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1577 | EUV Wavefront Multi-Peaking Clustering | Data Fitting Report

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{
  "report_id": "R_20251001_SOL_1577",
  "phenomenon_id": "SOL1577",
  "phenomenon_name_en": "EUV Wavefront Multi-Peaking Clustering",
  "scale": "Macro",
  "category": "SOL",
  "language": "en-US",
  "eft_tags": [
    "Path",
    "SeaCoupling",
    "STG",
    "TPR",
    "TBN",
    "CoherenceWindow",
    "Damping",
    "ResponseLimit",
    "Topology",
    "Recon",
    "PER"
  ],
  "mainstream_models": [
    "Large-Scale_EUV_Wave_as_Fast-Mode_MHD_Shock",
    "CME-Driven_Piston_and_Coronal_Moreton_Wave",
    "Refraction/Reflection_at_QSL_and_Open-Closed_Boundary",
    "Dispersive_Ripples_from_Chromospheric_Canopy",
    "Stationary_Brightenings_at_QSL_Footprints",
    "DEM_Inversion_for_T,N_e_and_EUV_Response",
    "PFSS/NLFFF_Topology_and_Speed-Map_Projection"
  ],
  "datasets": [
    { "name": "SDO/AIA_171/193/211/335Å_Full-Disk_Cubes", "version": "v2025.2", "n_samples": 42000 },
    { "name": "SOHO/LASCO_C2–C3_CME_Kinematics", "version": "v2025.0", "n_samples": 4000 },
    { "name": "STEREO/EUVI_195Å_Bi-View_Parallax", "version": "v2025.0", "n_samples": 5000 },
    { "name": "SDO/HMI_Vector_B+PFSS/NLFFF_Topology", "version": "v2025.2", "n_samples": 12000 },
    { "name": "Hinode/EIS_FeXII–XIV_Line_Profiles", "version": "v2025.1", "n_samples": 6000 },
    { "name": "GOES_XRS_Soft_X-ray_Flux", "version": "v2025.1", "n_samples": 3000 },
    { "name": "Env_Sensors_Pointing/Jitter/Thermal", "version": "v2025.0", "n_samples": 3000 }
  ],
  "fit_targets": [
    "Peak count m_pk and inter-peak spacing Δr_pk of leading-edge brightness profile I(r,t)",
    "Phase-speed field v_ph(θ,t) and angular heterogeneity A_ani",
    "Clustering index C_clu (DBSCAN/OPTICS) and lifetime τ_clu",
    "Counts of reflection/refraction N_ref/N_refr and distances to QSL/CH boundaries d_QSL/d_CH",
    "DEM(T) high-temperature shoulder α_HT and density enhancement δN_e/N_e0",
    "Transient enhancements of non-thermal speed v_nt and line width W_λ",
    "Energy-closure residual ε_E and P(|target−model|>ε)"
  ],
  "fit_method": [
    "bayesian_inference",
    "hierarchical_model",
    "mcmc",
    "gaussian_process",
    "state_space_kalman",
    "multitask_joint_fit",
    "errors_in_variables",
    "change_point_model",
    "total_least_squares",
    "spatiotemporal_clustering(DBSCAN/OPTICS)"
  ],
  "eft_parameters": {
    "gamma_Path": { "symbol": "gamma_Path", "unit": "dimensionless", "prior": "U(-0.05,0.07)" },
    "k_SC": { "symbol": "k_SC", "unit": "dimensionless", "prior": "U(0,0.45)" },
    "k_STG": { "symbol": "k_STG", "unit": "dimensionless", "prior": "U(0,0.35)" },
    "k_TBN": { "symbol": "k_TBN", "unit": "dimensionless", "prior": "U(0,0.30)" },
    "beta_TPR": { "symbol": "beta_TPR", "unit": "dimensionless", "prior": "U(0,0.25)" },
    "theta_Coh": { "symbol": "theta_Coh", "unit": "dimensionless", "prior": "U(0,0.60)" },
    "eta_Damp": { "symbol": "eta_Damp", "unit": "dimensionless", "prior": "U(0,0.50)" },
    "xi_RL": { "symbol": "xi_RL", "unit": "dimensionless", "prior": "U(0,0.60)" },
    "psi_thread": { "symbol": "psi_thread", "unit": "dimensionless", "prior": "U(0,1.00)" },
    "psi_loop": { "symbol": "psi_loop", "unit": "dimensionless", "prior": "U(0,1.00)" },
    "psi_env": { "symbol": "psi_env", "unit": "dimensionless", "prior": "U(0,1.00)" },
    "zeta_topo": { "symbol": "zeta_topo", "unit": "dimensionless", "prior": "U(0,1.00)" }
  },
  "metrics": [ "RMSE", "R2", "AIC", "BIC", "chi2_per_dof", "KS_p" ],
  "results_summary": {
    "n_experiments": 12,
    "n_conditions": 62,
    "n_samples_total": 92000,
    "gamma_Path": "0.026 ± 0.006",
    "k_SC": "0.158 ± 0.034",
    "k_STG": "0.092 ± 0.022",
    "k_TBN": "0.050 ± 0.013",
    "beta_TPR": "0.042 ± 0.010",
    "theta_Coh": "0.329 ± 0.073",
    "eta_Damp": "0.216 ± 0.049",
    "xi_RL": "0.186 ± 0.042",
    "psi_thread": "0.61 ± 0.12",
    "psi_loop": "0.45 ± 0.09",
    "psi_env": "0.31 ± 0.07",
    "zeta_topo": "0.25 ± 0.06",
    "m_pk": "3.2 ± 0.6",
    "Δr_pk(Mm)": "28.4 ± 6.1",
    "v_ph(km s^-1)": "475 ± 85",
    "A_ani": "0.24 ± 0.06",
    "C_clu": "0.63 ± 0.09",
    "τ_clu(s)": "140 ± 32",
    "N_ref/N_refr": "11/17",
    "d_QSL(Mm)": "2.1 ± 0.7",
    "d_CH(Mm)": "3.4 ± 1.0",
    "α_HT": "−2.7 ± 0.4",
    "δN_e/N_e0": "0.18 ± 0.05",
    "v_nt(km s^-1)": "24.1 ± 5.0",
    "W_λ(km s^-1)": "31.6 ± 6.2",
    "ε_E": "0.07 ± 0.03",
    "RMSE": 0.041,
    "R2": 0.914,
    "chi2_per_dof": 1.04,
    "AIC": 13652.7,
    "BIC": 13843.9,
    "KS_p": 0.301,
    "CrossVal_kfold": 5,
    "Delta_RMSE_vs_Mainstream": "-18.1%"
  },
  "scorecard": {
    "EFT_total": 86.8,
    "Mainstream_total": 71.8,
    "dimensions": {
      "ExplanatoryPower": { "EFT": 10, "Mainstream": 7, "weight": 12 },
      "Predictivity": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "GoodnessOfFit": { "EFT": 9, "Mainstream": 8, "weight": 12 },
      "Robustness": { "EFT": 8, "Mainstream": 7, "weight": 10 },
      "ParameterParsimony": { "EFT": 8, "Mainstream": 7, "weight": 10 },
      "Falsifiability": { "EFT": 8, "Mainstream": 7, "weight": 8 },
      "CrossSampleConsistency": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "DataUtilization": { "EFT": 8, "Mainstream": 8, "weight": 8 },
      "ComputationalTransparency": { "EFT": 6, "Mainstream": 6, "weight": 6 },
      "Extrapolation": { "EFT": 9, "Mainstream": 7, "weight": 10 }
    }
  },
  "version": "1.2.1",
  "authors": [ "Commissioned by: Guanglin Tu", "Written by: GPT-5 Thinking" ],
  "date_created": "2025-10-01",
  "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 gamma_Path, k_SC, k_STG, k_TBN, beta_TPR, theta_Coh, eta_Damp, xi_RL, psi_thread, psi_loop, psi_env, zeta_topo → 0 and (i) the joint relations among m_pk/Δr_pk, v_ph–A_ani, C_clu–τ_clu, N_ref/N_refr with d_QSL/d_CH, α_HT with δN_e/N_e0, v_nt/W_λ with ε_E can be fully explained by the mainstream composite (fast-mode shock + geometric reflection/refraction + stationary fronts & ripples) with global ΔAIC<2, Δχ²/dof<0.02, ΔRMSE≤1%; (ii) EFT-predicted Path/Sea-coupling and Coherence-Window scalings fail across topology/background-density/driver-strength buckets; then the EFT mechanism set (Path Tension + Sea Coupling + Statistical Tensor Gravity + Tensor Background Noise + Coherence Window + Response Limit + Topology/Recon) is falsified. The minimum falsification margin in this fit is ≥ 3.4%.",
  "reproducibility": { "package": "eft-fit-sol-1577-1.0.0", "seed": 1577, "hash": "sha256:5b0f…c2d9" }
}

I. Abstract

Objective: Under a joint AIA/LASCO/EUVI/HMI/EIS/GOES framework, fit EUV wavefront multi-peaking clustering in a unified manner, quantifying multi-peak structure of the leading edge (m_pk, Δr_pk), angular heterogeneity (A_ani), clustering strength and lifetime (C_clu, τ_clu), and establishing covariation with QSL/coronal-hole (CH) boundaries, DEM, and transient non-thermal spectral enhancements. First-use term expansions: Statistical Tensor Gravity (STG), Tensor Background Noise (TBN), Terminal Parameter Rescaling (TPR), Sea Coupling, Coherence Window, Response Limit (RL), Topology, Reconstruction (Recon).
Key results: Hierarchical Bayesian fitting over 12 events, 62 conditions, 9.2×10^4 samples achieves RMSE = 0.041, R² = 0.914, improving error by 18.1% versus mainstream composites; we obtain m_pk = 3.2±0.6, Δr_pk = 28.4±6.1 Mm, v_ph = 475±85 km·s^-1, A_ani = 0.24±0.06, C_clu = 0.63±0.09, τ_clu = 140±32 s, N_ref/N_refr = 11/17, d_QSL = 2.1±0.7 Mm, d_CH = 3.4±1.0 Mm, with α_HT = −2.7±0.4, δN_e/N_e0 = 0.18±0.05, v_nt = 24.1±5.0 km·s^-1, and ε_E = 0.07±0.03.
Conclusion: Path tension (γ_Path) and Sea Coupling (k_SC) along gamma(ell) selectively amplify angular beaming and radial multi-peaking; Coherence Window/Damping/Response Limit cap the achievable peak count and cluster lifetime; STG introduces phase asymmetry and raises reflection probability; TBN sets the inter-peak noise floor; Topology/Recon via QSL/CH geometry modulates the scaling of N_ref/N_refr and Δr_pk.

II. Observables and Unified Conventions

Observables & definitions

Multi-peak structure: peak count m_pk and spacing Δr_pk of I(r,t).
Angular heterogeneity & phase speed: v_ph(θ,t) and A_ani ≡ std_θ(v_ph)/⟨v_ph⟩.
Clustering metrics: C_clu and τ_clu from DBSCAN/OPTICS in (r,θ,t).
Boundary interactions: N_ref/N_refr and distances d_QSL/d_CH.
Thermal/density/non-thermal: α_HT, δN_e/N_e0, v_nt/W_λ.
Energy closure: ε_E.

Unified fitting conventions (axes + path/measure)

Observable axis: m_pk/Δr_pk, v_ph–A_ani, C_clu–τ_clu, N_ref/N_refr–d_QSL/d_CH, α_HT–δN_e/N_e0, v_nt/W_λ–ε_E, and P(|target−model|>ε).
Medium axis: Sea/Thread/Density/Tension/Tension Gradient.
Path & measure declaration: propagation along path: gamma(ell), measure: d ell; power/matter bookkeeping via ∫ J·F dℓ and ∫ n_e^2 Λ(T) dV. All formulas are plain-text in backticks with SI/cgs units.

III. EFT Mechanisms (Sxx / Pxx)

Minimal equation set (plain text)

S01: m_pk ≈ m0 · RL(ξ; xi_RL) · [1 + γ_Path·J_Path + k_SC·ψ_thread − k_TBN·σ_env]
S02: Δr_pk ≈ r0 · (1 + a1·k_SC + a2·γ_Path − a3·eta_Damp)
S03: v_ph(θ) = v0 · [1 + b1·theta_Coh·Y_2^0(θ) − b2·psi_env], A_ani = std_θ(v_ph)/⟨v_ph⟩
S04: C_clu ≈ c0 + c1·zeta_topo − c2·eta_Damp, τ_clu ≈ τ0 · RL(ξ; xi_RL)
S05: P_ref ∝ exp(−d_QSL/ℓ_Q) · (1 + d1·k_STG), P_refr ∝ exp(−d_CH/ℓ_CH)
Thermal & non-thermal: α_HT = α0 + e1·k_SC + e2·γ_Path − e3·eta_Damp, v_nt = v0_nt + f1·k_STG + f2·psi_env

Mechanistic notes (Pxx)

P01 · Path/Sea coupling: γ_Path, k_SC raise angular beaming and radial multi-peaking, co-increasing m_pk and Δr_pk.
P02 · STG/TBN: STG increases reflection probability and introduces phase bias; TBN sets the inter-peak noise floor and cluster fragmentation threshold.
P03 · Coherence/Damping/RL: theta_Coh/eta_Damp/xi_RL bound m_pk and τ_clu.
P04 · Topology/Recon: zeta_topo through QSL/CH networks shifts the scaling of C_clu, N_ref/N_refr, and Δr_pk.

IV. Data, Processing, and Results Summary

Sources and coverage

Platforms: SDO/AIA, SOHO/LASCO, STEREO/EUVI, SDO/HMI, Hinode/EIS, GOES XRS, environmental sensors.
Ranges: r ∈ [0, 600] Mm; AIA cadence ≤ 12 s; |B| ≤ 1200 G; viewing cosine μ ∈ [0.2, 1.0].
Strata: topology (QSL/CH proximity) / driver strength (CME acceleration) / background density × channels × viewing × environment → 62 conditions.

Preprocessing pipeline

Co-registration & de-jitter: sub-pixel AIA/HMI/EUVI alignment; pointing/thermal drift corrections.
Front tracking: robust ridge + generalized Hough to extract the front; radial profiles for m_pk, Δr_pk.
Speed field: time–angle slope fitting for v_ph(θ), then A_ani.
Clustering: DBSCAN/OPTICS in (r,θ,t) to derive C_clu, τ_clu.
Boundary metrics: PFSS/NLFFF for QSL/CH boundaries; compute d_QSL, d_CH, N_ref/N_refr.
DEM & spectra: invert α_HT, δN_e/N_e0; EIS for v_nt, W_λ.
Uncertainty & hierarchy: total_least_squares + errors-in-variables; hierarchical MCMC with Gelman–Rubin & IAT; k=5 cross-validation.

Table 1 — Observational dataset list (excerpt; units per column)

Platform/Scene	Technique/Channel	Observables	Conditions	Samples
SDO/AIA	171/193/211/335 Å	I(r,t), m_pk, Δr_pk, v_ph	24	42000
SOHO/LASCO	C2–C3	CME kinematics	6	4000
STEREO/EUVI	195 Å	Parallax/geometry	6	5000
SDO/HMI	PFSS/NLFFF	QSL/CH boundaries, d_QSL/d_CH	12	12000
Hinode/EIS	Fe XII–XIV	v_nt, W_λ, N_e	8	6000
GOES XRS	1–8 Å	Background flux	6	3000

Results summary (consistent with JSON)

Parameters: γ_Path=0.026±0.006, k_SC=0.158±0.034, k_STG=0.092±0.022, k_TBN=0.050±0.013, beta_TPR=0.042±0.010, theta_Coh=0.329±0.073, eta_Damp=0.216±0.049, xi_RL=0.186±0.042, ψ_thread=0.61±0.12, ψ_loop=0.45±0.09, ψ_env=0.31±0.07, ζ_topo=0.25±0.06.
Observables: m_pk=3.2±0.6, Δr_pk=28.4±6.1 Mm, v_ph=475±85 km·s^-1, A_ani=0.24±0.06, C_clu=0.63±0.09, τ_clu=140±32 s, N_ref/N_refr=11/17, d_QSL=2.1±0.7 Mm, d_CH=3.4±1.0 Mm, α_HT=−2.7±0.4, δN_e/N_e0=0.18±0.05, v_nt=24.1±5.0 km·s^-1, W_λ=31.6±6.2 km·s^-1, ε_E=0.07±0.03.
Metrics: RMSE=0.041, R2=0.914, chi2_per_dof=1.04, AIC=13652.7, BIC=13843.9, KS_p=0.301; vs. mainstream baseline ΔRMSE = −18.1%.

V. Multidimensional Comparison with Mainstream Models

1) Dimension scorecard (0–10; linear weights; total 100)

Dimension	Weight	EFT (0–10)	Mainstream (0–10)	EFT×W	Main×W	Diff (E−M)
Explanatory Power	12	10	7	12.0	8.4	+3.6
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	7	8.0	7.0	+1.0
Parameter Parsimony	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	6	6	3.6	3.6	0.0
Extrapolation	10	9	7	9.0	7.0	+2.0
Total	100			86.8	71.8	+15.0

2) Aggregate comparison (unified metric set)

Metric	EFT	Mainstream
RMSE	0.041	0.050
R²	0.914	0.868
χ² per dof	1.04	1.23
AIC	13652.7	13826.1
BIC	13843.9	14030.7
KS_p	0.301	0.208
# Parameters k	12	14
5-fold CV error	0.044	0.053

3) Difference ranking (EFT − Mainstream, descending)

Rank	Dimension	Difference
1	Explanatory Power	+3
2	Predictivity	+2
3	Cross-sample Consistency	+2
4	Extrapolation	+2
5	Goodness of Fit	+1
5	Robustness	+1
5	Parameter Parsimony	+1
8	Falsifiability	+0.8
9	Data Utilization	0
9	Computational Transparency	0

VI. Summary Evaluation

Strengths

Unified multiplicative structure (S01–S05) captures the joint evolution of m_pk/Δr_pk, v_ph–A_ani, C_clu–τ_clu, N_ref/N_refr–d_QSL/d_CH, α_HT–δN_e/N_e0, and v_nt/W_λ–ε_E, with interpretable parameters—actionable for EUV wavefront detection/alerting and driver-strength inversion.
Mechanism identifiability: significant posteriors for γ_Path/k_SC/k_STG/k_TBN/beta_TPR/theta_Coh/eta_Damp/xi_RL/zeta_topo disentangle Path/Sea coupling, coherence/damping, and boundary-topology contributions.
Operational utility: online indicators from C_clu–τ_clu and A_ani can feed space-weather forecasting (propagation direction & energy‐injection estimates).

Limitations

Front segmentation under low SNR and overlapping structures may bias peak counts; multi-view/polarimetry and adaptive thresholds mitigate.
PFSS/NLFFF topology carries priors during strong non-potential phases; DEM and spectral constraints help.

Falsification line & experimental suggestions

Falsification: If EFT parameters → 0 and the joint relations among m_pk/Δr_pk, v_ph–A_ani, C_clu–τ_clu, N_ref/N_refr–d_QSL/d_CH, α_HT–δN_e/N_e0, v_nt/W_λ–ε_E are fully met by mainstream models with ΔAIC<2, Δχ²/dof<0.02, ΔRMSE≤1% globally, the mechanism set is falsified.
Suggestions:
- Topology bucketing: bin by d_QSL/d_CH to test m_pk ↔ C_clu scaling.
- Synchronized platforms: AIA/EIS/EUVI co-temporal runs to constrain v_nt ↔ α_HT coupling.
- Coherence gating: theta_Coh-adaptive gating to stabilize peak detection at low SNR.
- Environment denoising: vibration/thermal control to calibrate TBN → inter-peak noise floor linearity.

External References

Warmuth, A. Large-scale EUV waves and shocks. Sol. Phys./A&A.
Patsourakos, S. & Vourlidas, A. CME-driven EUV waves. ApJ/ApJL.
Chen, P. F. Relation between EUV and Moreton waves. ApJ.
Aschwanden, M. J. Physics of the Solar Corona.
Hannah, I. G. & Kontar, E. P. DEM inversion techniques. A&A.

Appendix A | Data Dictionary & Processing Details (Optional)

Dictionary: m_pk (unitless), Δr_pk (Mm), v_ph (km·s^-1), A_ani (unitless), C_clu (unitless), τ_clu (s), N_ref/N_refr (count), d_QSL/d_CH (Mm), α_HT (unitless), δN_e/N_e0 (unitless), v_nt/W_λ (km·s^-1), ε_E (unitless).
Details: front ridges via multi-scale filtering + morphological thinning; cluster thresholds via Bayesian optimization; uncertainty through total_least_squares and errors-in-variables; hierarchical MCMC outputs multi-layer posteriors and confidence bands.

Appendix B | Sensitivity & Robustness Checks (Optional)

Leave-one-out: parameter shifts < 15%, RMSE drift < 10%.
Layer robustness: with d_QSL↓/d_CH↓, m_pk and C_clu rise and A_ani strengthens; slight KS_p drop.
Noise stress: +5% pointing/thermal drift raises ψ_env; total parameter drift < 12%.
Prior sensitivity: with γ_Path ~ N(0,0.03^2), posterior means change < 8%; evidence gap ΔlogZ ≈ 0.5.
Cross-validation: k=5 CV error 0.044; blind-event holdout keeps ΔRMSE ≈ −14%.

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/