Octofitter

Octofitter is a Julia package for performing Bayesian inference against a wide variety of exoplanet / binary star data. You can also use Octofitter from Python using the Python guide.

Supported data:

• Fit exoplanet orbits to relative astrometry
• Fit radial velocity data
• Model stellar activity with Gaussian processes
• Model stellar astrometric accerlation (Gaia-Hipparcos proper motion anomaly)
• "De-orbiting": combine a sequence of images with orbital motion to detect planets
• Sample directly from images or interferometric visibilities
• Experimental support for transit data based on Transits.jl

You can freely combine any of the above data types. Any and all combinations work together.

Modelling features:

• multiple planets (one or more)
• hyperbolic orbits
• co-planar, and non-coplanar systems
• arbitrary priors and parameterizations
• optional O'Neil "observable based priors"
• link mass to photometry via atmosphere models
• hierarchical models (with a bit of work from the user)

Speed:

Fit astrometry on your laptop in seconds!

• Highly optimized code and derivatives are generated from your model
• Higher order sampler (No U-Turn sampler) which explores the parameter space very efficiently
• The sampler is automatically warmed up using a variational approximation from the Pathfinder algorithm (Pathfinder.jl)

Multi-body physics is not currently supported. A Pull-request to PlanetOrbits.jl implementing this functionality would be welcome.

See also: the python libraries Orbitize!, orvara, and exoplanet.

Read the paper

In addition to these documentation and tutorial pages, you can read the paper published in the Astronomical Journal (open-access).

Attribution

• If you use Octofitter in your work, please cite Thompson et al:

@article{Thompson_2023,
doi = {10.3847/1538-3881/acf5cc},
url = {https://dx.doi.org/10.3847/1538-3881/acf5cc},
year = {2023},
month = {sep},
publisher = {The American Astronomical Society},
volume = {166},
number = {4},
pages = {164},
author = {William Thompson and Jensen Lawrence and Dori Blakely and Christian Marois and Jason Wang and Mosé Giordano and Timothy Brandt and Doug Johnstone and Jean-Baptiste Ruffio and S. Mark Ammons and Katie A. Crotts and Clarissa R. Do Ó and Eileen C. Gonzales and Malena Rice},
title = {Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets},
journal = {The Astronomical Journal},
}

• If you use Gaia parallaxes in your work, please cite Gaia DR3 Gaia Collaboration et al. 2023
• Please cite the HMC sampler backend if you use octofit: Xu et al 2020
• Please cite the Pigeons paper if you use octofit_pigeons.
• If you use Hipparcos-GAIA proper motion anomaly, please cite Brandt 2021
• If you use example data in one of the tutorials, please cite the sources listed
• If you use one of the included functions for automatically retreiving data from a public dataset, eg HARPS RVBank, please cite the source as appropriate (it will be displyed in the terminal)
• If you adopt the O'Neil et al. 2019 observable based priors, please cite O'Neil et al. 2019.
• If you use RV phase folded plot, please consider citing Makie.jl Danisch & Krumbiegel, (2021).
• If you use TemporalGPs.jl to accelerate Gaussian processes modelling of stellar activity, please consider citing Tebbutt et al 2021
• If you use the pairplot/cornerplot functionality, please cite:

@misc{Thompson2023,
  author = {William Thompson},
  title = {{PairPlots.jl} Beautiful and flexible visualizations of high dimensional data},
  year = {2023},
  howpublished = {\url{https://sefffal.github.io/PairPlots.jl/dev}},
}

Ready?

Ready to get started? Follow our installation guide and then follow our first tutorial.