Experiment 3: The Absolute Frame (The Hidden Variable)

Credits and References

Darwin's Cage Theory:

• Theory Creator: Gideon Samid
• Reference: Samid, G. (2025). Negotiating Darwin's Barrier: Evolution Limits Our View of Reality, AI Breaks Through. Applied Physics Research, 17(2), 102. https://doi.org/10.5539/apr.v17n2p102
• Publication: Applied Physics Research; Vol. 17, No. 2; 2025. ISSN 1916-9639 E-ISSN 1916-9647. Published by Canadian Center of Science and Education
• Available at: https://www.researchgate.net/publication/396377476_Negotiating_Darwin's_Barrier_Evolution_Limits_Our_View_of_Reality_AI_Breaks_Through

Experiments, AI Models, Architectures, and Reports:

• Author: Francisco Angulo de Lafuente
• Responsibilities: Experimental design, AI model creation, architecture development, results analysis, and report writing

Abstract

This experiment tests whether a holographic optical network can detect "Absolute Velocity" encoded in the quantum phase of spectral data, a variable that is theoretically undetectable by standard intensity-based instruments according to relativity. The experiment aims to determine if phase information, typically discarded by conventional measurement, contains physically meaningful "hidden variables."

Objective

To investigate if complex-valued optical processing can extract velocity information from quantum phase noise that is invisible to standard spectrometric measurements (which detect only intensity, $|A|^2$).

Hypothesis

Standard scientific instruments collapse the quantum wavefunction by measuring intensity, discarding phase information. If "absolute velocity" interacts with the quantum vacuum to modulate the phase (but not amplitude) of atomic emissions, a holographic processor that preserves complex fields through interference might detect this hidden signal.

Methodology

1. The Simulator

We simulate hydrogen-like spectral emissions where:

• Amplitude: Random Gaussian noise (thermal), velocity-independent.
• Phase: $\phi = \phi_{noise} + f(v, \nu)$
  • $\phi_{noise}$: Random uniform noise $\in [0, 0.1]$
  • $f(v, \nu) = \frac{v}{1000} \cdot \nu$: Velocity-dependent phase shift (linear encoding)

Critical Note: Initial simulator design had excessive noise ($\phi_{noise} \in [0, 2\pi]$) and used $\sin(\nu)$ encoding, causing signal cancellation. After diagnostic analysis (correlation $\approx 0.01$), we fixed the signal-to-noise ratio. Final correlation: $|r| \approx 0.47$.

2. The Darwinian Observer (Control)

• Measures intensity only: $I = |A|^2$
• Uses MLP Regressor to predict velocity from intensity
• Expected Result: Failure (phase info is lost)

3. The Holographic Aether Net (Experimental)

1. Complex Projection: $\mathbf{F} = \mathbf{X}_{complex} \times \mathbf{W}_{optical}$ (preserves phase)
2. Interference via FFT: $\mathbf{H} = \text{FFT}(\mathbf{F})$ (phase → amplitude conversion)
3. Detection: $\mathbf{S} = |\mathbf{H}|$ (now amplitude contains original phase info)
4. Readout: Ridge Regression on $\mathbf{S}$

Results

Standard Performance

Benchmark & Critical Audit

1. Standard Detection

• Result: R² = 0.9998 ✅
• Conclusion: Model detects the hidden variable with high precision.

2. Phase Scrambling (The Litmus Test)

• Protocol: Randomize phase while keeping amplitude unchanged.
• Result: R² = -0.14 ✅
• Conclusion: Model relies 100% on phase information. When phase is destroyed, detection fails completely, proving the model does not "cheat" by using amplitude correlations.

3. Extrapolation

• Protocol: Train on $v < 700$, Test on $v > 700$.
• Result: R² = -1.99 ❌
• Conclusion: Model does not generalize to unseen velocity ranges. It behaves as a local interpolator rather than discovering a universal physical law.

Scientific Interpretation

What We Proved

1. Phase Detection Works: The holographic architecture successfully converts phase information to amplitude through interference, enabling detection via magnitude measurements.
2. Signal Dependence: The model genuinely relies on phase (proven by scrambling test).
3. Cage Status: 🔓 BROKEN (within training domain). The system detects information invisible to standard instruments.

Limitations

1. No Generalization: Unlike Experiment 2 (Relativity), this model fails to extrapolate, suggesting it memorized the velocity-phase mapping rather than learning an underlying physical law.
2. Simulator Dependency: Results are contingent on the simulator's assumption that velocity modulates phase linearly. Real quantum systems may behave differently.

Comparison Across Experiments

*Only within training distribution.

Files

• experiment_3_absolute_frame.py: Main experiment code.
• benchmark_experiment_3.py: Audit script (phase scrambling, extrapolation).
• diagnostic_phase.py: Correlation analysis of phase signal.
• experiment_3_absolute_frame.png: Performance visualization.
• benchmark_3_results.png: Benchmark results.

Reproduction

python experiment_3_absolute_frame.py
python benchmark_experiment_3.py

Conclusion

Experiment 3 demonstrates that phase-based information can be extracted via optical interference, but the model's inability to generalize suggests it did not discover a fundamental physical principle. The experiment validates the technical feasibility of "cage-breaking" (accessing hidden information) while raising questions about the distinction between interpolation and true physical understanding.