arXiv Open Access 2025

Enabling stable preservation of ML algorithms in high-energy physics with petrifyML

Andy Buckley Louie Corpe Martin Habedank Tomasz Procter

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Machine learning (ML) in high-energy physics (HEP) has moved in the LHC era from an internal detail of experiment software, to an unavoidable public component of many physics data-analyses. Scientific reproducibility thus requires that it be possible to accurately and stably preserve the behaviours of these, sometimes very complex algorithms. We present and document the petrifyML package, which provides missing mechanisms to convert configurations from commonly used HEP ML tools to either the industry-standard ONNX format or to native Python or C++ code, enabling future re-use and re-interpretation of many ML-based experimental studies.

Topik & Kata Kunci

hep-ph hep-ex

Penulis (4)

Andy Buckley

Louie Corpe

Martin Habedank

Tomasz Procter

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APA MLA BibTeX

Buckley, A., Corpe, L., Habedank, M., Procter, T. (2025). Enabling stable preservation of ML algorithms in high-energy physics with petrifyML. https://arxiv.org/abs/2509.11830

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Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓