目录文档-技术白皮书(V5.05)47-PTN Template v1.0

第7章 误差预算与不确定度


I. 误差源清单(Error Sources Catalog)

统一口径:源项以符号、单位、估计方法、分布假设与相关性给出;类型标注 A(统计评定)/B(非统计评定);路径相关量显式 gamma(ell) 与测度 d ell。


II. 传播模型(Uncertainty Propagation Models)

线性化、协方差积分与蒙特卡洛三轨并行;当使用稳健损失时,提供二阶等价代理以进行误差传播。


III. 合成与区间(Composition & Intervals)


IV. 监控与告警(Monitoring & Alerts)


V. 误差预算卡(Error-Budget Card,发布格式)

字段:source, symbol, unit, type(A/B), estimate, distribution, correlation, note, see[]。

source

symbol

unit

type

estimate

distribution

correlation

see[]

Absolute timing

δt_abs

s

A

u(δt_abs)

approx-N

vs. channel skew

Core.Metrology v1.0

Path measure

d ell

m

B

u(d ell)

uniform

with gamma(ell)

Core.DataSpec v1.0:TARR

Medium index profile

n_eff(ell)

1

A/B

u(n_eff)

GP kernel

length L_c

Core.Terms v1.0

Reference speed

c_ref

m/s

B

u(c_ref)

normal

global

Core.Terms v1.0

Reference wavelength

λ_ref

m

B

u(λ_ref)

normal

global

Core.Metrology v1.0

Calibration params

θ_k

B

u(θ_k)

normal

block-correlated

Metrology.* v1.0

Readout noise

σ_ro

e⁻

A

u(σ_ro)

normal

per-channel

Methods.Cleaning v1.0

Discretization

B

u(discretization)

bounded

model-dependent

Methods.SimStack v1.0

交付:随结果页附 error_budget.csv 与展平的 see[](“卷名 + 版本 + 锚点”)。


VI. 报告与记录(Reporting & Records)


VII. 相干示例(Normative Examples,可直接复用)

Given: T_arr = ∑_i ( n_i / c_ref ) · d ell_i

u^2(T_arr) = ∑_{i,j} ( d ell_i d ell_j / c_ref^2 ) · Cov(n_i, n_j) + ( ∑_i n_i d ell_i / c_ref^2 )^2 · u^2(c_ref)

Dims check: [1]/[m·s^-1]*[m] = [s] ✅

Phi = ( 2π / λ_ref ) ∑_i n_i d ell_i

u^2(Phi) = ( 2π / λ_ref )^2 ∑_{i,j} d ell_i d ell_j · Cov(n_i, n_j) + ( 2π ∑_i n_i d ell_i / λ_ref^2 )^2 · u^2(λ_ref)

Unit: [rad] ✅

Draw B=10000 bootstrap replicates of residuals → refit → collect T_arr^*.

Report median [P2.5, P97.5]; compare with thresholds τ_T, mark pass/fail.


VIII. 机读模板(Machine-Readable, 可落库)

version: "1.0.0"

uncertainty:

targets: ["T_arr","Phi","ε_flux","ΔM","Q"]

methods:

T_arr: ["delta","mc"]

Phi: ["delta","mc"]

ε_flux: ["bootstrap","delta"]

delta:

jacobian: "auto"

cov_model:

n_eff:

kernel: "exp"

L_c_m: 25.0

mc:

draws: 10000

seed: 20250924

coverage:

k: 2

type: "confidence" # or "credible" / "quantile"

report:

export: ["error_budget.csv","uncertainty.md","check_dim_report.json"]

see:

- "EFT.WP.Core.Equations v1.1:S20-1"

- "EFT.WP.Core.Metrology v1.0:check_dim"

- "Methods.Cleaning v1.0"

- "Methods.SimStack v1.0"

- "Core.DataSpec v1.0:TARR"


IX. 与质量门的对齐(与第 5 章一致)


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版本信息: 首次发布:2025-11-11 | 当前版本:v6.0+5.05