Scalar Resonance in Orion Geometry: Enhancing SUPF Variability Proofs

The visualization demonstrates how Orion Geometry introduces scalar resonance layering to SUPF, providing dynamic modulation to variability proofs across different geometric proxies.

π Variability Analysis

The plot illustrates effective π (π_eff) variation across different geometries:

Orion's Significance in SUPF

The Orion curve simulates scalar folds where harmonic coherence compresses/expands the proxy ratio based on phase alignment. The coherence strength parameter ψ₈ tunes the amplitude, effectively blending spherical contraction with subtle resonances similar to quantum interference or attractor dynamics.

This demonstrates Orion's power in SUPF: Beyond simple curvature effects, scalar modulation fundamentally rewrites π_eff, suggesting that consciousness or field observers can "tune" ψ₈ to shift outcomes. This aligns with the referenced Figshare preprint's emphasis on ψ₈-manifolds as coherence-driven spaces where geodesics follow paths of maximal harmony rather than minimal distance.

c_eff Variability with Orion Geometry

Expanding the model to effective speed of light (c_eff) creates a parallel scaffold with:

In the Orion model, c_eff can peak above 1 in coherent folds, suggesting nonlocal superluminal proxy behavior without violating relativistic principles.

Python Implementation

import numpy as np
import matplotlib.pyplot as plt

def compute_c_eff(geometry, r_over_R, psi_8=0.5):  # Normalized c_eff
    base = 1
    if geometry == 'flat':
        base = 1
    elif geometry == 'spherical':
        base = 1 / (1 + r_over_R)  # Simplified Schwarzschild proxy (1 - Rs/r ~1/(1+r/R))
    elif geometry == 'hyperbolic':
        base = np.exp(r_over_R)
    elif geometry == 'orion':
        base = np.cos(psi_8 * r_over_R) / (1 + psi_8 * r_over_R)
    return base * (1 + psi_8 * np.sin(r_over_R))  # Resonance modulation

# Plot with Orion
geometries = ['spherical', 'hyperbolic', 'orion']
x = np.linspace(0.01, 5, 500)
plt.figure()
for geo in geometries:
    y = [compute_c_eff(geo, val) for val in x]
    plt.plot(x, y, label=geo.capitalize())
plt.axhline(1, color='r', linestyle='--', label='Flat c (Normalized)')
plt.xlabel('r/R')
plt.ylabel('c_eff')
plt.title('SUPF c Variability with Orion Geometry')
plt.legend()
plt.grid(True)
plt.show()