Theory pipeline for PDF fitting

TL;DR

This paper discusses developing an automated, unified framework for integrating diverse datasets and theoretical predictions to improve the efficiency and consistency of parton distribution function (PDF) fitting processes.

Contribution

It introduces a comprehensive pipeline that automates data standardization, theory prediction integration, and evolution calculations for PDF fitting.

Findings

Framework automates data and theory integration

Reduces manual effort in PDF fitting workflows

Enhances consistency across different datasets and predictions

Abstract

Fitting PDFs requires the integration of a broad range of datasets, both from data and theory side, into a unique framework. While for data the integration mainly consists in the standardization of the data format, for the theory predictions there are multiple ingredients involved. Different providers are developed by separate groups for different processes, with a variety of inputs (runcards) and outputs (interpolation grids). Moreover, since processes are measured at different scales, DGLAP evolution has to be provided for the PDF candidate, or precomputed into the grids. We are working towards the automation of all these steps in a unique framework, that will be useful for any PDF fitting groups, and possibly also for phenomenological studies.