{"id":"W4386346313","doi":"10.48550/arxiv.2308.15657","title":"KilonovAE: Exploring Kilonova Spectral Features with Autoencoders","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gamma-ray bursts and supernovae","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Uppsala Universitet; Universität Wien; Canada Research Chairs; McGill University","keywords":"Kilonova; Physics; Astrophysics; Ejecta; Context (archaeology); Spectral line; Astronomy; Supernova; Geology","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":[0.000831798,0.001123635,0.0006556642,0.0007404499,0.0003245036,0.0009317048,0.001388753,0.00103389,0.001351814],"category_scores_gemma":[0.002011574,0.0005929436,0.0009795144,0.0004999974,0.0004688449,0.001050394,0.0009465227,0.001596664,0.0003507737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008714068,"about_ca_system_score_gemma":0.0006880239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122391,"about_ca_topic_score_gemma":0.0116958,"domain_scores_codex":[0.9998214,0.00004669241,0.000009504629,0.00005907469,0.00002976595,0.00003360749],"domain_scores_gemma":[0.9991205,0.000609822,0.00007145882,0.00006903605,0.00009093642,0.00003809131],"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":[0.0002078696,0.0001899739,0.004813956,0.0000871093,0.0001908377,0.0001380933,0.0001247229,0.8978914,0.004980741,0.003903235,0.003363041,0.08410905],"study_design_scores_gemma":[0.000003104371,0.000005997406,0.0002279296,0.000003438769,0.000003203452,0.00000356841,0.000008315734,0.9981724,0.0002659608,0.001173443,0.0001299174,0.000002807102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3349245,0.001019432,0.6542942,0.0006787474,0.000153228,0.00008496768,0.001052608,0.005360092,0.00243233],"genre_scores_gemma":[0.795684,0.0002541426,0.1979757,0.000323421,0.00008197149,0.0001469845,0.002948145,0.0003902229,0.002195347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01122391,"threshold_uncertainty_score":0.02231717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024258226628871,"score_gpt":0.1935446608117387,"score_spread":0.09111883814885167,"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."}}