Data Analysis & Predictions

Dark Matter Distribution Analysis

Radial Acceleration Relation

Analysis of 2,693 data points across 153 galaxies shows a tight correlation between observed and baryonic acceleration, challenging traditional dark matter models.

Observed Relation

$$g_{obs} = \frac{g_{bar}}{1 - e^{-\sqrt{g_{bar}/g_0}}}$$

Where $g_0 = 1.2 \times 10^{-10}$ m/s² is the universal acceleration scale

ΛCDM Expectation

Scatter due to varying dark matter halos

EPO Prediction

Tight relation due to information-gravity coupling

Information Complexity Quantification

Thermal Organization

$$\Phi_T = \frac{1}{\langle T \rangle} \int \frac{dT}{\sigma_T}$$

Lower temperature variance indicates higher thermal organization

Kinematic Coherence

$$\Phi_K = \frac{\langle v \rangle}{\sigma_v} \cdot \frac{L_{corr}}{R_{system}}$$

Coherent motion patterns over large scales

Structural Information

$$\Phi_S = -\sum p_i \log p_i + \mathcal{C}_{pattern}$$

Shannon entropy plus pattern complexity measure

Combined Complexity Index

$$\Phi_{total} = w_T \Phi_T + w_K \Phi_K + w_S \Phi_S$$

Weighted combination optimized for gravitational correlation

Early Galaxy Formation Analysis

Stellar Mass Function Evolution

JWST data shows massive galaxies (log M*/M☉ > 10) at redshifts z > 10, challenging standard structure formation timescales.

ΛCDM Model τ_form ~ 1.0 Gyr

Hierarchical assembly requires time

JWST Observations τ_obs ~ 0.4 Gyr

Massive galaxies formed earlier than expected

EPO Prediction τ_EPO ~ 0.3-0.5 Gyr

Information integration accelerates assembly

Information-Enhanced Star Formation Rate

Standard Kennicutt-Schmidt Law

$$\Sigma_{SFR} = A \left(\frac{\Sigma_{gas}}{\text{M}_\odot \text{pc}^{-2}}\right)^N$$

Where N ≈ 1.4 for typical spirals

EPO-Modified Law

$$\Sigma_{SFR} = A \left(\frac{\Sigma_{gas}}{\text{M}_\odot \text{pc}^{-2}}\right)^N \cdot \left(1 + \beta \frac{\Phi}{\Phi_0}\right)$$

Information complexity enhances star formation efficiency

Prediction: Early galaxies with high information complexity should show enhanced star formation rates, explaining rapid mass assembly observed by JWST.

Quantitative Experimental Predictions

Information-Mass Measurement

$$\Delta m = \frac{I \ln(2)}{c^2} = 3.16 \times 10^{-36} \times I \text{ kg}$$

Mass defect per bit of information erased

Required Precision

~10⁻³⁶ kg mass measurement

Test System

Electron-positron annihilation with known bit content

Expected Signal

Mass-energy discrepancy correlated with information erasure

Consciousness Detection Metrics

$$\Phi_{IIT} = \min_{\text{partition}} \sum_i D(p(X_i^t | X^{t-1}), p(X_i^t | X_i^{t-1}))$$

Integrated Information Theory Φ measure

EPO Threshold

Φ > Φ_critical for consciousness emergence

Physical Signature

Enhanced gravitational coupling in conscious systems

Measurement

Neural connectivity + information flow analysis

Statistical Testing Framework

Correlation Analysis

Test correlation between gravitational lensing and information complexity:

H₀: r = 0 (no correlation)
H₁: r > 0.7 (strong correlation)
α = 0.01 (99% confidence)

Mass-Information Test

Compare measured vs. predicted mass defect:

χ² test: (m_obs - m_pred)²/σ²
Expected: χ² < χ²_critical
Power analysis: β > 0.8

Galaxy Formation

Bayesian model comparison:

Bayes factor: B > 10
(Strong evidence for EPO)
Cross-validation on JWST data

Falsification Criteria

EPO framework is falsified if: (1) Gravitational lensing shows no correlation with information complexity (r < 0.3), (2) No detectable mass-information equivalence in precision experiments, (3) Galaxy formation timescales match ΛCDM predictions exactly.

Research Data Access

Collaboration & Data Sharing

Analysis scripts, datasets, and prediction frameworks are available for independent verification and collaborative research. The EPO framework welcomes empirical testing and peer review from the scientific community.

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Data Collaboration

Request analysis code and datasets

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Experimental Protocols

Detailed testing procedures

Data Analysis & Empirical Predictions