https://github.com/bmorris3/exoplanet

Fast & scalable MCMC for all your exoplanet needs!

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
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.zenodo.json file
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DOI references
✓
Academic publication links
Links to: arxiv.org, zenodo.org
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Academic email domains
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (11.5%) to scientific vocabulary

Last synced: 10 months ago · JSON representation

Repository

Fast & scalable MCMC for all your exoplanet needs!

Basic Info

Host: GitHub
Owner: bmorris3
License: mit
Language: Python
Default Branch: master
Homepage: https://exoplanet.dfm.io
Size: 213 MB

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Fork of exoplanet-dev/exoplanet

Created almost 7 years ago · Last pushed almost 7 years ago

https://github.com/bmorris3/exoplanet/blob/master/

exoplanet
=========
































*exoplanet* is a toolkit for probabilistic modeling of transit and/or
radial velocity observations of
[exoplanets](https://en.wikipedia.org/wiki/Exoplanet) and other
astronomical time series using [PyMC3](https://docs.pymc.io). *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 language to support many of the custom
functions and distributions required when fitting exoplanet datasets.
These features include:

- A fast and robust solver for Kepler's equation.
- Scalable Gaussian Processes using
  [celerite](https://celerite.readthedocs.io).
- Fast and accurate limb darkened light curves using
  [starry](https://rodluger.github.io/starry).
- Common reparameterizations for [limb darkening
  parameters](https://arxiv.org/abs/1308.0009), and [planet radius and
  impact parameter](https://arxiv.org/abs/1811.04859).
- And many others!

All of these functions and distributions include methods for efficiently
calculating their *gradients* so that they can be used with
gradient-based inference methods like [Hamiltonian Monte
Carlo](https://arxiv.org/abs/1206.1901), [No U-Turns
Sampling](https://arxiv.org/abs/1111.4246), and [variational
inference](https://arxiv.org/abs/1603.00788). These methods tend to be
more robust than the methods more commonly used in astronomy (like
[ensemble samplers](https://emcee.readthedocs.io) and [nested
sampling](https://ccpforge.cse.rl.ac.uk/gf/project/multinest/))
especially when the model has more than a few parameters. For many
exoplanet applications, *exoplanet* (the code) can improve the typical
performance by orders of magnitude.

*exoplanet* is being actively developed in [a public repository on
GitHub](https://github.com/dfm/exoplanet) so if you have any trouble,
[open an issue](https://github.com/dfm/exoplanet/issues) there.