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Powerful telescopes equipped with multi-fibre or integral field spectrographscombined with detailed models of stellar atmospheres and automated fittingtechniques allow for the analysis of large number of stars. These datasetscontain a wealth of information that require new analysis techniques to bridgethe gap between observations and stellar evolution models. To that end, wedevelop BONNSAI (BONN Stellar Astrophysics Interface), a Bayesian statisticalmethod, that is capable of comparing all available observables simultaneouslyto stellar models while taking observed uncertainties and prior knowledge suchas initial mass functions and distributions of stellar rotational velocitiesinto account. BONNSAI can be used to (1) determine probability distributions offundamental stellar parameters such as initial masses and stellar ages fromcomplex datasets, (2) predict stellar parameters that were not yetobservationally determined and (3) test stellar models to further advance ourunderstanding of stellar evolution. An important aspect of BONNSAI is that itsingles out stars that cannot be reproduced by stellar models through$\chi^{2}$ hypothesis tests and posterior predictive checks. BONNSAI can beused with any set of stellar models and currently supports massivemain-sequence single star models of Milky Way and Large and Small MagellanicCloud composition. We apply our new method to mock stars to demonstrate itsfunctionality and capabilities. In a first application, we use BONNSAI to testthe stellar models of Brott et al. (2011a) by comparing the stellar agesinferred for the primary and secondary stars of eclipsing Milky Way binaries.Ages are determined from dynamical masses and radii that are known to betterthan 3%. We find that the stellar models reproduce the Milky Way binaries well.BONNSAI is available through a web-interface athttp://www.astro.uni-bonn.de/stars/bonnsai.

BONNSAI: a Bayesian tool for comparing stars with stellar evolution models