SELF-ORGANIZING MAPS AND PARTON DISTRIBUTION FUNCTIONS
| Autor: | Simonetta Liuti, D. Z. Perry, Katherine Holcomb |
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| Rok vydání: | 2011 |
| Předmět: |
Self-organizing map
Physics Artificial neural network Momentum transfer FOS: Physical sciences Experimental data Parton Type (model theory) Deep inelastic scattering High Energy Physics - Phenomenology High Energy Physics - Phenomenology (hep-ph) Distribution function High Energy Physics::Experiment Statistical physics Nuclear Experiment |
| Zdroj: | Exclusive Reactions at High Momentum Transfer IV. |
| DOI: | 10.1142/9789814329569_0008 |
| Popis: | We present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly useful for an anaysis directed at extracting generalized parton distributions from data. We show quantitative results of our initial analysis of the parton distribution functions from inclusive deep inelastic scattering. 8 pages, 4 figures, to appear in the proceedings of "Workshop on Exclusive Reactions at High Momentum Transfer (IV)", Jefferson Lab, May 18th -21st, 2010 |
| Databáze: | OpenAIRE |
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