Abstract Eclipsing binaries provide one of the most direct mechanisms for measuring stellar properties such as mass and radius, but determining these properties has historically been nontrivial and computationally prohibitive. As such, only a small fraction of all eclipsing binaries for which data have been available have been fully characterized. To improve computational efficiency, we construct an uncertainty-aware neural network which can ingest phase-folded light curves in any of 50 commonly used passbands, combined with phase-folded radial velocity measurements for both primary and secondary, as well as fluxes across the spectral energy distribution to predict stellar and orbital parameters of eclipsing binaries. Our model was trained to be agnostic to the presence of third light, spots (both cool and hot), and incomplete data. Given that the model is operating in a probabilistic framework, it is also capable of outputting uncertainties in all of the parameters. The model was trained on synthetic data and applied to a set of ∼200 previously solved real eclipsing binaries to demonstrate its performance. It is also capable of determining masses and radii of eclipsing binaries with precision of ≲20% and T eff with precision of ∼500 K in only a fraction of the time that it takes the more traditional solvers. Although the resulting uncertainties are larger than what is possible to produce using more boutique analysis of individual stars, in the era of large photometric surveys, this approach allows us to select the most interesting systems and provides a starting point to identify the parameter distributions that these solvers could improve upon.
Kounkel et al. (Wed,) studied this question.