Exploring nucleon structure with the self-organizing maps algorithm
| Autor: | Katherine Holcomb, Simonetta Liuti, Evan Askanazi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Physics
Nuclear and High Energy Physics Artificial neural network Scattering Self Structure (category theory) FOS: Physical sciences Parton Type (model theory) Nonlinear Sciences - Adaptation and Self-Organizing Systems High Energy Physics - Experiment High Energy Physics - Experiment (hep-ex) High Energy Physics - Phenomenology High Energy Physics - Phenomenology (hep-ph) Distribution function Statistical physics Nucleon Adaptation and Self-Organizing Systems (nlin.AO) |
| Zdroj: | Journal of Physics G: Nuclear and Particle Physics. 42:034030 |
| ISSN: | 1361-6471 0954-3899 |
| DOI: | 10.1088/0954-3899/42/3/034030 |
| Popis: | We discuss the application of an alternative type of neural network, the Self-Organizing Map to extract parton distribution functions from various hard scattering processes. 15 pages, 13 figures. arXiv admin note: substantial text overlap with arXiv:1309.7085 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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