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PreprintCC BY 4.0arXiv

pylhe: A Lightweight Python interface to Les Houches Event files

  • Alexander Puck Neuwirth
  • Matthew Feickert
  • Lukas Heinrich
  • Eduardo Rodrigues
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Abstract

Les Houches Event files are a standard format for Monte Carlo event generators in high-energy physics. pylhe is a lightweight pure-Python library for reading and writing LHE event data. It supports .lhe and compressed .lhe.gz files, implements the widely used LHE 3.0 features including multiple event weights and generator metadata, and also supports the recent HDF5-based LHEH5 format. By exposing events through a pythonic iterator and shared data structures, pylhe enables memory-efficient processing of large event samples without loading entire files into memory. The library also supports format conversion and integration with modern analysis workflows, including columnar analysis with Awkward Array and downstream machine-learning applications.

Versions

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Cite

BibTeXRIS
BibTeX
@misc{neuwirth2026pylhe,
  title = {{pylhe: A Lightweight Python interface to Les Houches Event files}},
  author = {Alexander Puck Neuwirth and Matthew Feickert and Lukas Heinrich and Eduardo Rodrigues},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2607.29352},
  eprint = {2607.29352},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.29352},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - pylhe: A Lightweight Python interface to Les Houches Event files
AU  - Alexander Puck Neuwirth
AU  - Matthew Feickert
AU  - Lukas Heinrich
AU  - Eduardo Rodrigues
PY  - 2026
DA  - 2026/10/06
PB  - arXiv
DO  - 10.48550/arxiv.2607.29352
UR  - https://arxiv.org/abs/2607.29352
AB  - Les Houches Event files are a standard format for Monte Carlo event generators in high-energy physics. pylhe is a lightweight pure-Python library for reading and writing LHE event data. It supports .lhe and compressed .lhe.gz files, implements the widely used LHE 3.0 features including multiple event weights and generator metadata, and also supports the recent HDF5-based LHEH5 format. By exposing events through a pythonic iterator and shared data structures, pylhe enables memory-efficient processing of large event samples without loading entire files into memory. The library also supports format conversion and integration with modern analysis workflows, including columnar analysis with Awkward Array and downstream machine-learning applications.
ER  -