Active Regime Structural Prediction (ARSP): Emergent Non-Random Structure in High-Resolution Orbital Datasets - Cross-Pipeline Analysis and Multi-Scale Validation within the Trinamica CP364 Framework

This release contains the complete implementation and results of the Active Regime Structural Prediction (ARSP) framework within the Trinamica CP364 system, applied to high-resolution orbital datasets derived from JPL Horizons. The framework transforms classical orbital data into a trinamic representation based on the following fundamental parameters: temporal variation (Δtφ) active energy (Ea) temporal memory (μt) transition parameter (§) This transformation enables the detection of non-random dynamic structures that are not observable in traditional geometric representations. Key Results The analysis of 87 Solar System objects shows that: orbital datasets contain an intrinsic structural organization incompatible with random distributions objects can be classified into four dynamic regimes (strong, intermediate, weak, inactive) statistical separation reaches Z ≈ 341σ, indicating extremely strong evidence Multi-scale Validation (MDT14 Bridge) A central result is the validation of structural consistency across different scales: pilot pipeline (18 objects, clustering-based) global pipeline (87 objects, regime-based classification) Both independently converge to the same structural organization without merging raw datasets, demonstrating that: the structure emerges intrinsically from the data and is not induced by the analytical method Observability Framework The release includes a formalization of conditional observability, describing how the detectability of dynamic regimes depends on temporal resolution. This explains why some objects (e.g., in the outer Solar System) appear inactive: the regime exists in the underlying dynamics but is not observable at the available temporal resolution What ARSP Actually Does ARSP does not classify objects based on predefined physical categories. Instead, the system: detects where structural transitions occur identifies when the dynamic regime changes estimates the intensity of transitions operates without assuming the physical nature of the event Applications The framework enables: analysis of Near-Earth Objects (NEOs) structural study of Trans-Neptunian Objects (TNOs) identification of dynamic transitions in orbital evolution support for future predictive models, including cases such as Apophis (2029 flyby) Release Contents This release includes: full ARSP pipeline (scripts and processing workflows) processed datasets (CSV / Parquet) reports, diagnostics, and validation outputs multi-scale comparison (MDT14 Bridge) technical and structural documentation Main Contribution This work introduces a replicable and scalable analytical framework that: separates observation from interpretation reveals hidden structures in orbital datasets lays the foundation for future predictive extensions