{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008786283,0.0005876323,0.0003009645,0.0004091156,0.0003158624,0.0006144374,0.001697251,0.0006477791,0.003241268],"category_scores_gemma":[0.002867246,0.0005986591,0.0005191057,0.0004285864,0.000444202,0.000547917,0.0009550847,0.0009773178,0.0009521088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000755067,"about_ca_system_score_gemma":0.0009090413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01013855,"about_ca_topic_score_gemma":0.008614982,"domain_scores_codex":[0.9997597,0.00005361841,0.00001291829,0.00006425936,0.00007744914,0.00003205508],"domain_scores_gemma":[0.9992742,0.0002657477,0.00006760601,0.000152996,0.0001238591,0.0001155375],"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":[0.0007997259,0.0002825663,0.01962557,0.0001392658,0.0002349349,0.0004729971,0.0004214122,0.8793258,0.02071679,0.005258586,0.01630127,0.05642108],"study_design_scores_gemma":[0.00006205578,0.00002742062,0.001399147,0.000002404409,0.00000629134,0.00001878901,0.00001552984,0.9930786,0.002895237,0.0009056861,0.001576126,0.00001268094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3831937,0.0001771093,0.5295884,0.0007287629,0.0002390594,0.000504797,0.01045242,0.0687971,0.006318673],"genre_scores_gemma":[0.6914849,0.00005872116,0.2930173,0.0001573277,0.00003723084,0.0003261868,0.01091488,0.002627149,0.001376295],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01013855,"threshold_uncertainty_score":0.02015907,"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). 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