PAC Studio Machine Learning: Human-in-the-Loop Analysis of TDPAC Spectra

Abstract

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 parameter prediction, Auto sites model-family screening, ML-seeded nonlinear least-squares refinement, visualization, benchmarking, diagnostics, model-card reporting, and export tools. The ML component is designed to support, not replace, conventional fitting and expert interpretation by accelerating parameter exploration, suggesting plausible initialization regions, comparing site-count hypotheses, and improving reproducibility. Held-out synthetic tests demonstrate proof of operation and illustrate the unequal recoverability of PAC parameters in difficult inverse problems. Selected BiFeO3 examples demonstrate conventional, direct-ML, ML-seeded, and Auto sites workflows as software case studies, not as a complete experimental validation corpus. PAC Studio ML is therefore positioned as a supporting tool for expert PAC analysis: it improves workflow speed and diagnostic transparency while final model choice, physical constraints, and materials interpretation remain the responsibility of the researcher.

Withdrawn by the submitting author because the manuscript was posted before all co-authors had completed their review and approved its public release. A revised version may be submitted after all authors have reached agreement

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