K., 2008, MNRAS, 384, 173Google Scholar
Fuhrmann, K., 2011, MNRAS, 414, 2893Google Scholar
Sebastian, D., Guenther, E. W., Schaffenroth, V., Gandolfi, D., Geier, S., Heber, U., Deleuil, M., Moutou, C. 2011, A&A, 541, 34Google Scholar
Malyuto, V., Lazauskaite, R., & Shvelidze, T. 2001, NewA, 6, 381Google Scholar
Singh, H. P., Bailer-Jones, C. A. L., & Gupta, R., 2002, in “Automated Data Analysis in Astronomy”, eds. Gupta, Singh & Bailer-Jones, p. 69Google Scholar
Stock, J. & Stock, M. J., 1999, RMxAA, 35, 143Google Scholar
Wu, Y., Singh, H. P., Prugniel, P., Gupta, R., & Koleva, M. 2011, A&A, 525, 71Google Scholar