antonypamo/RRFV2_42nodes

RRF ̄5.2 (v2) - 42-Node Geodesic Manifold

Resonance of Reality Framework (RRF) is a custom topological architecture for modeling galactic rotation curves with high precision. Version 5.2 (v2) utilizes a 42-node geodesic manifold and Radial Normalization ($R/R_{max}$) to achieve scale-invariant fitting.

Model Description

Unlike standard parametric models like the Navarro-Frenk-White (NFW) profile, RRF maps galactic rotation data onto a high-resolution geodesic sphere. This allows the model to capture complex dynamical features without assuming a specific dark matter density profile.

Key Features:

• Manifold Resolution: 42-node Geodesic Subdivision (v=2).
• Scale Invariance: Invariant to galactic size via radial normalization.
• Morphology Independent: Validated across Spirals, Dwarfs, and LSB galaxies ($p = 0.9019$).

Performance Benchmarks

On the SPARC dataset (175 galaxies):

• Accuracy: Mean RMSE of 0.41 km/s.
• Coherence: Mean coherence of 0.9940.
• Vs. NFW: Outperforms standard NFW models by a factor of 16.2x.

Usage

This model uses custom code on the Hugging Face Hub. To use it, ensure you have transformers and torch installed.

from transformers import AutoModel
import torch

# Load the RRF v2 Framework
model = AutoModel.from_pretrained("antonypamo/RRFV2_42nodes", trust_remote_code=True)

# Analyze a rotation curve
# r_phys: radius array, v_obs: velocity array
v_recon = model.analyze_galaxy_invariant(r_phys, v_obs)

Stability

Ridge analysis confirms topological robustness with a stability coefficient of -0.00059, showing graceful degradation under observational noise.

Citation

If you use this framework in your research, please cite: A. Padilla Morales (ORCID: 0009-0000-3530-2146)

@misc{antony_padilla_morales_2026, author = { Antony Padilla Morales }, title = { RRFV2_42nodes (Revision 8242196) }, year = 2026, url = { https://huggingface.co/antonypamo/RRFV2_42nodes }, doi = { 10.57967/hf/8846 }, publisher = { Hugging Face } }