pelican-hep 1.0.1

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

Efficient PELICAN

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, we find 8x reduced memory usage for batch size 100, and 2x training speedup for batch size 1 compared to the original implementation. PELICAN can be used as the Frames-Net in Lorentz Local Canonicalization (LLoCa).

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

Installation

You can either install the latest release using pip

pip install pelican-hep

or clone the repository and install the package in dev mode

git clone https://github.com/heidelberg-hepml/pelican.git
cd pelican
pip install -e .

How to use PELICAN

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

Examples

• https://github.com/heidelberg-hepml/lorentz-frames: PELICAN 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 as the Frames-Net.

Let us know if you use pelican, 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"
}