{"paper":{"title":"NNVub: a Neural Network Approach to $B\\to X_u \\ell \\nu$","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex"],"primary_cat":"hep-ph","authors_text":"Cristina Mondino, Kristopher J. Healey, Paolo Gambino","submitted_at":"2016-04-26T10:04:36Z","abstract_excerpt":"We use artificial neural networks to parameterize the shape functions in inclusive semileptonic $B$ decays without charm. Our approach avoids the adoption of functional form models and allows for a straightforward implementation of all experimental and theoretical constraints on the shape functions. The results are used to extract $|V_{ub}|$ in the GGOU framework and compared with the original GGOU paper and the latest HFAG results, finding good agreement in both cases. The possible impact of future Belle-II data on the $M_X$ distribution is also discussed."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.07598","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"}