{"paper":{"title":"PAC Studio Machine Learning: Human-in-the-Loop Analysis of TDPAC Spectra","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Doru Constantin Lupascu, Thien Thanh Dang","submitted_at":"2026-07-13T09:15:02Z","abstract_excerpt":"Time-differential perturbed angular correlation (TDPAC or PAC) analysis is an ill-conditioned inverse problem in which site count, interaction type, correlated hyperfine parameters, damping, and initialization choices can produce competing numerical solutions. This software paper presents PAC Studio ML, a human-in-the-loop Python desktop environment for physics-informed inverse analysis of PAC spectra. The software integrates a Hamiltonian-based forward PAC model, user-defined synthetic training libraries, feature extraction, one-, two-, and three-site machine-learning predictors, direct param"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11298","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/2607.11298/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"}