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Machine learning models and utilities for exoplanet science.
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Exoplanet ML

Machine learning models and utilities for exoplanet science.

Code Author

Chris Shallue: @cshallue


You can jump straight to the AstroNet walkthrough.

Otherwise, click through to the desired directory as outlined below.



  • A neural network for identifying exoplanets in light curves. Contains code for:
    • Downloading and preprocessing Kepler light curves.
    • Building different types of neural network classification models.
    • Training and evaluating a new model.
    • Using a trained model to generate new predictions.


  • A generative model for light curves.


  • Utilities for operating on light curves. These include:
    • Reading Kepler data from .fits files.
    • Applying a median filter to smooth and normalize a light curve.
    • Phase folding, splitting, removing periodic events, etc.
  • light_curve/fast_ops/ contains optimized C++ light curve operations.


  • Shared TensorFlow utilities.


  • Utilities derived from third party code.


Required Packages

Run Unit Tests

Verify that all dependencies are satisfied by running the unit tests:

cd exoplanet-ml  # Bazel must run from a directory with a WORKSPACE file
bazel test astronet/... astrowavenet/... light_curve/... tf_util/... third_party/...


If you find this code useful, please cite our paper:

Shallue, C. J., & Vanderburg, A. (2018). Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90. The Astronomical Journal, 155(2), 94.

Full text available at The Astronomical Journal.


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