Data di Pubblicazione:
2023
Citazione:
Pineline: Industrialization of High-Energy Theory Predictions / A. Barontini, A. Candido, J.M. Cruz-Martinez, F. Hekhorn, C. Schwan. - In: COMPUTER PHYSICS COMMUNICATIONS. - ISSN 0010-4655. - 297:(2023 Feb 23), pp. 109061.1-109061.8. [10.1016/j.cpc.2023.109061]
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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Grids; Parton distributions; Reproducibility
Elenco autori:
A. Barontini, A. Candido, J.M. Cruz-Martinez, F. Hekhorn, C. Schwan
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