{"id":"W3178740802","doi":"10.22323/1.395.0766","title":"Identifying muon rings in VERITAS data using convolutional neural networks trained on images classified with Muon Hunters 2","year":2021,"lang":"en","type":"article","venue":"Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Science; Natural Sciences and Engineering Research Council of Canada; European Commission; National Energy Research Scientific Computing Center; Alfred P. Sloan Foundation; U.S. Department of Energy; Smithsonian Institution; National Science Foundation","keywords":"Muon; Convolutional neural network; Physics; Artificial intelligence; Identification (biology); Computer science; Algorithm; Hough transform; Image (mathematics); Pattern recognition (psychology); Computer vision; Particle physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000727894,0.0007785006,0.0003488428,0.001566399,0.0004480396,0.0005822481,0.0008362409,0.0008030618,0.001667197],"category_scores_gemma":[0.001548034,0.000221186,0.0006186565,0.0007547511,0.0004141976,0.0006039456,0.0008076868,0.0007842272,0.0008823591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065155,"about_ca_system_score_gemma":0.0006376563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03751566,"about_ca_topic_score_gemma":0.07286475,"domain_scores_codex":[0.9995865,0.00003656884,0.00001846705,0.0001640165,0.00008523699,0.0001092194],"domain_scores_gemma":[0.9993652,0.0001916943,0.00007494769,0.0001281443,0.0001971248,0.00004296006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003603691,0.0008313367,0.1594405,0.0008996535,0.0005695686,0.001685418,0.001186238,0.1223316,0.09064941,0.00166264,0.05507165,0.5620683],"study_design_scores_gemma":[0.00009357974,0.0004381553,0.1978751,0.0002297178,0.0001724908,0.0005425991,0.001288913,0.7329094,0.04679869,0.001202462,0.01836671,0.00008221324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752958,0.0008151243,0.009511129,0.0002908767,0.0002076775,0.0001159006,0.006490751,0.00226621,0.005006414],"genre_scores_gemma":[0.93604,0.0002407635,0.02754031,0.0001387518,0.00004484808,0.00005966547,0.03098445,0.000119903,0.004831296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03751566,"threshold_uncertainty_score":0.07459462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05379909659143374,"score_gpt":0.282477043247432,"score_spread":0.2286779466559983,"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."}}