Dynamic Segmentation and Ontological Interpretation of CMS Open Data:Application of PRCS and Mazein Mechanics to Dimuon Event Analysis

This work presents a novel approach to the analysis of CMS Dimuon Open Data, which have been available to the scientic community for over a decade. Traditional methods rely on static kinematic cuts and supervised learning, ignoring the sequential structure of events.We apply the method of Phenomenological Reconstruction of Complex Systems (PRCS) to extract hidden temporal dynamics: scalar proxy → uctuation density → singular value decomposition (SVD) → structural risk index K(t) → segmentation → shapelet clustering → kinematic mapping. High-structural-risk phases are found to concentrate with statistical signicance in the regions M ≈ 6580 GeV, pT > 40 GeV, and |η| < 1. The results are interpreted within the framework of the Tensor Model of Discrete Dynamics (TMDD) and Mazein Mechanics (MM), where inertia serves as a measure of dynamic complexity and pattern stability, time emerges as an internal coordination parameter, and particles correspond to classes of distinguishable θ-congurations. This work demonstrates a methodological shift from searching for static classes toward dynamic segmentation of phase space, oering a reproducible pipeline applicable to any sequential experimental data.