Minimisation strategies for the determination of parton density functions
High Energy Physics - Phenomenology
2017-11-29 v1
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.
Keywords
Cite
@article{arxiv.1711.09991,
title = {Minimisation strategies for the determination of parton density functions},
author = {Stefano Carrazza and Nathan P. Hartland},
journal= {arXiv preprint arXiv:1711.09991},
year = {2017}
}
Comments
5 pages, 3 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017)