exoplanet: Gradient-based probabilistic inference for exoplanet data & other astronomical time series

TL;DR

exoplanet is a toolkit built on PyMC3 that enables probabilistic modeling of astronomical time series data, especially for characterizing exoplanets and star systems by integrating various observational datasets.

Contribution

It extends PyMC3's capabilities with custom functions and distributions tailored for exoplanet data analysis, facilitating comprehensive probabilistic inference.

Findings

Supports modeling of diverse astronomical datasets

Enables simultaneous parameter estimation and variability accounting

Facilitates exoplanet and star system characterization

Abstract

"exoplanet" is a toolkit for probabilistic modeling of astronomical time series data, with a focus on observations of exoplanets, using PyMC3 (Salvatier et al., 2016). PyMC3 is a flexible and high-performance model-building language and inference engine that scales well to problems with a large number of parameters. "exoplanet" extends PyMC3's modeling language to support many of the custom functions and probability distributions required when fitting exoplanet datasets or other astronomical time series. While it has been used for other applications, such as the study of stellar variability, the primary purpose of "exoplanet" is the characterization of exoplanets or multiple star systems using time-series photometry, astrometry, and/or radial velocity. In particular, the typical use case would be to use one or more of these datasets to place constraints on the physical and orbital parameters of the system, such as planet mass or orbital period, while simultaneously taking into account the effects of stellar variability.