{"id":"W3047856369","doi":"10.1093/mnras/stab1214","title":"Deep generative models for galaxy image simulations","year":2021,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"National Science Foundation","keywords":"Galaxy; Generative model; Parametric statistics; Parametric model; Computer science; Physics; Deep learning; Generative grammar; Artificial intelligence; Sample (material); Machine learning; Astrophysics; Statistics; Mathematics","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":[0.001613329,0.0007052426,0.000900125,0.001118348,0.0006402161,0.001491003,0.002489762,0.001947098,0.004758078],"category_scores_gemma":[0.007436312,0.0009609231,0.001598281,0.0008586249,0.001560706,0.001445064,0.001578519,0.002386253,0.0008535348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002159256,"about_ca_system_score_gemma":0.0009692092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01628559,"about_ca_topic_score_gemma":0.01805741,"domain_scores_codex":[0.9995797,0.0001660184,0.00002052467,0.00007142624,0.0001071186,0.00005522319],"domain_scores_gemma":[0.9964899,0.002493453,0.0002388208,0.0002705077,0.0003262314,0.0001811092],"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":[0.00001125027,0.000009090678,0.0005734964,0.00001030044,0.00001079778,0.00001962933,0.000024429,0.9799478,0.0001836983,0.01647743,0.0004666776,0.00226533],"study_design_scores_gemma":[0.000001927051,0.000001020095,0.00002442114,0.000001851139,9.266088e-7,0.000002773758,0.000001715607,0.9946014,0.00005672993,0.005156558,0.000148984,0.000001753386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03986578,0.0002543962,0.9526113,0.0007033663,0.00006080034,0.00005892027,0.0005908906,0.00192682,0.003927845],"genre_scores_gemma":[0.7695598,0.0004152012,0.2193779,0.0005539192,0.0001138415,0.0003940056,0.001608989,0.0008980443,0.007078224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01628559,"threshold_uncertainty_score":0.03238159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510662334725302,"score_gpt":0.2537671895499657,"score_spread":0.2386605662027127,"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."}}