{"id":"W4406261991","doi":"10.1109/qce60285.2024.10262","title":"Calo4pQVAE: Quantum-Assisted 4-Partite VAE Surrogate for High Energy Particle-Calorimeter Interactions","year":2024,"lang":"en","type":"article","venue":"","topic":"High-Energy Particle Collisions Research","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of British Columbia; Perimeter Institute; University of Waterloo; TRIUMF","funders":"Ontario Ministry of Research, Innovation and Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Calorimeter (particle physics); Energy (signal processing); Particle (ecology); Physics; Quantum; Computer science; Nuclear physics; Nuclear engineering; Quantum mechanics; Engineering; Optics; Detector; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001106869,0.000716045,0.001036807,0.0003701455,0.0005746903,0.001087232,0.002092486,0.001614508,0.004904061],"category_scores_gemma":[0.003461499,0.0005659226,0.0008043541,0.0004547013,0.001190827,0.001097265,0.001917241,0.002305031,0.0009232527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023664,"about_ca_system_score_gemma":0.001500856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005688301,"about_ca_topic_score_gemma":0.01179097,"domain_scores_codex":[0.9996222,0.0001582464,0.00001225617,0.00006127513,0.00008763783,0.00005831283],"domain_scores_gemma":[0.9989296,0.0006682384,0.00005901484,0.000130832,0.0001268146,0.00008551241],"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":[0.00007072042,0.00003163157,0.0005269673,0.0000435843,0.00003818538,0.00006885486,0.0000282713,0.9585261,0.0007585443,0.02309367,0.00280964,0.01400382],"study_design_scores_gemma":[0.000003907172,0.000006478773,0.00001851198,0.000002894195,0.000001267957,0.000004545433,0.000002109889,0.9942452,0.0001227017,0.005209143,0.0003812971,0.000001990684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03286068,0.0005997001,0.9539993,0.0008149663,0.0001791572,0.00007859089,0.0006373975,0.001635277,0.009194811],"genre_scores_gemma":[0.7192898,0.0003502295,0.2632273,0.001157069,0.0001531984,0.0003756622,0.00195632,0.0008569669,0.01263329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005688301,"threshold_uncertainty_score":0.0164057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03526951628139185,"score_gpt":0.3250101010367336,"score_spread":0.2897405847553417,"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."}}