Separating channel cancellation from temporal cancellation in dynamical systems
Year: 2026
Type: preprint
Abstract: This preprint develops an equation-based procedure for distinguishing two reasons that an observable can change little despite substantial activity: opposing contributions at the same time, and reversals of their summed rate over time. Applying the chain rule to a declared observable constructs compatible signed channels, including explicit time dependence. Three integrated magnitudes separate channel cancellation from temporal cancellation, while signed histories and cumulative balances show how the contributions accumulate. The same implementation is applied to archived magnetofluid energy histories, linear solar oscillations reconstructed from published Model S eigenfunctions, and Hodgkin-Huxley voltage responses. Illustrative channel-cancellation fractions are approximately 36%, 57%, and 82%, respectively, with different temporal behavior. The elementary identities are established mathematics; the contribution is their explicit construction, common implementation, and numerical assessment across these distinct balances. The interpretation depends on the observable, channel grouping, numerical accuracy, and selected time window. Solar eigenfunction reconstruction is not an independent eigenvalue solution. Five of six comparisons with historical calculated neuronal curves meet the stated tolerance; the unsuccessful comparison remains reported. The method quantifies opposition in a specified balance without determining a unique causal transfer network or establishing universal behavior across materials. The deposit includes the paper, detailed supplement, editable sources, figures, compact channel histories, analysis scripts, and integrity manifests. The MHD evidence is reused from the parent study (https://doi.org/10.5281/zenodo.23633307). Saved-data figure and table reproduction is documented; full model replay requires the identified upstream inputs, with an explicit retrieval route for omitted solar files. OpenAI Codex assisted with source inspection, analysis-code development, mathematical discussion, data analysis, plotting-code preparation, and manuscript preparation. Jake Campbell directed the research and accepts responsibility for the manuscript.
Author: Campbell, Jake
Language: English
Topic: Complex Systems and Dynamics
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225450637