Minimisation strategies for the determination of parton density functions
| Autor: | Nathan P. Hartland, Stefano Carrazza |
|---|---|
| Přispěvatelé: | (Astro)-Particles Physics |
| Rok vydání: | 2017 |
| Předmět: |
History
Particle physics Mathematical optimization Artificial neural network 010308 nuclear & particles physics Computer science FOS: Physical sciences hep-ph Parton 01 natural sciences Minimisation (clinical trials) Computer Science Applications Education High Energy Physics - Phenomenology SDG 17 - Partnerships for the Goals High Energy Physics - Phenomenology (hep-ph) 0103 physical sciences Optimisation algorithm CMA-ES 010306 general physics Evolution strategy Particle Physics - Phenomenology |
| Zdroj: | Carrazza, S & Hartland, N P 2018, ' Minimisation strategies for the determination of parton density functions ', Journal of Physics : Conference Series, vol. 1085, no. 5, 052007, pp. 1-5 . https://doi.org/10.1088/1742-6596/1085/5/052007 Journal of Physics : Conference Series, 1085(5):052007, 1-5. IOP Publishing Ltd. Journal of Physics: Conference Series |
| ISSN: | 1742-6588 |
| DOI: | 10.48550/arxiv.1711.09991 |
| Popis: | 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. Comment: 5 pages, 3 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|