This preprint provides empirical and synthetic validation of time–frequency representations aligned with the Geometry-Constrained Scale-Invariant Nonstationary (GCSIN) signal class for tokamak diagnostic analysis. Tokamak diagnostic signals commonly exhibit strong nonstationarity, frequency chirping, intermittency, and overlapping modal activity. A parametric synthetic benchmark suite comprising four signal families was constructed to reflect recurring diagnostic phenomenology: linear-frequency chirps with geometric phase structure, multiplicative (log-frequency) chirps, intermittent burst mixtures with broadband backgrounds, and overlapping modes constrained by geometry. Standard representations (STFT, CWT, synchrosqueezed wavelets, reassignment) were systematically compared against GCSIN-aligned kernels using three quantitative metrics: ridge sharpness, time–frequency entropy, and mode separability. Results show that GCSIN-aligned representations consistently reduce ridge dispersion, representational ambiguity, and modal conflation across parameter ranges, with the largest gains for signals exhibiting relative frequency evolution, intermittency, or geometry-driven overlap. These improvements arise from structural alignment between signal properties and representational assumptions rather than from algorithmic complexity. The benchmark suite and evaluation metrics introduced here provide a neutral, reproducible framework for assessing time–frequency representations in nonstationary, multi-scale, and geometry-structured diagnostic settings. This work focuses exclusively on the representational layer and does not address physical modeling, prediction, or control. Related open-source artifacts and prior work:• Psi Universe Attractor Library v2.0: https://doi.org/10.5281/zenodo.18939068• TASD Unified Framework: https://doi.org/10.5281/zenodo.18926912• A formal class of scale-invariant, geometry-constrained nonstationary signals: https://doi.org/10.5281/zenodo.18247445• A toroidal log-chirplet transform for nonstationary, scale-invariant plasma signals: https://doi.org/10.5281/zenodo.18050116 Keywords: Time-frequency analysis, GCSIN signals, Tokamak diagnostics, Synthetic benchmarks, Ridge sharpness, Mode separability
Nicolas Brian Quiroz (Fri,) studied this question.