Minimisation strategies for the determination of parton density functions

Stefano Carrazza, Nathan P. Hartland

Research output: Contribution to JournalArticleAcademicpeer-review


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
Issue number5
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


S. C. is supported by the HICCUP ERC Consolidator grant (614577) and by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement n◦ 740006). N. H. is supported by an European Research Council Starting Grant “PDF4BSM”.

FundersFunder number
Horizon 2020 Framework Programme
Seventh Framework Programme614577, 740006
European Research Council


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