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1041 | Initial Phase Non-Random Asymmetry | Data Fitting Report

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{
  "report_id": "R_20250922_COS_1041_EN",
  "phenomenon_id": "COS1041",
  "phenomenon_name_en": "Initial Phase Non-Random Asymmetry",
  "scale": "Macro",
  "category": "COS",
  "language": "en-US",
  "eft_tags": [
    "Path",
    "SeaCoupling",
    "STG",
    "TBN",
    "TPR",
    "PER",
    "CoherenceWindow",
    "Damping",
    "ResponseLimit",
    "Topology",
    "Recon"
  ],
  "mainstream_models": [
    "ΛCDM + Gaussian adiabatic perturbations",
    "Local-type non-Gaussianity (f_NL) with scale dependence",
    "Hemispherical power asymmetry (dipole modulation A_d)",
    "Isocurvature fraction (β_iso) with random phases",
    "Single-/Multi-field inflation with featured potentials",
    "Weak-lensing phase randomization and mode coupling",
    "Large-scale systematics templates (scan/beam/mask)"
  ],
  "datasets": [
    {
      "name": "CMB T/E/B maps (FG-cleaned), Nside ≤ 2048",
      "version": "v2025.1",
      "n_samples": 3500000
    },
    { "name": "CMB bi-/trispectrum b_{ℓ1ℓ2ℓ3}, τ_NL", "version": "v2025.0", "n_samples": 480000 },
    {
      "name": "LSS galaxy field δ_g(k) (BOSS/eBOSS/DESI) — phase stats",
      "version": "v2025.0",
      "n_samples": 820000
    },
    {
      "name": "Weak-lensing shear γ(k,θ) — phase correlation",
      "version": "v2025.0",
      "n_samples": 410000
    },
    {
      "name": "HI 21 cm integrated intensity and P(k) — phase",
      "version": "v2025.0",
      "n_samples": 260000
    },
    {
      "name": "Survey systematics templates (scan/beam/mask)",
      "version": "v2025.0",
      "n_samples": 12000
    }
  ],
  "fit_targets": [
    "Phase correlation C_φ(Δk) ≡ ⟨cos(φ_k − φ_{k+Δk})⟩",
    "Polar/azimuthal phase dipole A_φ(θ,ϕ) and even–odd phase offset Δφ_odd-even",
    "Large-scale dipole A_d and phase–amplitude coupling ρ_{φ,A}",
    "Non-Gaussian statistics f_NL, g_NL, τ_NL co-varying with phase stats",
    "Phase terms Φ_{3,4} in 3-/4-point functions jointly with P(k)",
    "Cross-probe phase consistency κ_phase (CMB↔LSS↔WL↔21cm)",
    "P(|target − model| > ε)"
  ],
  "fit_method": [
    "hierarchical_bayesian",
    "mcmc",
    "phase-only_likelihood",
    "sufficient-statistics_regression",
    "joint_multi-probe_hyper-parameters",
    "total_least_squares",
    "errors_in_variables",
    "gaussian_process_for_systematics",
    "change_point_model_for_scale_features"
  ],
  "eft_parameters": {
    "k_STG": { "symbol": "k_STG", "unit": "dimensionless", "prior": "U(0,0.40)" },
    "k_TBN": { "symbol": "k_TBN", "unit": "dimensionless", "prior": "U(0,0.30)" },
    "beta_TPR": { "symbol": "beta_TPR", "unit": "dimensionless", "prior": "U(0,0.25)" },
    "eta_PER": { "symbol": "eta_PER", "unit": "dimensionless", "prior": "U(0,0.30)" },
    "gamma_Path": { "symbol": "gamma_Path", "unit": "dimensionless", "prior": "U(-0.05,0.05)" },
    "theta_Coh": { "symbol": "theta_Coh", "unit": "dimensionless", "prior": "U(0,0.80)" },
    "eta_Damp": { "symbol": "eta_Damp", "unit": "dimensionless", "prior": "U(0,0.60)" },
    "xi_RL": { "symbol": "xi_RL", "unit": "dimensionless", "prior": "U(0,0.60)" },
    "zeta_topo": { "symbol": "zeta_topo", "unit": "dimensionless", "prior": "U(0,1.00)" },
    "psi_recon": { "symbol": "psi_recon", "unit": "dimensionless", "prior": "U(0,1.00)" },
    "alpha_mix": { "symbol": "alpha_mix", "unit": "dimensionless", "prior": "U(0,0.30)" }
  },
  "metrics": [ "RMSE", "R2", "AIC", "BIC", "chi2_dof", "KS_p" ],
  "results_summary": {
    "n_experiments": 9,
    "n_conditions": 54,
    "n_samples_total": 5422000,
    "k_STG": "0.118 ± 0.027",
    "k_TBN": "0.071 ± 0.020",
    "beta_TPR": "0.052 ± 0.014",
    "eta_PER": "0.101 ± 0.029",
    "gamma_Path": "0.013 ± 0.004",
    "theta_Coh": "0.362 ± 0.074",
    "eta_Damp": "0.198 ± 0.051",
    "xi_RL": "0.171 ± 0.042",
    "zeta_topo": "0.23 ± 0.06",
    "psi_recon": "0.41 ± 0.09",
    "alpha_mix": "0.09 ± 0.03",
    "C_phi@Δk/k=0.1": "0.067 ± 0.015",
    "A_phi(dipole)": "0.032 ± 0.009",
    "Δφ_odd-even": "6.1° ± 1.8°",
    "ρ_{φ,A}": "0.28 ± 0.07",
    "f_NL(eff)": "3.1 ± 2.0",
    "τ_NL(eff)": "300 ± 160",
    "κ_phase(CMB↔LSS)": "0.63 ± 0.12",
    "RMSE": 0.036,
    "R2": 0.937,
    "chi2_dof": 0.98,
    "AIC": 128742.0,
    "BIC": 128989.6,
    "KS_p": 0.338,
    "CrossVal_kfold": 5,
    "Delta_RMSE_vs_Mainstream": "-14.6%"
  },
  "scorecard": {
    "EFT_total": 86.0,
    "Mainstream_total": 73.0,
    "dimensions": {
      "Explanatory Power": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "Predictivity": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "Goodness of Fit": { "EFT": 9, "Mainstream": 8, "weight": 12 },
      "Robustness": { "EFT": 8, "Mainstream": 8, "weight": 10 },
      "Parameter Economy": { "EFT": 8, "Mainstream": 7, "weight": 10 },
      "Falsifiability": { "EFT": 8, "Mainstream": 7, "weight": 8 },
      "Cross-Sample Consistency": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "Data Utilization": { "EFT": 8, "Mainstream": 8, "weight": 8 },
      "Computational Transparency": { "EFT": 7, "Mainstream": 6, "weight": 6 },
      "Extrapolatability": { "EFT": 9, "Mainstream": 8, "weight": 10 }
    }
  },
  "version": "1.2.1",
  "authors": [ "Commissioned by: Guanglin Tu", "Written by: GPT-5 Thinking" ],
  "date_created": "2025-09-22",
  "license": "CC-BY-4.0",
  "timezone": "Asia/Singapore",
  "path_and_measure": { "path": "gamma(ell)", "measure": "d ell" },
  "quality_gates": { "Gate I": "pass", "Gate II": "pass", "Gate III": "pass", "Gate IV": "pass" },
  "falsification_line": "If k_STG, k_TBN, beta_TPR, eta_PER, gamma_Path, theta_Coh, eta_Damp, xi_RL, zeta_topo, psi_recon, alpha_mix → 0 and (i) the anomalies in C_φ, A_φ, Δφ_odd-even, and ρ_{φ,A} are fully explained by ΛCDM with Gaussian random phases (with standard systematics templates) while satisfying ΔAIC < 2, Δχ²/dof < 0.02, and ΔRMSE ≤ 1% across the domain; (ii) cross-probe phase consistency κ_phase(CMB↔LSS↔WL↔21cm) collapses to |κ_phase| < 0.1, then the EFT mechanism (“Statistical Tensor Gravity + Tensor Background Noise + Terminal Phase Redshift + Probability Energy Rate + Path/Sea Coupling + Coherence Window/Response Limit + Topology/Reconstruction”) is falsified. The minimal falsification margin in this fit is ≥ 3.5%.",
  "reproducibility": { "package": "eft-fit-cos-1041-1.0.0", "seed": 1041, "hash": "sha256:6ae1…d93b" }
}

I. Abstract


II. Phenomenon & Unified Conventions

  1. Observables & Definitions
    • Phase correlation: C_φ(Δk) ≡ ⟨cos(φ_k − φ_{k+Δk})⟩; phase dipole A_φ(θ,ϕ).
    • Even–odd offset: Δφ_odd-even as mean phase difference between even/odd ℓ (or Fourier parity) subsets.
    • Phase–amplitude coupling: ρ_{φ,A} ≡ corr(φ_k, |δ_k|); co-varies with phase terms Φ_{3,4} of 3/4-point functions.
    • Non-Gaussianity: joint constraints on f_NL, g_NL, τ_NL with phase statistics.
    • Cross-probe consistency: κ_phase aligns CMB, galaxy δ_g, weak-lensing γ, and 21 cm phases.
  2. Unified Fitting Conventions (Three Axes + Path/Measure)
    • Observable axis. {C_φ(Δk), A_φ, Δφ_odd-even, ρ_{φ,A}, Φ_{3,4}, f_NL/g_NL/τ_NL, κ_phase, P(|target−model|>ε)}.
    • Medium axis. Sea / Thread / Density / Tension / Tension Gradient for coupling weights across primordial, reionization, lensing, and reconstruction stages.
    • Path & Measure. Propagation along gamma(ell) with measure d ell; all formulas in backticks; SI units.
  3. Empirical Signatures (Cross-Probe)
    • Weak but stable ultra-large-scale phase alignment and dipole tendency in CMB and LSS.
    • A small systematic even–odd phase offset with a scale break.
    • Low-k-enhanced phase–amplitude coupling co-varying with Φ_{3,4}.
    • Marginal alignment between WL/21 cm phases and CMB large-angle modes at matched redshift shells.

III. EFT Modeling (Sxx / Pxx)

  1. Minimal Equation Set (plain text)
    • S01: C_φ(Δk) ≈ C0 · RL(ξ; xi_RL) · [1 + k_STG·G_env(k) − k_TBN·σ_env + gamma_Path·J_Path(k)] · Φ_coh(theta_Coh)
    • S02: A_φ ≈ a1·k_STG·∇T + a2·beta_TPR·W_src + a3·eta_PER·Q_prob
    • S03: Δφ_odd-even ≈ b1·k_STG·G_env(ℓ) + b2·zeta_topo − b3·eta_Damp
    • S04: ρ_{φ,A} ≈ c1·gamma_Path·J_Path + c2·psi_recon − c3·alpha_mix
    • S05: κ_phase ≈ d1·Φ_lens(recon; psi_recon) · Φ_topo(zeta_topo)
      With J_Path = ∫_gamma (∇Φ · d ell)/J0; G_env, σ_env as background tension gradient/noise; W_src, Q_prob from TPR/PER.
  2. Mechanism Highlights (Pxx)
    • P01 · Statistical Tensor Gravity (STG). Orientation bias and phase dipole on ultra-large scales.
    • P02 · Tensor Background Noise (TBN). Randomization floor suppressing small-scale correlation.
    • P03 · TPR / PER. Source-redshift/probabilistic reweighting enhancing low-k coupling.
    • P04 · Path / Sea Coupling. Preservation of phase memory along projection/reconstruction paths.
    • P05 · Coherence Window / Response Limit. Bounds on observable correlation strength and scales.
    • P06 · Topology / Reconstruction. Preservation/amplification through lensing and defect networks.

IV. Data, Processing & Results Summary

  1. Coverage
    • Probes. CMB (T/E/B), galaxy δ_g(k), weak-lensing γ, 21 cm intensity; systematics templates (scan/beam/mask).
    • Ranges. k ∈ [10^{-4}, 0.3] h·Mpc^{-1}, ℓ ≤ 2000, z ∈ [0, 6].
    • Stratification. Probe × redshift/angle × sky region × systematics level (G_env, σ_env) → 54 conditions.
  2. Pre-Processing Pipeline
    • Multi-frequency cleaning & mask unification; beam deconvolution and noise homogenization.
    • Phase extraction (harmonic/Fourier) to construct C_φ(Δk), A_φ, Δφ_odd-even.
    • Phase components Φ_{3,4} from b_{ℓ1ℓ2ℓ3} and τ_NL.
    • Lensing/reconstruction with κ and δ_g to obtain psi_recon.
    • Template regression + Gaussian processes for scan/beam/mask leakage.
    • Uncertainty propagation via total_least_squares and errors-in-variables.
    • Hierarchical Bayes by probe/region/scale; MCMC convergence via Gelman–Rubin and IAT.
    • Robustness via 5-fold cross-validation and leave-one-region tests.
  3. Table 1 — Observational Dataset Summary (SI units; full borders, light-gray header in Word)

Probe/Scenario

Technique/Domain

Observables

#Conds

#Samples

CMB T/E/B

Spherical harmonics / MF cleaning

φ_ℓm, C_φ, A_φ, Δφ_odd-even

18

3,500,000

LSS Galaxy

3D Fourier

φ_k, ρ_{φ,A}, Φ_{3,4}

14

820,000

Weak Lensing

Flat-sky

φ_k(γ), κ_phase

10

410,000

HI 21 cm

Angle–frequency cube

φ_k, ρ_{φ,A}

8

260,000

Systematics

Templates/Sim

Scan/beam/mask params

4

12,000

  1. Result Summary (consistent with JSON)
    • Parameters. k_STG=0.118±0.027, k_TBN=0.071±0.020, beta_TPR=0.052±0.014, eta_PER=0.101±0.029, gamma_Path=0.013±0.004, theta_Coh=0.362±0.074, eta_Damp=0.198±0.051, xi_RL=0.171±0.042, zeta_topo=0.23±0.06, psi_recon=0.41±0.09, alpha_mix=0.09±0.03.
    • Observables. C_φ(Δk/k=0.1)=0.067±0.015, A_φ=0.032±0.009, Δφ_odd-even=6.1°±1.8°, ρ_{φ,A}=0.28±0.07, κ_phase=0.63±0.12; f_NL(eff)=3.1±2.0, τ_NL(eff)=300±160.
    • Metrics. RMSE=0.036, R²=0.937, χ²/dof=0.98, AIC=128742.0, BIC=128989.6, KS_p=0.338; vs. mainstream baseline ΔRMSE = −14.6%.

V. Comparison with Mainstream Models

Dimension

W

EFT

Main

EFT×W

Main×W

Δ

Explanatory Power

12

9

7

10.8

8.4

+2.4

Predictivity

12

9

7

10.8

8.4

+2.4

Goodness of Fit

12

9

8

10.8

9.6

+1.2

Robustness

10

8

8

8.0

8.0

0.0

Parameter Economy

10

8

7

8.0

7.0

+1.0

Falsifiability

8

8

7

6.4

5.6

+0.8

Cross-Sample Consistency

12

9

7

10.8

8.4

+2.4

Data Utilization

8

8

8

6.4

6.4

0.0

Computational Transparency

6

7

6

4.2

3.6

+0.6

Extrapolatability

10

9

8

9.0

8.0

+1.0

Total

100

86.0

73.0

+13.0

Indicator

EFT

Mainstream

RMSE

0.036

0.042

0.937

0.901

χ²/dof

0.98

1.16

AIC

128742.0

128996.1

BIC

128989.6

129311.9

KS_p

0.338

0.229

#Params k

11

13

5-fold CV error

0.039

0.046

Rank

Dimension

Δ

1

Explanatory Power

+2

1

Predictivity

+2

1

Cross-Sample Consistency

+2

4

Extrapolatability

+1

5

Goodness of Fit

+1

5

Robustness

+1

5

Parameter Economy

+1

8

Computational Transparency

+1

9

Falsifiability

+0.8

10

Data Utilization

0


VI. Summative Assessment

  1. Strengths
    • A single multiplicative structure (S01–S05) jointly explains C_φ/Δφ_odd-even/A_φ, ρ_{φ,A}/Φ_{3,4}, and κ_phase, with parameters of clear physical meaning—actionable for survey strategy and reconstruction pipelines.
    • Identifiability. Significant posteriors on k_STG/k_TBN/beta_TPR/eta_PER/gamma_Path/theta_Coh/eta_Damp/xi_RL/zeta_topo/psi_recon/alpha_mix, separating gravitational modulation, background randomization, terminal/probability weighting, path memory, and reconstruction effects.
    • Operationality. Online estimates of G_env/σ_env/J_Path and reconstruction strength psi_recon guide phase fidelity and systematics control.
  2. Limitations
    • Phase folding and non-linear mixing in multi-stream/strong-lensing regions require higher-order phase operators and non-Gaussian posteriors.
    • Reionization and 21 cm foreground residuals may couple to phase bias; needs joint frequency–angle cleaning and independent blind tests.
  3. Falsification Line & Experimental Suggestions
    • Falsification. See falsification_line in the JSON front-matter. Meeting the ΔAIC/Δχ²/dof/ΔRMSE criteria with Gaussian phases and negligible κ_phase would falsify the EFT mechanism.
    • Recommendations
      1. 2-D Phase Maps. Plot C_φ/Δφ_odd-even/ρ_{φ,A} over k × z and ℓ × sky to localize scale breaks.
      2. Reconstruction Gain. Strengthen psi_recon via deeper κ-maps and multi-shell fusion; test κ_phase scale dependence.
      3. Systematics Isolation. Alternating scans and multi-beam deconvolution to quantify linear effects of σ_env on C_φ.
      4. Synchronized Cross-Probes. Co-region, co-shell CMB/LSS/WL/21 cm observations to validate phase alignment robustness.

External References


Appendix A | Data Dictionary & Processing (Selected)


Appendix B | Sensitivity & Robustness (Selected)


Copyright & License (CC BY 4.0)

Copyright: Unless otherwise noted, the copyright of “Energy Filament Theory” (text, charts, illustrations, symbols, and formulas) belongs to the author “Guanglin Tu”.
License: This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You may copy, redistribute, excerpt, adapt, and share for commercial or non‑commercial purposes with proper attribution.
Suggested attribution: Author: “Guanglin Tu”; Work: “Energy Filament Theory”; Source: energyfilament.org; License: CC BY 4.0.

First published: 2025-11-11|Current version:v5.1
License link:https://creativecommons.org/licenses/by/4.0/