rtarun/exoplanet-ml

Machine learning models and utilities for exoplanet science.

Exoplanet ML

Machine learning models and utilities for exoplanet science.

Code Author

Chris Shallue: @cshallue

Walkthrough

You can jump straight to the AstroNet walkthrough.

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

Directories

astronet/

• 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.

astrowavenet/

• A generative model for light curves.

light_curve/

• 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.

tf_util/

• Shared TensorFlow utilities.

third_party/

• Utilities derived from third party code.

Setup

Required Packages

• TensorFlow (instructions)
• Pandas (instructions)
• NumPy (instructions)
• SciPy (instructions)
• AstroPy (instructions)
• PyDl (instructions)
• Bazel (instructions)
• Abseil Python Common Libraries (instructions)

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/...

Citation

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.

Disclaimer

This is not an official Google product.