• Open Access

Mapping the sensitivity of hadronic experiments to nucleon structure

Bo-Ting Wang, T. J. Hobbs, Sean Doyle, Jun Gao, Tie-Jiun Hou, Pavel M. Nadolsky, and Fredrick I. Olness
Phys. Rev. D 98, 094030 – Published 28 November 2018
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Abstract

Determinations of the proton’s collinear parton distribution functions (PDFs) are emerging with growing precision due to increased experimental activity at facilities like the Large Hadron Collider. While this copious information is valuable, the speed at which it is released makes it difficult to quickly assess its 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 Hessian 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 μ. 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. Along the way, many new physics insights can be gained or reinforced. As one of many examples, PDFSense is employed to rank the projected impact of new LHC measurements in jet, vector boson, and tt¯ production and leads us to the conclusion that inclusive jet production at the LHC has a potential for playing an indispensable role in future PDF fits. These conclusions are independently verified by preliminarily fitting this experimental information and investigating the constraints they supply using the Lagrange multiplier technique.

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  • Received 10 July 2018
  • Revised 16 October 2018

DOI:https://doi.org/10.1103/PhysRevD.98.094030

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Particles & Fields

Authors & Affiliations

Bo-Ting Wang1,*, T. J. Hobbs1,4,†, Sean Doyle1, Jun Gao2, Tie-Jiun Hou3, Pavel M. Nadolsky1,‡, and Fredrick I. Olness1

  • 1Department of Physics, Southern Methodist University, Dallas, Texas 75275-0181, USA
  • 2INPAC, Shanghai Key Laboratory for Particle Physics and Cosmology, School of Physics and Astronomy, Shanghai Jiao-Tong University, Shanghai 200240, China
  • 3School of Physics Science and Technology, Xinjiang University, Urumqi, Xinjiang 830046 China
  • 4Jefferson Lab, EIC Center, Newport News, ‎Virginia 23606, USA

  • *botingw@mail.smu.edu
  • tjhobbs@smu.edu
  • nadolsky@physics.smu.edu

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Issue

Vol. 98, Iss. 9 — 1 November 2018

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