Cargando…
The neural network approach to parton fitting
We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parto...
| Autores principales: | , , , , |
|---|---|
| Lenguaje: | eng |
| Publicado: |
2005
|
| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1063/1.2122059 http://cds.cern.ch/record/834983 |
| _version_ | 1780905952448348160 |
|---|---|
| author | Rojo, Joan Del Debbio, Luigi Forte, Stefano Latorre, Jose I. Piccione, Andrea |
| author_facet | Rojo, Joan Del Debbio, Luigi Forte, Stefano Latorre, Jose I. Piccione, Andrea |
| author_sort | Rojo, Joan |
| collection | CERN |
| description | We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits. |
| id | cern-834983 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2005 |
| record_format | invenio |
| spelling | cern-8349832022-08-13T02:17:13Zdoi:10.1063/1.2122059http://cds.cern.ch/record/834983engRojo, JoanDel Debbio, LuigiForte, StefanoLatorre, Jose I.Piccione, AndreaThe neural network approach to parton fittingParticle Physics - PhenomenologyWe introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep‐inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.hep-ph/0505044oai:cds.cern.ch:8349832005-05-06 |
| spellingShingle | Particle Physics - Phenomenology Rojo, Joan Del Debbio, Luigi Forte, Stefano Latorre, Jose I. Piccione, Andrea The neural network approach to parton fitting |
| title | The neural network approach to parton fitting |
| title_full | The neural network approach to parton fitting |
| title_fullStr | The neural network approach to parton fitting |
| title_full_unstemmed | The neural network approach to parton fitting |
| title_short | The neural network approach to parton fitting |
| title_sort | neural network approach to parton fitting |
| topic | Particle Physics - Phenomenology |
| url | https://dx.doi.org/10.1063/1.2122059 http://cds.cern.ch/record/834983 |
| work_keys_str_mv | AT rojojoan theneuralnetworkapproachtopartonfitting AT deldebbioluigi theneuralnetworkapproachtopartonfitting AT fortestefano theneuralnetworkapproachtopartonfitting AT latorrejosei theneuralnetworkapproachtopartonfitting AT piccioneandrea theneuralnetworkapproachtopartonfitting AT rojojoan neuralnetworkapproachtopartonfitting AT deldebbioluigi neuralnetworkapproachtopartonfitting AT fortestefano neuralnetworkapproachtopartonfitting AT latorrejosei neuralnetworkapproachtopartonfitting AT piccioneandrea neuralnetworkapproachtopartonfitting |