BONNSAI: a Bayesian tool for comparing stars with stellar evolution   models
  Powerful telescopes equipped with multi-fibre or integral field spectrographs combined with detailed models of stellar atmospheres and automated fitting techniques allow for the analysis of large number of stars. These datasets contain a wealth of information that require new analysis techniques to bridge the gap between observations and stellar evolution models. To that end, we develop BONNSAI (BONN Stellar Astrophysics Interface), a Bayesian statistical method, that is capable of comparing all available observables simultaneously to stellar models while taking observed uncertainties and prior knowledge such as initial mass functions and distributions of stellar rotational velocities into account. BONNSAI can be used to (1) determine probability distributions of fundamental stellar parameters such as initial masses and stellar ages from complex datasets, (2) predict stellar parameters that were not yet observationally determined and (3) test stellar models to further advance our understanding of stellar evolution. An important aspect of BONNSAI is that it singles out stars that cannot be reproduced by stellar models through $\chi^{2}$ hypothesis tests and posterior predictive checks. BONNSAI can be used with any set of stellar models and currently supports massive main-sequence single star models of Milky Way and Large and Small Magellanic Cloud composition. We apply our new method to mock stars to demonstrate its functionality and capabilities. In a first application, we use BONNSAI to test the stellar models of Brott et al. (2011a) by comparing the stellar ages inferred for the primary and secondary stars of eclipsing Milky Way binaries. Ages are determined from dynamical masses and radii that are known to better than 3%. We find that the stellar models reproduce the Milky Way binaries well. BONNSAI is available through a web-interface at http://www.astro.uni-bonn.de/stars/bonnsai. 
Fabian R. N. Schneider, Norbert Langer, Alex de Koter, Ines Brott,   Robert G. Izzard, Herbert H.B. Lau
astro-ph.SR astro-ph.IM