{"paper":{"title":"Obtaining Parton Distribution Functions from Self-Organizing Maps","license":"","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"D. Brogan, H. Honkanen, P. Reynolds, S. Liuti, Y.C. Loitiere","submitted_at":"2006-08-22T20:15:37Z","abstract_excerpt":"We present an alternative algorithm to global fitting procedures to construct Parton Distribution Functions (PDFs) parametrizations. The proposed algorithm uses Self-Organizing Maps (SOMs) which at variance with the standard Neural Networks, are based on competitive-learning. SOMs generate a non-uniform projection from a high dimensional data s pace onto a low dimensional one (usually 1 or 2 dimensions) by clustering similar PDF representations together. The SOMs are trained on progressively narrower selections of data samples. The selection criterion is that of convergence towards a neighborh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"hep-ph/0608254","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"}