pelican-lite 1.1.0

Efficient reimplementation of PELICAN (Permutation-Equivariant and Lorentz-Invariant or Covariant Aggregator Network for Particle Physics)

PELICAN-lite

This is an efficient reimplementation of the PELICAN architecture. PELICAN was first published at the ML4PS workshop 2022 and on JHEP. The official implementation is available on https://github.com/abogatskiy/PELICAN.

This implementation aims to improve efficiency and ease of use. For toptagging with batch size 100, we find 8x reduced memory usage and a 3x training speedup compared to the original implementation. PELICAN-lite can be used as the Frames-Net in Lorentz Local Canonicalization (LLoCa).

You can read more about this implementation in the PELICAN-lite documentation.

Installation

You can either install the latest release using pip

pip install pelican-lite

or clone the repository and install the package in dev mode

git clone https://github.com/heidelberg-hepml/pelican-lite.git
cd pelican
pip install -e .
pip install -r requirements.txt
pre-commit install

How to use PELICAN-lite

Please have a look at the PELICAN-lite documentation and our example notebook in examples/demo.ipynb.

Examples

• https://github.com/heidelberg-hepml/lorentz-frames: PELICAN-lite jet taggers and amplitude regressors. A PELICAN tagger based on the official implementation is also included, allowing a fair comparison. Within the LLoCa framework, one can also use PELICAN-lite as the Frames-Net.

Let us know if you use pelican-lite, so we can add your repo to the list!

Citation

If you find this code useful in your research, please cite these papers

@article{Favaro:2025pgz,
   author = "Favaro, Luigi and Gerhartz, Gerrit and Hamprecht, Fred A. and Lippmann, Peter and Pitz, Sebastian and Plehn, Tilman and Qu, Huilin and Spinner, Jonas",
   title = "{Lorentz-Equivariance without Limitations}",
   eprint = "2508.14898",
   archivePrefix = "arXiv",
   primaryClass = "hep-ph",
   month = "8",
   year = "2025"
}
@article{Bogatskiy:2023nnw,
   author = "Bogatskiy, Alexander and Hoffman, Timothy and Miller, David W. and Offermann, Jan T. and Liu, Xiaoyang",
   title = "{Explainable equivariant neural networks for particle physics: PELICAN}",
   eprint = "2307.16506",
   archivePrefix = "arXiv",
   primaryClass = "hep-ph",
   doi = "10.1007/JHEP03(2024)113",
   journal = "JHEP",
   volume = "03",
   pages = "113",
   year = "2024"
}
@article{Bogatskiy:2022czk,
   author = "Bogatskiy, Alexander and Hoffman, Timothy and Miller, David W. and Offermann, Jan T.",
   title = "{PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics}",
   eprint = "2211.00454",
   archivePrefix = "arXiv",
   primaryClass = "hep-ph",
   month = "11",
   year = "2022"
}