{"paper":{"title":"Extraction of the microscopic properties of quasi-particles using deep neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nucl-th"],"primary_cat":"hep-ph","authors_text":"Andrea Palermo, Elena Bratkovskaya, Olga Soloveva","submitted_at":"2023-11-27T16:28:40Z","abstract_excerpt":"We use deep neural networks (DNN) to obtain the microscopic characteristics of partons in terms of dynamical degrees of freedom on the basis of an off-shell quasiparticle description. We aim to infer masses and widths of quasi-gluons, up/down, and strange quarks using constraints on the macroscopic thermodynamic observables obtained by the first-principles calculations lattice QCD. In this work, we use 3 independent dimensionless thermodynamic observables from lQCD for minimization. First, we train our DNN using the DQPM (Dynamical QuasiParticle Model) Ansatz for the masses and widths. Further"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15984","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.15984/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}