PoS - Proceedings of Science
Volume 316 - XXVI International Workshop on Deep-Inelastic Scattering and Related Subjects (DIS2018) - WG1: Structure Functions and Parton Densities
PDFS ENSE : ∗ Mapping the sensitivity of hadronic experiments to nucleon structure
B.T. Wang, T.J. Hobbs, S. Doyle, J. Gao, T.J. Hou, P. Nadolsky and F. Olness*
Full text: pdf
Pre-published on: September 20, 2018
Published on: November 23, 2018
Recent high precision experimental data from a variety of hadronic
processes opens new opportunities for determination of the collinear
parton distribution functions (PDFs) of the proton. In fact, the
wealth of information from experiments such as the Large Hadron
Collider (LHC) and others, makes it difficult to quickly assess the
impact on the PDFs, short of performing computationally expensive
global fits. As an alternative, we explore new methods for
quantifying the potential impact of experimental data on the
extraction of proton PDFs. Our approach relies crucially on the
correlation between theory-data residuals and the PDFs themselves, as
well as on a newly defined quantity --- the sensitivity --- which
represents an extension of the correlation and reflects both
PDF-driven and experimental uncertainties. This approach is realized
in a new, publicly available analysis package PDFSense, which operates
with these statistical measures to identify particularly sensitive
experiments, weigh their relative or potential impact on PDFs, and
visualize their detailed distributions in a space of the parton
momentum fraction x and factorization scale mu. This tool offers a
new means of understanding the influence of individual measurements in
existing fits, as well as a predictive device for directing future
fits toward the highest impact data and assumptions.
DOI: https://doi.org/10.22323/1.316.0024
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.