Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-24T17:08:25.794Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantitative Stellar Classification with Low-Resolution Spectroscopy

Published online by Cambridge University Press:  29 April 2014

Matthias Ammler-von Eiff ,
Daniel Sebastian  and
Eike W. Guenther
Matthias Ammler-von Eiff
Affiliation:
Thüringer Landessternwarte Sternwarte 5, 07778 Tautenburg, Germany email: [email protected], [email protected], [email protected]
Daniel Sebastian
Affiliation:
Thüringer Landessternwarte Sternwarte 5, 07778 Tautenburg, Germany email: [email protected], [email protected], [email protected]
Eike W. Guenther
Affiliation:
Thüringer Landessternwarte Sternwarte 5, 07778 Tautenburg, Germany email: [email protected], [email protected], [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Keywords

stars: fundamental parameters (classification)planetary systems
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 8 , Symposium S293: Formation, Detection, and Characterization of Extrasolar Habitable Planets , August 2012 , pp. 375 - 377
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., 2000, www.ing.iac.es/~klaus/pap_100.psGoogle 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