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.

πŸš€ 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)

πŸ“Š 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)

πŸ”¬ 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: Vrecon=βˆ‘i=136⟨Vobs,ψi⟩ψiV_{recon} = \sum_{i=1}^{36} \langle V_{obs}, \psi_i \rangle \psi_i

πŸ“‚ 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.

πŸ“œ 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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