{"paper":{"title":"Signal Confidence Limits from a Neural Network Data Analysis","license":"","headline":"","cross_cats":["hep-ph"],"primary_cat":"hep-ex","authors_text":"B. Berg, J. Riedler (FSU, Tallahassee)","submitted_at":"1997-03-01T22:12:57Z","abstract_excerpt":"This paper deals with a situation of some importance for the analysis of experimental data via Neural Network (NN) or similar devices: Let $N$ data be given, such that $N=N_s+N_b$, where $N_s$ is the number of signals, $N_b$ the number of background events, both unknown. Assume that a NN has been trained, such that it will tag signals with efficiency $F_s$, $(0<F_s<1)$ and background data with $F_b$, $(0<F_b<1)$. Applying the NN yields $N^Y$ tagged events. We demonstrate that the knowledge of $N^Y$ is sufficient to calculate confidence bounds for the signal likelihood, which have the same stat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"hep-ex/9703001","kind":"arxiv","version":2},"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"}