{"id":"W4403789085","doi":"10.48550/arxiv.2409.16365","title":"Perturbative treatment of nonlocal chiral interactions in auxiliary-field diffusion Monte Carlo calculations","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum, superfluid, helium dynamics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nuclear Physics; Advanced Scientific Computing Research; Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; Alliance de recherche numérique du Canada; National Energy Research Scientific Computing Center; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Monte Carlo method; Statistical physics; Physics; Non-perturbative; Auxiliary field; Field (mathematics); Diffusion Monte Carlo; Quantum electrodynamics; Monte Carlo molecular modeling; Mathematics; Mathematical physics; Markov chain Monte Carlo; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004095431,0.0003502706,0.0004378005,0.0003517322,0.00008161327,0.00003309327,0.0002364729,0.0001311035,0.0002063945],"category_scores_gemma":[0.000006659137,0.0003653039,0.0004107238,0.0003639559,0.00008046521,0.0001184105,0.0004991958,0.0005731147,0.00004291873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004378474,"about_ca_system_score_gemma":0.0002217088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01302586,"about_ca_topic_score_gemma":0.001825712,"domain_scores_codex":[0.9986137,0.00008994726,0.0003036887,0.0006669991,0.00006922622,0.0002564446],"domain_scores_gemma":[0.9990031,0.0001676959,0.0001253617,0.000512272,0.00009023584,0.0001014031],"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":[0.0002685557,0.002257497,0.1999384,0.0001773975,0.001010219,0.0002488424,0.006547198,0.5100321,0.0004341648,0.2750007,0.0001962692,0.003888595],"study_design_scores_gemma":[0.000645702,0.000127842,0.003415258,0.0002356962,0.0002764667,8.722648e-7,0.001223238,0.9750269,0.0001982341,0.01827363,0.0002207819,0.0003553719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979744,0.00007061522,0.01184144,0.000121995,0.0005864781,0.0004222068,0.0003367582,0.00004753556,0.006828988],"genre_scores_gemma":[0.99418,0.00004781362,0.00007380682,0.000008636685,0.0001333009,0.00000636135,0.0001099624,0.0000309824,0.005409166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4649948,"threshold_uncertainty_score":0.9998799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04384550716832553,"score_gpt":0.2221319137798915,"score_spread":0.1782864066115659,"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."}}