Minimisation strategies for the determination of parton density functions

Stefano Carrazza, Nathan P. Hartland

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

We discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks. We present a short overview of a proton PDF determination obtained using the covariance matrix adaptation evolution strategy (CMA-ES) optimisation algorithm. We perform comparisons between the CMA-ES and the standard nodal genetic algorithm (NGA) adopted by the NNPDF collaboration.

Original languageEnglish
Article number052007
Pages (from-to)1-5
Number of pages5
JournalJournal of Physics: Conference Series
Volume1085
Issue number5
DOIs
Publication statusPublished - 18 Oct 2018
Event18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 - Seattle, United States
Duration: 21 Aug 201725 Aug 2017

Fingerprint

Dive into the research topics of 'Minimisation strategies for the determination of parton density functions'. Together they form a unique fingerprint.

Cite this