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.

python

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 } }