摘要：A deep understanding of the Milky Way galaxy, its formation and evolution requires observations of huge numbers of stars. Stellar photometry, therefore, provides an economical method to obtain intrinsic stellar parameters. With the addition of distance information – a prospect made real for more than a billion stars with the secondGaiadata release – deriving reliable ages from photometry is a possibility. We have developed a Bayesian method that generates 2D probability maps of a star’s age and metallicity from photometry and parallax using isochrones. Our synthetic tests show that including a near-UV passband enables us to break the degeneracy between a star’s age and metallicity for certain evolutionary stages. It is possible to find well-constrained ages and metallicities for turn-off and sub-giant stars with colours including aUband and a parallax with uncertainty less than ∼20%. Metallicities alone are possible for the main sequence and giant branch. We find good agreement with the literature when we apply our method to theGaiabenchmark stars, particularly for turn-off and young stars. Further tests on the old open cluster NGC 188, however, reveal significant limitations in the stellar isochrones. The ages derived for the cluster stars vary with evolutionary stage, such that turn-off ages disagree with those on the sub-giant branch, and metallicities vary significantly throughout. Furthermore, the parameters vary appreciably depending on which colour combinations are used in the derivation. We identify the causes of these mismatches and show that improvements are needed in the modelling of giant branch stars and in the creation and calibration of synthetic near-UV photometry. Our results warn against applying isochrone fitting indiscriminately. In particular, the uncertainty on the stellar models should be quantitatively taken into account. Further efforts to improve the models will result in significant advancements in our ability to study the Galaxy.
关键词：enGalaxy: formationGalaxy: stellar contentstars: evolutionstars: fundamental parametersHertzsprung–Russell and C–M diagramsmethods: data analysis
