My research projects Calibration of stellar-parameter estimation

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Background Apart from precise parallax positions, the astrometic Gaia satellite will also provide optical spectra for numerous objects. These spectra are used for three different purposes: Radial-velocity measurements of stars, which together with positions provides information about the phase space and thus about the gravitational potential of the Milky Way. Classification of stars and non-stars. Estimation of stellar parameters such as effective temperature, metallicity, and surface gravity. What is the problem? The algorithms used for stellar-parameter estimation employ synthetic stellar spectra. While these synthetic spectra are becoming more and more realistic, a certain discrepancy between synthetic and real spectra will always exist. These discrepancies inevitably lead to a misestimation of stellar parameters. This is the so-called "synthetic spectra mismatch problem". What am I doing? My task is to assess how large this misestimation is in practice. Moreover, the question is whether this effect can be calibrated? Essentially, this reduces to "calibrating the unknown". I have developed a simple method is able to calibrate systematic (i.e. non-random) discrepancies between real and synthetic spectra. This method can modify the synthetic spectra "to look like real spectra". The ultimate problem of calibration is to find a suitable set of standard stars whose physical parameters (effective temperature, surface gravity, metallicity, and line-of-sight extinction) are known reliably and cover all relevant parts of the parameter space. For instances, I am using asteroseismic data from NASA's Kepler satellite for creating such a set of standard stars.