1 research outputs found
Exoplanet atmosphere evolution: emulation with random forests
Atmospheric mass-loss is known to play a leading role in sculpting the
demographics of small, close-in exoplanets. Understanding the impact of such
mass-loss driven evolution requires modelling large populations of planets to
compare with the observed exoplanet distributions. As the quality of planet
observations increases, so should the accuracy of the models used to understand
them. However, to date, only simple semi-analytic models have been used in such
comparisons since modelling populations of planets with high accuracy demands a
high computational cost. To address this, we turn to machine learning. We
implement random forests trained on atmospheric evolution models, including XUV
photoevaporation, to predict a given planet's final radius and atmospheric
mass. This evolution emulator is found to have an RMS fractional radius error
of 1 from the original models and is times faster to evaluate.
As a test case, we use the emulator to infer the initial properties of
Kepler-36b and c, confirming that their architecture is consistent with
atmospheric mass loss. Our new approach opens the door to highly sophisticated
models of atmospheric evolution being used in demographic analysis, which will
yield further insight into planet formation and evolution.Comment: 5 pages, 3 figures. Submitted to MNRAS letter