Pineline: Industrialization of High-Energy Theory Predictions

TL;DR

Pineline introduces a framework that automates and standardizes the computation of high-energy physics theory predictions, enabling efficient parameter searches, fitting, and reproducibility of complex calculations.

Contribution

It provides a novel, unified toolset for automating and harmonizing diverse high-energy physics predictions, improving efficiency and reproducibility.

Findings

Replaced NNLO QCD K-factors with exact NNLO predictions in a PDF fit.

Demonstrated the framework's utility in parameter searches and fitting.

Enhanced reproducibility of high-energy physics computations.

Abstract

We present a collection of tools automating the efficient computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors in a PDF fit with the exact NNLO predictions.