{"id":"W2995385347","doi":"10.1051/0004-6361/201936622","title":"SYMBA : an end-to-end VLBI synthetic data generation pipeline simulating event horizon telescope observations of M 87","year":2020,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Perimeter Institute; University of Waterloo","funders":"Los Alamos National Laboratory; Office of International Science and Engineering; National Key Research and Development Program of China; Comisión Nacional de Investigación Científica y Tecnológica; Japan Society for the Promotion of Science; China Scholarship Council; European Southern Observatory; Ministry of Education, Culture, Sports, Science and Technology; John Templeton Foundation; Ministerio de Economía y Competitividad; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; Institut Périmètre de physique théorique; Recruitment Program of Global Experts; Max-Planck-Gesellschaft; Centre National de la Recherche Scientifique; National Natural Science Foundation of China; National Research Foundation of Korea; Ministerio de Ciencia, Innovación y Universidades; Nuclear Safety and Security Commission; Generalitat Valenciana; Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Consejo Nacional de Ciencia y Tecnología; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Instituto de Astrofísica de Andalucía; Chinese Academy of Sciences; Vetenskapsrådet; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; Smithsonian Institution; U.S. Department of Energy; European Commission; Leverhulme Trust; National Radio Astronomy Observatory; Harvard University; Toray Science Foundation; National Research Foundation; University of Arizona; National Astronomical Observatory of Japan; Space Telescope Science Institute; Associated Universities; Universidad Nacional Autónoma de México; Korea Astronomy and Space Science Institute; National Science Foundation; Compute Canada; National Institutes of Natural Sciences; Government of Canada; Istituto Nazionale di Fisica Nucleare; Department of Science and Technology, Ministry of Science and Technology, India; Russian Science Foundation; Gordon and Betty Moore Foundation; National Aeronautics and Space Administration; Academia Sinica; Flatiron Health","keywords":"Very-long-baseline interferometry; Physics; Synthetic data; Context (archaeology); Calibration; Pipeline (software); Noise (video); Telescope; Astrophysics; Remote sensing; Astronomy; Algorithm; Computer science; Image (mathematics); Geology; Artificial intelligence","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":[0.0001656432,0.0001157104,0.000222172,0.00008626676,0.0001889108,0.00002627418,0.0005434012,0.00003332609,0.0005511447],"category_scores_gemma":[0.00003301546,0.000139204,0.00007119784,0.0003193302,0.00006141917,0.0005034534,0.0003709916,0.0001191129,0.00001906971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002282086,"about_ca_system_score_gemma":0.0001115165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003629013,"about_ca_topic_score_gemma":0.00005150028,"domain_scores_codex":[0.9988405,0.00008563503,0.000198293,0.0003592004,0.0003393297,0.0001770547],"domain_scores_gemma":[0.9989037,0.00008094141,0.0001459067,0.0004375858,0.0002609217,0.0001709358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004746532,0.001482794,0.01880045,0.0004251463,0.0005473004,0.00003306675,0.01422514,0.05346319,0.5999453,0.01769771,0.0153188,0.2775864],"study_design_scores_gemma":[0.002278749,0.001336644,0.04359166,0.0002505479,0.0002326965,9.914691e-7,0.01635218,0.911292,0.004802784,0.002044657,0.01702931,0.0007877993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665098,0.00002099416,0.02704079,0.001988025,0.00005150324,0.0003198301,0.003249038,0.00001080613,0.0008092763],"genre_scores_gemma":[0.9888368,0.000001888082,0.006976238,0.00004144092,0.0001604853,3.242307e-7,0.003156455,0.00001183766,0.0008145737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8578288,"threshold_uncertainty_score":0.6034647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07469419085518118,"score_gpt":0.3122159529511405,"score_spread":0.2375217620959593,"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."}}