Theoretical stellar models predict luminosity and effective temperatures of stars. For photometric observations, however, transformations into the colour-magnitude-diagram is needed. Since there are many photometric systems around (Johnsons, Stromgren, SDSS, HST, ....) a variety of such transformations is needed. This can be done either by connecting existing empirical transformations or by adding simple stellar atmospheres to our models, predict spectra and fold them with photometric folder definitions.
The equation of state is a fundamental ingredient in stellar evolution codes. It is implemented in the form of pre-calculated tables for a number of chemical mixtures. Using the FreeEOS program new tables for a larger variety of chemical mixtures, in particulas for so-called alpha-enhanced composition, plus an update of EoS for higher densities, should be prepared.
Detached eclipsed binary systems are a very important test case for stellar evolution codes and physics. In this thesis a prediction of the necessary physical ingredients, of age and composition of the system components should be made using a new machine learning algorithm. In the second step models based on these parameter estimates should be computed, which include an updated treatment of stellar atmospheres and convective envelopes, which will affect mainly the stellar radius, which is the one quantity measured directly from the system's lightcurve and which is to be matched for both components at identical stellar age.
In many fields of astrophysics, simplified stellar evolution results are used, mostly in the form of analytical fitting formulae. In particular in binary systems with mass exchange between the components it is widely assumed that after mass loss of one object, that star continues its evolution as if it had the new, reduced mass from the very beginning. This is to be tested, and if found to be an insufficiently accurate approximation, an improved treatment is to be created.
Our stellar evolution code has to be optimized to be able to follow reliably and accurately the evolution of massive stars until the very last nuclear burning phases. This requires implementation of a suitable nuclear network, an update of neutrino loss descriptions, work on the treatment of mass loss, and technical work. A good overview over the physics of stars and an inclination towards programming in a higher programming language (Fortran, C, etc) is required.
(This subject can be a master, diploma, or PhD thesis. Depending on definition the programming will be more complete and fewer or more science subjects will be included. For a PhD the structure of main sequence stars in comparison to seismology or the influence of physical assumptions on the structure or core collapse supernova progenitors can be such science issues).