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.
Minimisation strategies for the determination of parton density functions / S. Carrazza, N. Hartland. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - 1085:5(2018), pp. 052007.1-052007.5. ((Intervento presentato al 18. convegno International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 tenutosi a Seattle nel 2017 [10.1088/1742-6596/1085/5/052007].
Minimisation strategies for the determination of parton density functions
S. Carrazza;
2018
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.
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Carrazza_2018_J._Phys.__Conf._Ser._1085_052007.pdf
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