{"id":"W7128938467","doi":"","title":"Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning","year":2025,"lang":"en","type":"article","venue":"Lancaster EPrints (Lancaster University)","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; HORIZON EUROPE Framework Programme; Office of Science; European Commission; Ministerio de Ciencia e Innovación; Centre National de la Recherche Scientifique; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; European Regional Development Fund; U.S. Department of Energy; Junta de Andalucía; Fundação para a Ciência e a Tecnologia; Fundação de Amparo à Pesquisa do Estado de Goiás; Fermilab; UK Research and Innovation; National Science Foundation; Royal Society; Xunta de Galicia; CERN; Fundação de Amparo à Pesquisa do Estado de São Paulo; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Generalitat Valenciana","keywords":"Neutrino; Software; Vertex (graph theory); Time projection chamber; Detector; Artificial neural network; Neutrino detector; Projection (relational algebra); Deep learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008937214,0.0002082771,0.0002724449,0.0005915094,0.0001156703,0.00008327189,0.0002470887,0.00005511173,0.0005763884],"category_scores_gemma":[8.622559e-7,0.0002146022,0.00007327522,0.0009033474,0.00006683675,0.0005391553,0.0002157626,0.0006140356,0.0002669823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000147026,"about_ca_system_score_gemma":0.00005526782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002101214,"about_ca_topic_score_gemma":0.0001252155,"domain_scores_codex":[0.9986585,0.0001459806,0.0001832985,0.0004695612,0.0001509895,0.0003916099],"domain_scores_gemma":[0.999338,0.00008258141,0.00009754184,0.0003104248,0.00009274718,0.00007866446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004650892,0.0001222864,0.9026759,0.00003270535,0.0001228817,0.00004823388,0.0002136242,0.00003596105,0.0007315622,0.01184112,0.00001082521,0.0836998],"study_design_scores_gemma":[0.006577337,0.0001436109,0.9520428,0.0004985342,0.00007871461,0.0000204832,0.004525853,0.002777087,0.01022558,0.000461359,0.02204311,0.0006054772],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7493752,0.000004529768,0.004575679,0.00007740968,0.0001669866,0.0002219855,0.000002647998,0.00003124817,0.2455443],"genre_scores_gemma":[0.9880646,0.000004639496,0.0001151416,0.00002120208,0.00006837506,0.000004055348,0.00001516703,0.00001679004,0.01169004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2386894,"threshold_uncertainty_score":0.8751223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008396451749199943,"score_gpt":0.2314131016385957,"score_spread":0.2230166498893958,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}