EXONEST is a Bayesian based inference software package that allows for plug-and-play characterization and model testing of exoplanets given photometric data. Currently, the code is available in Matlab, but work is ongoing to convert it to Python to make use of PyMultinest, which has greater functionality than the Matlab version of Multinest.
Currently, EXONEST supports transits and eclipses, reflected light for Lambertian reflectors, thermal light using either a dayside/nightside model, or a model that treats the exoplanet as if its temperature is distributed as concentric spherical segments, boosted light, and ellipsoidal variations.
Efforts are being made to improve the reflected light model by accurately accounting for the illumination created by spherical stars. In addition, a thermal model that treats the exoplanet as if its temperature is distributed like the sections of a beach ball is being incorporated.
People involved with this project
- Jenn Carter – Developer
- Michelle Arrigo – Exoplanet Thermal Light updates
- Jocelyn McMahon – Exoplanet Thermal Light updates
- John Morris – Python conversion and efficiency upgrades
- Amanda McLaughlin – Creating user interface to acquire and prepare exoplanet data for use with EXONEST.
Links to papers, posters, and presentations related to this project
- Kevin H. Knuth, Ben Placek, Daniel Angerhausen, Jennifer L. Carter, Bryan D’Angelo, Anthony D. Gai, and Bertrand Carado. EXONEST: The Bayesian Exoplanetary Explorer. Entropy. 19(10), 2017
- John Morris. Expanding the Footprint of EXONEST by Code Conversion. 2021 SVUR Symposium. 2021
- Michelle Arrigo. Developing Computational Models for Exoplanet Visibility and Luminosity in MATLAB. 2021 SVUR Symposium. 2021