2021MNRAS.507.1795R

The parameter space for modelling stellar systems is vast and complicated. To find best-fitting models for a star one needs a statistically robust way of exploring this space. We present a new machine-learning approach to predict the modelling parameters for detached double-lined eclipsing binary systems, including the system age, based on observable quantities. Our method allows for the estimation of the importance of several physical effects which are included in a parametrized form in stellar models, such as convective core overshoot or stellar spot coverage. The method yields probability distribution functions for the predicted parameters which take into account the statistical and, to a certain extent, the systematic errors which is very difficult to do using other methods. We employ two different approaches to investigate the two components of the system either independently or in a combined manner. Furthermore, two different grids are used as training data. We apply the method to 26 selected objects and test the predicted best solutions with an on-the-fly optimization routine which generates full hydrostatic models. While we do encounter failures of the predictions, our method can serve as a rapid estimate for stellar ages of detached eclipsing binaries taking full account of the uncertainties in the observables.