| Abstract |
Understanding planet formation requires a large statistical sample. But while we have discovered a large number of exoplanets, the known properties of individual worlds remain sparse. This issue cannot be resolved by more observations alone. Measurable planet properties depend on detection technique, but detection with multiple techniques is frequently impossible. This is especially true when we search for planets on longer orbits. One solution is to impute missing properties based on the current known distribution. In this talk, I will present one method for such an interpolation using a neural network. Neural networks are strong at identifying complex trends in a high number of dimensions, making this technique well suited to mining the maximum information from the exoplanet archive. |
