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

Daniel Foreman-Mackey, Rodrigo Luger, Eric Agol, Thomas Barclay, Luke Bouma, Timothy Brandt, Ian Czekala, Trevor David, Jiayin Dong, Emily Gilbert, Tyler Gordon, Christina Hedges, Daniel Hey, Brett Morris, Adrian Price-Whelan, Arjun Savel

Research output: Contribution to journalArticlepeer-review


"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.
Original languageEnglish
Article number3285
Number of pages7
JournalJournal of Open Source Software
Issue number62
Publication statusPublished - 22 Jun 2021


  • Python
  • Astronomy


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