An open-source machine learning framework for global analyses of parton distributions

TL;DR

This paper introduces an open-source software framework for the global analysis of parton distribution functions, facilitating reproducibility, customization, and broader phenomenological applications in high-energy physics.

Contribution

It provides a comprehensive, publicly available framework for PDF fitting, data handling, and analysis, enhancing reproducibility and enabling user-defined analyses.

Findings

Framework ensures reproducibility of NNPDF4.0 PDFs

Enables custom phenomenological analyses

Supports efficient comparison of data and theory

Abstract

We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code base is composed by a PDF fitting package, tools to handle experimental data and to efficiently compare it to theoretical predictions, and a versatile analysis framework. In addition to ensuring the reproducibility of the NNPDF4.0 (and subsequent) determination, the public release of the NNPDF fitting framework enables a number of phenomenological applications and the production of PDF fits under user-defined data and theory assumptions.