{"id":"W4388514407","doi":"10.48550/arxiv.2311.03591","title":"No Catch-22 for Fuzzy Dark Matter: testing substructure counts and core sizes via high-resolution cosmological simulations","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tamkeen; Ministero dell’Istruzione, dell’Università e della Ricerca; York University; New York University Abu Dhabi","keywords":"Physics; Cold dark matter; Dark matter; Astrophysics; Substructure; Warm dark matter; Galaxy; Dwarf galaxy; Scalar field dark matter; Cosmology; Dark energy","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":[0.001279735,0.0005785476,0.000632844,0.0006933953,0.0006272553,0.0008642389,0.001671566,0.001060449,0.001770494],"category_scores_gemma":[0.007444316,0.000410567,0.0007140593,0.0005295128,0.0007446521,0.0008781441,0.0007400446,0.0009435752,0.0001599853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023393,"about_ca_system_score_gemma":0.0006264753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02160584,"about_ca_topic_score_gemma":0.0139648,"domain_scores_codex":[0.9997275,0.0001043956,0.00001291186,0.00005197747,0.00004496839,0.00005821017],"domain_scores_gemma":[0.9965359,0.002248286,0.0002932167,0.0003268325,0.0002636085,0.0003322423],"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":[0.0002543475,0.0001900368,0.02288931,0.00008754092,0.0001673985,0.0001752237,0.0001629506,0.9589583,0.002150721,0.009594979,0.00128728,0.004081899],"study_design_scores_gemma":[0.00003445964,0.00002075234,0.001277111,0.000005253768,0.000008320381,0.00001039349,0.00002020454,0.9971581,0.0003680175,0.0009245997,0.0001661048,0.000006775373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887797,0.000143254,0.006780588,0.000268441,0.00002885652,0.00002970607,0.0006194742,0.0002947323,0.003055217],"genre_scores_gemma":[0.9907776,0.00004667242,0.008117156,0.00009096785,0.00001343785,0.00002887212,0.0005479971,0.0001144099,0.0002629324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02160584,"threshold_uncertainty_score":0.04296017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06554721642249675,"score_gpt":0.2089044944475404,"score_spread":0.1433572780250437,"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."}}