{"id":"W4401319384","doi":"10.48550/arxiv.2408.00922","title":"Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method","year":2024,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"SLAC National Accelerator Laboratory; Deutsche Forschungsgemeinschaft; High Energy Physics; Office of Science; Institut de Física d'Altes Energies; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Centres de Recerca de Catalunya; Argonne National Laboratory; European Regional Development Fund; U.S. Department of Energy; European Commission; Science and Technology Facilities Council; University College London; University of Portsmouth; Ohio State University; Integrated Electronics Engineering Center, Binghamton University; University of Illinois at Urbana-Champaign; Lawrence Berkeley National Laboratory; University of Pennsylvania; Financiadora de Estudos e Projetos; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; University of Sussex; Ministério da Ciência, Tecnologia e Inovação; Generalitat de Catalunya; Fermilab; National Science Foundation","keywords":"Redshift; Physics; Photometric redshift; Astrophysics; Weak gravitational lensing; Redshift survey; Dark energy; Galaxy; Characterization (materials science); Cosmology","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":[0.001274983,0.0008996822,0.0004861284,0.001634305,0.0003282111,0.00094076,0.001237357,0.0005854194,0.00167834],"category_scores_gemma":[0.004770977,0.0002491002,0.0007025519,0.0008686706,0.0002905631,0.0009100209,0.001124477,0.0007158311,0.001179402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004536203,"about_ca_system_score_gemma":0.0009155862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076849,"about_ca_topic_score_gemma":0.01461231,"domain_scores_codex":[0.9996083,0.00009247138,0.00002072215,0.00007741364,0.0001369628,0.00006416201],"domain_scores_gemma":[0.9990577,0.0002989977,0.000114944,0.0001743903,0.0002842518,0.00006975051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000417769,0.0002248403,0.04592463,0.0002552025,0.0004054055,0.0001613715,0.0001931725,0.2819888,0.0175724,0.008364883,0.0130686,0.6314231],"study_design_scores_gemma":[0.00002313557,0.00002422928,0.007146983,0.00001228251,0.00002433979,0.00003988383,0.00006115709,0.9818954,0.004959088,0.003787972,0.002006481,0.00001904297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1466888,0.0005326309,0.8421879,0.0004465654,0.00008061673,0.000109415,0.001383911,0.005119519,0.003450747],"genre_scores_gemma":[0.613037,0.0003005259,0.3796284,0.0002215455,0.0001055788,0.0001232553,0.003706388,0.000459339,0.00241783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01076849,"threshold_uncertainty_score":0.02141166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767681085164182,"score_gpt":0.1674048781621946,"score_spread":0.1497280673105528,"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."}}