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Savant_v7 Stability Engine (RRF Φ5.2)

Overview

The Savant_v7 Stability Engine is a machine-learning-based classification and prediction system designed to evaluate the topological stability of galactic rotation curves within the Resonance of Reality Framework (Φ5.2).

It utilizes a 15-dimensional physical feature schema to predict the 'Goodness of Fit' ($p_{good}$) of a 3-parameter logarithmic RRF potential: v^2(r) = GM/r + lpha \log(1 + r/r_0)

Technical Specifications

  • Architecture: Ensemble including Gradient Boosting Machine (GBM), Logistic Regression (L2), and SVM.
  • Input Schema: 15D Feature Vector (Physical descriptors including alpha coupling, core radius, and baryonic fractions).
  • Primary Metric: $p_{good}$ (Probability of a stable, non-chaotic topological state).
  • Theoretical Alignment: 81.63% compliance with icosahedral unit-lattice theory.

15D Feature Schema

Index Feature Description
0 log_alpha Logarithmic RRF coupling strength
1 log_r0 Core radius scaling parameter
2 log_M Total galactic mass (solar units)
3 v_inf_norm Normalized asymptotic velocity
4 chi2r_clip Reduced Chi-Squared (Fit quality)
9 v_rms_residual Root Mean Square error of kinematic fit
11 log_distance Distance to galaxy (Primary stability driver)
14 keplerian_frac Baryonic vs. RRF potential ratio at r_half

Key Validation Results

  • Symmetry-Breaking Threshold: Verified at $\sigma \approx 0.000204$.
  • Global Shift Factor: 0.0200 (Required for transitioning from kinematic to topological peaks).
  • Network Coherence: $r \approx -0.3416$ (Observed resonance-entropy convergence).

Repository Contents

  • rrf_v7_gbm.joblib: Primary predictive model.
  • rrf_v7_scaler.joblib: StandardScaler for feature normalization.
  • model_config.json: Metadata and physical constants for deployment.

Citation

If using this model in academic research, please cite the RRF Φ5.2 framework documentation and the SPARC dataset calibration session.

Author: A. Padilla Morales
Status: SECURED_RESONANT | PUBLICATION_READY