{"id":"W4287728232","doi":"10.1103/physrevd.107.016002","title":"Variational autoencoders for anomalous jet tagging","year":2023,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Anomaly detection; Outlier; Jet (fluid); Computer science; Anomaly (physics); Regularization (linguistics); Artificial intelligence; Pattern recognition (psychology); Physics; Particle physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002304827,0.0009107909,0.0009083682,0.0006291053,0.000398686,0.0008453146,0.001539914,0.001206355,0.00124475],"category_scores_gemma":[0.006613773,0.0007260601,0.0008884459,0.0005686678,0.001040902,0.001272356,0.001231564,0.002409112,0.0003314975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009836307,"about_ca_system_score_gemma":0.000812966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005123549,"about_ca_topic_score_gemma":0.005418557,"domain_scores_codex":[0.9993474,0.0002937659,0.00003650908,0.0001339309,0.000123325,0.00006512024],"domain_scores_gemma":[0.9965537,0.002601848,0.0001843193,0.000267741,0.0003171081,0.00007535132],"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":[0.00005959589,0.00003448711,0.001587543,0.00006872842,0.0001103674,0.0000653586,0.00007732194,0.8972396,0.00251512,0.0377343,0.00103656,0.05947103],"study_design_scores_gemma":[0.000001441303,0.000005759799,0.00006003594,0.000003711387,0.000003270618,0.000006136018,0.000001896513,0.9943718,0.0002809539,0.005061989,0.0002003169,0.000002720368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01830202,0.0007684834,0.9791607,0.0002738597,0.00005090793,0.00001903994,0.00006496182,0.0003088489,0.001051038],"genre_scores_gemma":[0.6709287,0.001113793,0.3196582,0.0004782979,0.0001708313,0.0001310104,0.0005467245,0.0002737058,0.006698874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005123549,"threshold_uncertainty_score":0.01218921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378164081759547,"score_gpt":0.4251199105942585,"score_spread":0.401338269776663,"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."}}