{"id":"W2925047697","doi":"10.1007/jhep08(2019)110","title":"DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC","year":2019,"lang":"en","type":"article","venue":"Journal of High Energy Physics","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Science and Technology Facilities Council; Nvidia","keywords":"Large Hadron Collider; Detector; Event generator; Monte Carlo method; Kinematics; Generator (circuit theory); Quantum chromodynamics; Code (set theory)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001517346,0.0001359155,0.0002759622,0.000005633023,0.0002146993,0.00001553483,0.0002115476,0.00001901311,0.00003225065],"category_scores_gemma":[0.00000327713,0.00007034063,0.0002631637,0.0001126051,0.0001086257,0.0001099256,0.0001142974,0.00008929455,0.000002835257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002874791,"about_ca_system_score_gemma":0.00001942248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000391209,"about_ca_topic_score_gemma":3.529197e-7,"domain_scores_codex":[0.9990996,0.00006255506,0.0002984803,0.0001050514,0.0002411784,0.0001931005],"domain_scores_gemma":[0.9989246,0.000367207,0.0003796832,0.0001706714,0.0001215713,0.00003632781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002207789,0.0001648902,0.001963892,0.000003398967,0.0003585892,4.394797e-8,0.0002025075,0.5001471,0.001094006,0.4934348,0.0003221701,0.002087807],"study_design_scores_gemma":[0.002430238,0.0003955924,0.0007948565,0.00002755477,0.0002851239,5.537348e-7,0.0004197968,0.09734302,0.109902,0.7856255,0.002520399,0.0002552943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.373598,0.0002478765,0.6242249,0.0002290106,0.0005149011,0.000179704,0.00002425808,0.000003396979,0.0009779744],"genre_scores_gemma":[0.9970868,0.000007210824,0.0007006858,0.0000429855,0.001983647,0.00001361475,0.00001215949,0.00001411314,0.0001387363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6235242,"threshold_uncertainty_score":0.2868407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422906991789903,"score_gpt":0.2518428085202876,"score_spread":0.2376137386023886,"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."}}