Work

Codebase release 2.1 for petrifyML

Year: 2026

Type: article

Abstract: 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.

Source: SciPost Physics Codebases

DOI: 10.21468/scipostphyscodeb.78-r2.1

Authors: Andy Buckley, Louie Corpe, Martin Habedank, Tomasz Procter

Institutions: University of Glasgow, Université Clermont Auvergne, Laboratoire de Physique de Clermont Auvergne, Jagiellonian University

Language: English

Topic: Scientific Computing and Data Management

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

Open Access status: diamond

Funders: UK Research and Innovation, Infrastruktura PL-Grid

Awards: G174534775, G7748757768