{"paper":{"title":"Progress in the Neural Network Determination of Polarized Parton Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","nucl-th"],"primary_cat":"hep-ph","authors_text":"A. Guffanti, F. Cerutti, G. Ridolfi, J. I. Latorre, J. Rojo, L. Del Debbio, M. Ubiali, R. D. Ball, S. Forte, V. Bertone","submitted_at":"2010-07-02T12:31:13Z","abstract_excerpt":"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 unce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1007.0351","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}