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gallifrey

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Introduction

gallifrey is a package for structure discovery in time series data using Gaussian Processes with explicit applications to exoplanet transit lightcurves. It is a JAX-based python implementation of the julia package AutoGP.jl by Feras Saad.

Installation

Option 1: Installation using pip (Recommended)

pip install gallifrey

Option 2: Installation from Source

  1. Clone the repository:

    git clone git@github.com:ChrisBoettner/gallifrey.git
cd gallifrey

  2. Install the package:

    pip install .
    (or, for development mode: pip install -e .)

Citation

If you use gallifrey in your research, please cite it as:

@article{https://doi.org/10.1051/0004-6361/202554518,
  doi = {10.1051/0004-6361/202554518},
  author = {Boettner, Christopher},
  title = {gallifrey: JAX-based Gaussian Process Structure Learning for Astronomical Time Series},
  year = {2025},
  journal = {A\&A},
  publisher = {EDP Sciences},
  issn = {0004-6361, 1432-0746},
  eprint = {2505.20394},
  archiveprefix = {arXiv},
  primaryclass = {astro-ph},
  keywords = {Astrophysics - Earth and Planetary Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics},
  copyright = {{\copyright} 2025, ESO},
}

And please also cite the original paper by Saad et al.

@article{https://doi.org/10.48550/arxiv.2307.09607,
  doi = {10.48550/ARXIV.2307.09607},
  url = {https://arxiv.org/abs/2307.09607},
  author = {Saad,  Feras A. and Patton,  Brian J. and Hoffman,  Matthew D. and Saurous,  Rif A. and Mansinghka,  Vikash K.},
  keywords = {Machine Learning (cs.LG),  Artificial Intelligence (cs.AI),  Methodology (stat.ME),  Machine Learning (stat.ML),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {Sequential Monte Carlo Learning for Time Series Structure Discovery},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}

Acknowledgements

This package is a direct re-implementation of AutoGP.jl and would not be possible without it. The Gaussian Procress implementation is strongly inspired by the fantastic packages GPJax and tinygp.