Progress in the neural network determination of polarized parton distributions
We review recent progress towards a determination of a set of polarized parton distributions from a global set of deep-inelastic scattering data based on the NNPDF methodology, in analogy with the unpolarized case. This method is designed to provide a faithful and statistically sound representation...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2010
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| _version_ | 1797083672352915456 |
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| author | Rojo, J Forte, S Ridolfi, G Ball, R Del Debbio, L Ubiali, M Bertone, V Guffanti, A Cerutti, F Latorre, J |
| author_facet | Rojo, J Forte, S Ridolfi, G Ball, R Del Debbio, L Ubiali, M Bertone, V Guffanti, A Cerutti, F Latorre, J |
| author_sort | Rojo, J |
| collection | OXFORD |
| description | We review recent progress towards a determination of a set of polarized parton distributions from a global set of deep-inelastic scattering data based on the NNPDF methodology, in analogy with the unpolarized case. This method is designed to provide a faithful and statistically sound representation of parton distributions and their uncertainties. We show how the FastKernel method provides a fast and accurate method for solving the polarized DGLAP equations. We discuss the polarized PDF parametrizations and the physical constraints which can be imposed. Preliminary results suggest that the uncertainty on polarized PDFs, most notably the gluon, has been underestimated in previous studies. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence. |
| first_indexed | 2024-03-07T01:44:45Z |
| format | Journal article |
| id | oxford-uuid:980887ed-3ced-406f-a3be-7b5cf1694a44 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:44:45Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:980887ed-3ced-406f-a3be-7b5cf1694a442022-03-27T00:04:10ZProgress in the neural network determination of polarized parton distributionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:980887ed-3ced-406f-a3be-7b5cf1694a44EnglishSymplectic Elements at Oxford2010Rojo, JForte, SRidolfi, GBall, RDel Debbio, LUbiali, MBertone, VGuffanti, ACerutti, FLatorre, JWe review recent progress towards a determination of a set of polarized parton distributions from a global set of deep-inelastic scattering data based on the NNPDF methodology, in analogy with the unpolarized case. This method is designed to provide a faithful and statistically sound representation of parton distributions and their uncertainties. We show how the FastKernel method provides a fast and accurate method for solving the polarized DGLAP equations. We discuss the polarized PDF parametrizations and the physical constraints which can be imposed. Preliminary results suggest that the uncertainty on polarized PDFs, most notably the gluon, has been underestimated in previous studies. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence. |
| spellingShingle | Rojo, J Forte, S Ridolfi, G Ball, R Del Debbio, L Ubiali, M Bertone, V Guffanti, A Cerutti, F Latorre, J Progress in the neural network determination of polarized parton distributions |
| title | Progress in the neural network determination of polarized parton distributions |
| title_full | Progress in the neural network determination of polarized parton distributions |
| title_fullStr | Progress in the neural network determination of polarized parton distributions |
| title_full_unstemmed | Progress in the neural network determination of polarized parton distributions |
| title_short | Progress in the neural network determination of polarized parton distributions |
| title_sort | progress in the neural network determination of polarized parton distributions |
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