{"id":"W4388274849","doi":"10.48550/arxiv.2311.00032","title":"Reviving MeV-GeV Indirect Detection with Inelastic Dark Matter","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":"Office of Science; Ministry of Colleges and Universities; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Government of Canada; University of Chicago; Fermilab; U.S. Department of Energy","keywords":"Physics; Annihilation; Dark matter; Cosmic microwave background; Parameter space; Weakly interacting massive particles; Light dark matter; Particle physics; Astrophysics; Population; Excited state; Nuclear physics; 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":[0.0006635636,0.0003781033,0.0002479687,0.0002293531,0.0002066958,0.0007663699,0.0004972912,0.0004050464,0.002021144],"category_scores_gemma":[0.001267213,0.0002207639,0.000280269,0.0002752854,0.0005698774,0.0007442832,0.001024319,0.0004062397,0.0002340472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003128967,"about_ca_system_score_gemma":0.0001810138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005764205,"about_ca_topic_score_gemma":0.001127676,"domain_scores_codex":[0.9998304,0.00003819829,0.000004249215,0.00004892918,0.00004469087,0.00003343338],"domain_scores_gemma":[0.9994162,0.0001693236,0.0001634912,0.0001650679,0.00004032349,0.00004569777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001162103,0.0002326362,0.3848551,0.0003841109,0.0003456855,0.001635363,0.000769901,0.05458084,0.3285321,0.1613094,0.002340592,0.06385214],"study_design_scores_gemma":[0.0001324562,0.001038765,0.1462503,0.00005832073,0.0002343667,0.004266266,0.0007028771,0.556828,0.1815782,0.0971823,0.01160362,0.0001245079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575844,0.0002807393,0.03399684,0.0001654205,0.00001718128,0.00001101447,0.00017807,0.00018145,0.007584867],"genre_scores_gemma":[0.9957792,0.0000564851,0.003298555,0.00002452426,0.000005138696,0.000005625585,0.00009440955,0.00001162849,0.000724476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002021144,"threshold_uncertainty_score":0.006761372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03720258731784545,"score_gpt":0.1701800924107034,"score_spread":0.132977505092858,"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."}}