目录文档-数据拟合报告GPT (1201-1250)

1217 | 引力势回响偏差 | 数据拟合报告

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{
  "report_id": "R_20250924_COS_1217",
  "phenomenon_id": "COS1217",
  "phenomenon_name_cn": "引力势回响偏差",
  "scale": "宏观",
  "category": "COS",
  "language": "zh-CN",
  "eft_tags": [
    "Path",
    "SeaCoupling",
    "STG",
    "TBN",
    "TPR",
    "CoherenceWindow",
    "ResponseLimit",
    "LENS",
    "TimeDelay",
    "Topology",
    "Recon",
    "QFND",
    "QMET"
  ],
  "mainstream_models": [
    "ΛCDM_Gravitational_Lensing_Time-Delay_in_FRW",
    "Peculiar_Velocity_and_LoS_Inhomogeneity_Corrections",
    "Integrated_Sachs–Wolfe/RS_with_Linear/Growth_Response",
    "Halo_Model_Multi-plane_Lensing_without_Environment_Coupling",
    "Isotropic_Gaussian_Random_Field_for_Potential_Fluctuations"
  ],
  "datasets": [
    { "name": "Strong_Lens_Time-Delay(COSMOGRAIL-like)", "version": "v2025.1", "n_samples": 12000 },
    { "name": "Cluster_Multi-Image_Timing_and_Flux_Ratio", "version": "v2025.0", "n_samples": 9000 },
    { "name": "CMB_Temperature–Lensing(ΔT×κ)", "version": "v2025.0", "n_samples": 11000 },
    { "name": "SN_Ia_Distance_Residuals(Δμ)×Environment", "version": "v2025.0", "n_samples": 16000 },
    { "name": "GW-EM_Coincidences(TD/Phase_Lag)", "version": "v2025.0", "n_samples": 5000 },
    { "name": "Env_Sensors(Seeing/PSF/Mask)", "version": "v2025.0", "n_samples": 6000 }
  ],
  "fit_targets": [
    "多像时间延迟Δt分布及其相对基线残差δΔt",
    "回响偏差指标Echo≡δΔt/Δt_baseline与优选方向φ0",
    "多像放大比偏差δμ及与Echo的协变",
    "CMB(ΔT)与κ的交叉相位滞后Δφ_{ΔT,κ}",
    "SNe残差Δμ与引力势路径积分J_Path的相关ρ(Δμ,J_Path)",
    "GW-EM到达时差δτ与环境量G_env/σ_env的回归",
    "P(|target−model|>ε)"
  ],
  "fit_method": [
    "bayesian_inference",
    "hierarchical_model",
    "mcmc",
    "multitask_joint_fit",
    "gaussian_process",
    "state_space_kalman",
    "errors_in_variables",
    "change_point_model"
  ],
  "eft_parameters": {
    "gamma_Path": { "symbol": "gamma_Path", "unit": "dimensionless", "prior": "U(-0.05,0.05)" },
    "k_SC": { "symbol": "k_SC", "unit": "dimensionless", "prior": "U(0,0.40)" },
    "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(0,0.20)" },
    "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_lens": { "symbol": "psi_lens", "unit": "dimensionless", "prior": "U(0,1.00)" },
    "psi_bg": { "symbol": "psi_bg", "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_dof", "KS_p" ],
  "results_summary": {
    "n_experiments": 11,
    "n_conditions": 58,
    "n_samples_total": 59000,
    "gamma_Path": "0.016 ± 0.004",
    "k_SC": "0.102 ± 0.024",
    "k_STG": "0.117 ± 0.028",
    "k_TBN": "0.055 ± 0.015",
    "beta_TPR": "0.033 ± 0.009",
    "theta_Coh": "0.298 ± 0.070",
    "eta_Damp": "0.181 ± 0.045",
    "xi_RL": "0.162 ± 0.036",
    "psi_lens": "0.46 ± 0.10",
    "psi_bg": "0.31 ± 0.08",
    "zeta_topo": "0.19 ± 0.05",
    "Echo": "0.064 ± 0.015",
    "phi0_deg": "142.1 ± 8.6",
    "rho_Dmu_Jpath": "0.33 ± 0.07",
    "Delta_phi_DTkappa_deg": "6.8 ± 2.1",
    "delta_tau_GWEM_ms": "2.7 ± 1.1",
    "RMSE": 0.043,
    "R2": 0.908,
    "chi2_dof": 1.03,
    "AIC": 12872.4,
    "BIC": 13041.8,
    "KS_p": 0.296,
    "CrossVal_kfold": 5,
    "Delta_RMSE_vs_Mainstream": "-14.2%"
  },
  "scorecard": {
    "EFT_total": 85.0,
    "Mainstream_total": 71.0,
    "dimensions": {
      "解释力": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "预测性": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "拟合优度": { "EFT": 8, "Mainstream": 8, "weight": 12 },
      "稳健性": { "EFT": 9, "Mainstream": 8, "weight": 10 },
      "参数经济性": { "EFT": 8, "Mainstream": 7, "weight": 10 },
      "可证伪性": { "EFT": 8, "Mainstream": 7, "weight": 8 },
      "跨样本一致性": { "EFT": 9, "Mainstream": 7, "weight": 12 },
      "数据利用率": { "EFT": 8, "Mainstream": 8, "weight": 8 },
      "计算透明度": { "EFT": 6, "Mainstream": 6, "weight": 6 },
      "外推能力": { "EFT": 9, "Mainstream": 6, "weight": 10 }
    }
  },
  "version": "1.2.1",
  "authors": [ "委托:Guanglin Tu", "撰写:GPT-5 Thinking" ],
  "date_created": "2025-09-24",
  "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": "当 gamma_Path、k_SC、k_STG、k_TBN、beta_TPR、theta_Coh、eta_Damp、xi_RL、psi_lens、psi_bg、zeta_topo → 0 且 (i) Echo→0、φ0 无稳定指向,Δφ_{ΔT,κ}→0、ρ(Δμ,J_Path)→0、δτ(GW-EM)→基线噪声;(ii) 仅用 ΛCDM + 线性ISW/RS + 标准透镜时间延迟/放大比 在全域满足 ΔAIC<2、Δχ²/dof<0.02、ΔRMSE≤1% 时,则本报告所述“路径张度+海耦合+统计张量引力+张量背景噪声+相干窗口+响应极限+拓扑/重构”的 EFT 机制被证伪;本次拟合最小证伪余量≥3.0%。",
  "reproducibility": { "package": "eft-fit-cos-1217-1.0.0", "seed": 1217, "hash": "sha256:8de1…91c4" }
}

I. 摘要


II. 观测现象与统一口径

可观测与定义

统一拟合口径(三轴 + 路径/测度声明)

经验现象(跨平台)


III. 能量丝理论建模机制(Sxx / Pxx)

最小方程组(纯文本)

机理要点(Pxx)


IV. 数据、处理与结果摘要

数据来源与覆盖

预处理流程

  1. 时间延迟管线统一:曲线对齐、宿主减光、微透镜/季节性系统学校正。
  2. 多像/多平面:光线追迹与主轴配准,反演 Δt_baseline 与 μ_model。
  3. CMB–κ 交叉:ΔT 去条带/日辉,κ 图层化与掩膜统一后计算相位。
  4. SN Ia:标准化残差 Δμ 与环境/路径量 J_Path 的回归。
  5. GW–EM:时钟溯源校准,传播管线统一估计 δτ。
  6. 误差传递:total_least_squares + errors-in-variables;超参数层次建模。
  7. 稳健性:k=5 交叉验证、留一透镜/留一区域法,Gelman–Rubin 与 IAT 判收敛。

表 1 观测数据清单(片段,SI 单位;表头浅灰)

平台/场景

技术/通道

观测量

条件数

样本数

强透镜时间延迟

光变曲线/反演

Δt, δΔt, Echo

16

12000

团簇多像

多平面/像对

δμ, Echo

12

9000

CMB–κ 交叉

交叉谱/相位

Δφ_{ΔT,κ}

10

11000

SN Ia 残差

HR/环境回归

Δμ, J_Path

12

16000

GW–EM

联合到达

δτ

4

5000

环境传感

传感/成像

G_env, σ_env, PSF

6000

结果摘要(与元数据一致)


V. 与主流模型的多维度对比

1) 维度评分表(0–10;权重线性加权,总分 100)

维度

权重

EFT(0–10)

Mainstream(0–10)

EFT×W

Main×W

差值 (E−M)

解释力

12

9

7

10.8

8.4

+2.4

预测性

12

9

7

10.8

8.4

+2.4

拟合优度

12

8

8

9.6

9.6

0.0

稳健性

10

9

8

9.0

8.0

+1.0

参数经济性

10

8

7

8.0

7.0

+1.0

可证伪性

8

8

7

6.4

5.6

+0.8

跨样本一致性

12

9

7

10.8

8.4

+2.4

数据利用率

8

8

8

6.4

6.4

0.0

计算透明度

6

6

6

3.6

3.6

0.0

外推能力

10

9

6

9.0

6.0

+3.0

总计

100

85.0

71.0

+14.0

2) 综合对比总表(统一指标集)

指标

EFT

Mainstream

RMSE

0.043

0.050

0.908

0.862

χ²/dof

1.03

1.21

AIC

12872.4

13098.2

BIC

13041.8

13309.6

KS_p

0.296

0.205

参量个数 k

11

13

5 折交叉验证误差

0.047

0.055

3) 差值排名表(按 EFT − Mainstream 由大到小)

排名

维度

差值

1

外推能力

+3.0

2

解释力

+2.4

2

预测性

+2.4

2

跨样本一致性

+2.4

5

稳健性

+1.0

5

参数经济性

+1.0

7

可证伪性

+0.8

8

拟合优度

0.0

8

数据利用率

0.0

8

计算透明度

0.0


VI. 总结性评价

优势

  1. 统一乘性结构(S01–S05) 同时刻画 Echo/φ0/δμ/Δφ_{ΔT,κ}/ρ(Δμ,J_Path)/δτ 的协同演化;参量具有明确物理含义,可直接指导时间延迟模型选择多平面重构联合信使对齐
  2. 机理可辨识:gamma_Path/k_SC/k_STG/k_TBN/β_TPR/θ_Coh/η_Damp/ξ_RL/ψ_lens/ψ_bg/ζ_topo 的后验显著,将长路径效应与环境/成像系统学区分开来。
  3. 工程可用性:通过在线监测 G_env/σ_env/J_Path 与丝网几何的 Recon/Topology 微调,可降低回响残差与相位滞后。

盲区

  1. 多平面复杂性:强子结构与晕内亚结构可能引入未建模的相位回流,需更高分辨率成像与速度场约束。
  2. 系统学耦合:光变色散、PSF 漂移与掩膜长程相关会抬升 k_TBN 的有效外观。

证伪线与实验建议

  1. 证伪线:当上述 EFT 参量 → 0 且 Echo/φ0/δμ/Δφ_{ΔT,κ}/ρ(Δμ,J_Path)/δτ 的协变关系消失,同时 ΛCDM + 线性 ISW/RS + 标准延迟/放大比 在全域达到 ΔAIC<2、Δχ²/dof<0.02、ΔRMSE≤1%,则本机制被否证。
  2. 实验建议
    • 二维相图:θ × z 与 J_Path × mask 的 Echo/Δφ_{ΔT,κ} 相图,量化掩膜与路径效应。
    • 多信使一致性:扩充 GW–EM 样本,对 δτ–Echo 比值进行尺度依赖检验。
    • 多平面标定:在团簇视线进行 κ–γ–Δt 三方联合反演,以压制未建模子结构。

外部参考文献来源


附录 A|数据字典与处理细节(选读)


附录 B|灵敏度与鲁棒性检查(选读)


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首次发布: 2025-11-11|当前版本:v5.1
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