{"id":"W3178384612","doi":"10.1088/1748-0221/16/11/c11001","title":"Direction Reconstruction using a CNN for GeV-Scale Neutrinos in IceCube","year":2021,"lang":"en","type":"preprint","venue":"Journal of Instrumentation","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":0,"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; Zenith; Neutrino detector; Neutrino astronomy; Scale (ratio); Observatory; Neutrino oscillation; Convolutional neural network; Measurements of neutrino speed; Astronomy; Particle physics; Solar neutrino; Optics; Computer science; 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":[0.0003154068,0.0007891204,0.0004448073,0.0003952307,0.0002799309,0.0005079134,0.0009488089,0.0006147771,0.001757197],"category_scores_gemma":[0.0008917234,0.0003667372,0.0005525948,0.0003862423,0.0002834417,0.0006966694,0.0005440986,0.000730352,0.0003987665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008752179,"about_ca_system_score_gemma":0.0007590443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02056672,"about_ca_topic_score_gemma":0.01611323,"domain_scores_codex":[0.9999013,0.00001218014,0.000003537152,0.00003626212,0.00002064822,0.00002608853],"domain_scores_gemma":[0.9998394,0.00004971675,0.0000175697,0.00003114127,0.0000455962,0.00001648661],"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":[0.0004926585,0.0001460373,0.006366343,0.00006292331,0.0002000466,0.0003141042,0.00005754756,0.8225821,0.01245288,0.003582229,0.004126557,0.1496165],"study_design_scores_gemma":[0.000004834617,0.00001119329,0.0003076522,0.000002255591,0.000004373378,0.00000740837,0.000004889564,0.9976068,0.001118259,0.0007211299,0.0002089952,0.000002271559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6534022,0.001096451,0.3310139,0.000835222,0.0003237718,0.00009337014,0.001380297,0.00444173,0.00741301],"genre_scores_gemma":[0.9208007,0.0002117404,0.07275712,0.0001526912,0.00005738017,0.00004057897,0.002196003,0.0001035758,0.003680199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02056672,"threshold_uncertainty_score":0.04089403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836691459690464,"score_gpt":0.2712073009356039,"score_spread":0.2528403863386993,"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."}}