{"id":"W3180702093","doi":"10.22323/1.395.1053","title":"Reconstructing Neutrino Energy using CNNs for GeV Scale IceCube Events","year":2021,"lang":"en","type":"preprint","venue":"Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Office of Experimental Program to Stimulate Competitive Research; Deutsches Elektronen-Synchrotron; College of Engineering, Michigan State University; Helmholtz Alliance for Astroparticle Physics; RWTH Aachen University; Vetenskapsrådet; Knut och Alice Wallenbergs Stiftelse; Fonds Wetenschappelijk Onderzoek; Deutsche Forschungsgemeinschaft; Belgian Federal Science Policy Office; Bundesministerium für Bildung und Forschung; Office of Polar Programs; Fonds De La Recherche Scientifique - FNRS; Polarforskningssekretariatet; Science and Technology Facilities Council; Michigan State University; Marquette University; University of Wisconsin-Madison; U.S. Department of Energy; National Science Foundation","keywords":"Neutrino; Physics; Neutrino detector; Neutrino oscillation; Observatory; Event (particle physics); Event reconstruction; Particle physics; Convolutional neural network; Energy (signal processing); Detector; Astronomy; Computer science; Astrophysics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003402624,0.000775521,0.0003798617,0.0006131083,0.0002762124,0.0006447136,0.0008032756,0.000598806,0.001804911],"category_scores_gemma":[0.001701753,0.0004080622,0.0005317554,0.0005800643,0.000320779,0.0007648632,0.0005783323,0.0008215847,0.0005028563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005421,"about_ca_system_score_gemma":0.0005160147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01866399,"about_ca_topic_score_gemma":0.02062784,"domain_scores_codex":[0.9998813,0.00001425604,0.000004815778,0.00004120059,0.00002905316,0.00002946946],"domain_scores_gemma":[0.9997509,0.00009310422,0.00003570519,0.00004519451,0.00005447559,0.00002056474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005612159,0.00009832555,0.01722623,0.00008959221,0.0001907664,0.000338925,0.00006400618,0.8453381,0.011528,0.006113455,0.006957422,0.111494],"study_design_scores_gemma":[0.000005763192,0.000008691305,0.00158167,0.00000505975,0.000007047991,0.00002057725,0.0000108183,0.9929102,0.002655896,0.002143931,0.000645454,0.000004937871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7925711,0.0009976336,0.1847368,0.001057587,0.0002786494,0.0000515537,0.003930038,0.003655235,0.01272151],"genre_scores_gemma":[0.9417489,0.0003466708,0.04729746,0.0001590257,0.00008089231,0.00002990542,0.006264393,0.0002176017,0.003855104],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01866399,"threshold_uncertainty_score":0.03711075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02932423144838037,"score_gpt":0.2681792089657912,"score_spread":0.2388549775174109,"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."}}