{"id":"W3045422897","doi":"10.1051/0004-6361/202039048","title":"Persistent homology in cosmic shear: Constraining parameters with topological data analysis","year":2021,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Facilities Council; Danmarks Grundforskningsfond; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Deutsche Forschungsgemeinschaft; National Research Foundation; Western Canada Research Grid; Compute Canada","keywords":"Topological data analysis; Persistent homology; Dark energy; Cosmology; COSMIC cancer database; Topological defect; Weak gravitational lensing; Dark matter; Markov chain Monte Carlo","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002824273,0.0009713494,0.0009117351,0.003151082,0.0007247784,0.001746454,0.001572697,0.00112014,0.001986057],"category_scores_gemma":[0.01510198,0.0006043412,0.0009675337,0.001782168,0.001261761,0.002501775,0.002754496,0.001275561,0.0004780534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008124316,"about_ca_system_score_gemma":0.0007796082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004485961,"about_ca_topic_score_gemma":0.004606014,"domain_scores_codex":[0.9992999,0.0003282479,0.00002575279,0.0001284605,0.0001396145,0.00007801339],"domain_scores_gemma":[0.9953042,0.002677417,0.0005791486,0.0008039409,0.0002762975,0.0003590155],"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":[0.0003487318,0.0001432904,0.05578289,0.0001689011,0.0002482844,0.0002021034,0.0004224679,0.8410699,0.004723608,0.03143129,0.002708971,0.06274958],"study_design_scores_gemma":[0.00001705369,0.00002612836,0.002663918,0.00001243024,0.000009814819,0.00002933354,0.00004315047,0.9766499,0.001077709,0.01882052,0.0006283058,0.00002176139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7110832,0.0003746898,0.2802399,0.0004643906,0.0000557413,0.00004947386,0.001217346,0.003252562,0.003262586],"genre_scores_gemma":[0.9258566,0.00008893572,0.07118715,0.00009439057,0.000039865,0.00004470383,0.00191973,0.0004291037,0.0003395158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004485961,"threshold_uncertainty_score":0.01493639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314804417966561,"score_gpt":0.2403467344350823,"score_spread":0.2088662926384262,"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."}}