Expanding the Footprint of EXONEST by Code Conversion

EXONEST, a Bayesian-based inference engine, is used for exoplanet detection, characterization, and hypothesis testing by employing multinested sampling. The purpose of this project is to increase the accuracy of model testing and parameter estimation, while decreasing execution time by converting from MATLAB to Python. The transition should increase the adoption of EXONEST within the astronomy community, given Python's open-source platform and its increasing use within the community. A basic linear fit was used to determine if the full conversion could produce the desired results. The test was promising because in a 1024 sample run on synthetic data the execution time of the test decreased from 125 seconds in MATLAB to 7 seconds in the Python implementation, with accuracy of parameter estimation improving. Further transition of EXONEST's libraries, implementation of time-reducing strategies such as multithreading, and testing on both synthetic and real exoplanet light curve data will be explored to further improve EXONEST prior to full release.