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Stellar characterization of large spectroscopic datasets: A Bayesian approach
Author(s) -
Kaiser A.,
Weiss W.W.
Publication year - 2012
Publication title -
astronomische nachrichten
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 63
eISSN - 1521-3994
pISSN - 0004-6337
DOI - 10.1002/asna.201211819
Subject(s) - bayesian probability , physics , reliability (semiconductor) , computer science , resolution (logic) , characterization (materials science) , stars , scale (ratio) , spectral line , kepler , task (project management) , astrophysics , astronomy , artificial intelligence , optics , power (physics) , quantum mechanics , management , economics
The task of accurately determining the fundamental parameters of a star from medium or low resolution spectra is time consuming and requires substantial experience. Since the first large‐scale space missions such as CoRoT or Kepler came along, the need for a fast and statistically sound method emerged to cope with the large amount of ground based support data. We present here a fully automated approach for the determination of stellar parameters from low‐resolution stellar spectra using a Bayesian approach and discuss the reliability and limits of the method (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)