ExoPlex: a new Python library for detailed modeling of rocky exoplanet internal structure and mineralogy

The pace of exoplanet discoveries has rapidly accelerated in the past few decades and the number of planets with measured mass and radius is expected to pick up in the coming years. Many more planets with a size similar to earth are expected to be found. Currently, software for characterizing rocky planet interiors is lacking. There is no doubt that a planet’s interior plays a key role in determining surface conditions including atmosphere composition and land area. Comparing data with diagrams of mass vs. radius for terrestrial planets provides only a first-order estimate of the internal structure and composition of planets [e.g. Seager et al 2007]. This thesis will present a new Python library, ExoPlex, which has routines to create a forward model of rocky exoplanets between 0.1 and 5 Earth masses. The ExoPlex code offers users the ability to model planets of arbitrary composition of Fe-Si-Mg-Al-Ca-O in addition to a water layer. This is achieved by modeling rocky planets after the earth and other known terrestrial planets. The three distinct layers which make up the Earth's internal structure are: core, mantle, and water. Terrestrial planet cores will be dominated by iron however, like earth, there may be some quantity of light element inclusion which can serve to enhance expected core volumes. In ExoPlex, these light element inclusions are S-Si-O and are included as iron-alloys. Mantles will have a more diverse mineralogy than planet cores. Unlike most other rocky planet models, ExoPlex remains unbiased in its treatment of the mantle in terms of composition. Si-Mg-Al-Ca oxide components are combined by predicting the mantle mineralogy using a Gibbs free energy minimization software package called Perple\_X [Connolly 2009]. By allowing an arbitrary composition, ExoPlex can uniquely model planets using their host star’s composition as an indicator of planet composition. This is a proven technique [Dorn et al 2015] which has not yet been widely utilized, possibly due to the lack of availability of easy to use software. I present a model sensitivity analysis to indicate the most important parameters to constrain in future observing missions. ExoPlex is currently available on PyPI so it may be installed using pip or conda on Mac OS or Linux based operating systems. It requires a specific scripting environment which is explained in the documentation currently stored on the ExoPlex GitHub page.