English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Modern analysis on parton distribution functions (PDFs) requires calculationsof the log-likelihood functions from thousands of experimental data points, andscans of multi-dimensional parameter space with tens of degrees of freedom. Inconventional analysis the Hessian approximation has been widely used for theestimation of the PDF uncertainties.The Lagrange Multiplier (LM) scan whilebeing a more faithful method is less used due to computational limitations, andis the main focus of this study. We propose to use Neural Networks (NNs) andmachine learning techniques to model the profile of the log-likelihoodfunctions or cross sections for multi-dimensional parameter space in order toovercome those limitations which work beyond the quadratic approximations andmeanwhile ensures efficient scans of the full parameter space. We demonstratethe efficiency of the new approach in the framework of the CT18 global analysisof PDFs by constructing NNs for various target functions, and performing LMscans on PDFs and cross sections at hadron colliders. We further study theimpact of the NOMAD dimuon data on constraining PDFs with the new approach, andfind enhanced strange-quark distributions and reduced PDF uncertainties.Moreover, we show how the approach can be used to constrain new physics beyondthe Standard Model (BSM) by a joint fit of both PDFs and Wilson coefficients ofoperators in the SM effective field theory.

Machine learning of log-likelihood functions in global analysis of parton distributions