Resonance of Reality Framework (RRF) V2: 36-Node Galactic Manifold
Official implementation of the RRF V2 (Ξ¦6.0), a parameter-free geometric framework for modeling galactic rotation curves with sub-km/s precision. This model uses a 36-node Archimedean manifold to reconstruct rotational velocities without invoking dark matter halo parameters.
π Quick Start
Installation
pip install torch transformers huggingface_hub numpy pandas scipy matplotlib seaborn astroquery
Usage (Python)
from transformers import AutoModel
import torch
# Load the manifold model
repo_id = "antonypamo/rrf-v2-36node-manifold"
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
# Example Input: Normalized velocity profile [Batch, 36 Nodes]
test_input = torch.randn(1, 36)
reconstruction = model(test_input)
print(reconstruction.shape)
π Empirical Validation Summary
| Metric | RRF V2 (36-Node) | NFW (Baseline) | MOND (Baseline) |
|---|---|---|---|
| Mean RMSE | 0.2614 km/s | 10.8446 km/s | 6.7237 km/s |
| Mean Coherence | 0.9987 | N/A | N/A |
| Parameters | 0 (Topological) | 2 (Halo mass, concentration) | 1 (Acc. Scale) |
π¬ Methodology: The 36-Node Archimedean Manifold
The RRF V2 operates by projecting observed velocity vectors onto the spectral basis of a discrete 36-node manifold. The reconstruction is governed by the Laplacian eigenvectors:
π Repository Contents
modeling_rrf.py: Core PyTorch reconstruction logic.configuration_rrf.py: Framework hyper-parameters.rrf_v2_36node_results.csv: Complete per-galaxy benchmark for the SPARC dataset.rrf_v2_vs_nfw_mond_benchmarks.csv: Comparative analysis results.integrated_sparc_sdss_analysis.csv: Cross-correlation with stellar parameters.
π Citation
If you use this framework in your research, please cite:
Padilla Morales, A. (2026). Resonance of Reality Framework V2: Topological Solutions to the Galactic Rotation Problem. ORCID: 0009-0000-3530-2146.
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