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Octofitter

GitHub Commit Activity License Stars

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](@ref python).

!!! note Octofitter is under active development and is only tested with the latest stable julia release (currently 1.10)

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

@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](@ref install) and then follow our [first tutorial](@ref fit-astrometry).