2016MNRAS.455.4399K

The Kepler survey has provided a wealth of astrophysical knowledge by continuously monitoring over 150 000 stars. The resulting data base contains thousands of examples of known variability types and at least as many that cannot be classified yet. In order to reveal the knowledge hidden in the data base, we introduce a new visualization method that allows us to inspect regularly sampled time series in an explorative fashion. To that end, we propose dimensionality reduction on the parameters of a model capable of representing time series as fixed-length vector representation. We show that a more refined objective function can be chosen by minimizing the reconstruction error, that is the deviation between prediction and observation, of the observed time series instead of reconstructing model parameters. The proposed visualization exhibits a strong correlation between the variability behaviour of the light curves and their physical properties. As a consequence, temperature and surface gravity can, for some stars, be directly inferred from non- (or quasi-) periodic light curves.