The Complete Cosmic Blueprint Laboratory-Grade Reproducibility PDF

Author’s Technical Note — Laboratory-Grade Reproducibility Package

The Complete Cosmic Blueprint: Sub-Percent Determination of the Universe’s Composition after Resolving the Hubble Tension

Paper 3 DOI : 

https://doi.org/10.5281/zenodo.17379338

(Pre-release documentation; executable ZIP forthcoming)

This document accompanies the paper, "The Complete Cosmic Blueprint: Sub-Percent Determination of the Universe’s Composition after Resolving the Hubble Tension”, which forms Part III of the Precision Cascade research series. It provides a complete, transparent description of the datasets, computational workflows, and validation framework used to derive the full cosmic energy-density composition and validate universal flatness to sub-percent precision.

This dataset provides the extended, human-readable curvature and flatness analysis used as the geometric baseline for The Complete Cosmic Blueprint (Paper 3 in a six-paper precision-cosmology series). It compiles multi-probe curvature constraints (Ωₖ) from CMB geometry, BAO geometry, SN Ia geometry, H₀-enhanced geometry, weak and strong lensing, and standard-siren luminosity distances. All measurements are documented exactly as used in the analysis, with uncertainties (σ), normalization notes, provenance, and methodological guardrails.Using inverse-variance weighting, the combined curvature estimator yields Ωₖ* ≈ 0.000 with an uncertainty σ(Ωₖ*) ≈ 0.0007, validating spatial flatness at >6σ significance—consistent with the full sub-percent cosmological model presented in the accompanying publication.The dataset includes: (1) geometric_constraints.csv, (2) golden_outputs.csv, (3) human-readable derivations, (4) code for minimal and full replication, (5) protocols, (6) supplements, (7) SHA-256 checksums, and (8) a continuous-integration workflow for audit-grade reproducibility. All scientific values are preserved exactly, with no modification or interpretation. This release represents the transparent, static, human-readable component of the larger Lab-Grade Reproducibility Package that will accompany the full cosmological analysis. This release continues the series’ commitment to audit-grade transparency, rigorous reproducibility, and independent verification of discovery-level cosmological findings.

DescriptionThis record contains the complete human-readable curvature and geometric-flatness dataset that supports The Complete Cosmic Blueprint: Sub-Percent Determination of the Universe’s Composition after Resolving the Hubble Tension (Daniel Jeffrey, 2025). It documents the geometric curvature baseline used in the larger sub-percent cosmological model and provides a fully transparent, static, text-based version of the curvature replication pipeline.

This dataset is intentionally designed to be:

Static: all values and equations are preserved exactly as used—no transformations, reinterpretations, or automated recalculations.

Human-Readable: formatted for clarity, reviewability, and scholarly inspection, without requiring execution of code.

Audit-Grade: includes provenance, derivations, validation targets, and reproducibility guardrails.

Machine-Ready: dataset files (CSV), code (Python), and workflows (CI) correspond exactly to the human-readable documentation.

The full dataset includes:

1. Core Data Files

data/geometric_constraints.csvMulti-probe curvature measurements (Ωₖ, σ) from Planck, CMB+BAO, CMB+SN, H₀-enhanced geometry, weak lensing, strong lensing, and gravitational-wave luminosity distances.

data/golden_outputs.csvExpected values and tolerances for validating the reproduction of Ωₖ*, σ(Ωₖ*), and flatness significance.

2. Documentation & Provenance

data/provenance.md — Source explanations and harmonization rules

supplements/derivations.pdf — Human-readable canonical derivations

audit_notes.md, assumptions.md, glossary.md

3. Code (Reproducibility Scripts)

code/minimal_run.py — Deterministic quick-check calculation

code/reproduce_geometry_flatness.ipynb — Full notebook replication

code/export_results.py — Consolidation and archival

All scripts are transparent, complete, and free of placeholders.

4. Protocols & Environment

protocol/replication_protocol.md — Step-by-step instructions

protocol/runbook.md — High-level checklist

protocol/environment.yml and protocol/requirements.txt — Reproducible runtime environment

5. Continuous-Integration Workflow

.github/workflows/reproducibility.ymlAutomated pipeline that executes the full replication and validates outputs against golden values.

6. Integrity & Transparency

checksums/sha256sums.txt — Bit-level integrity verification

metadata.json, LICENSE, README.md

This dataset directly corresponds to the curvature subsystem used in the main paper’s fully correlated Bayesian cosmological model. It is intended as the transparent, inspectable foundation for the forthcoming executable ZIP archives, allowing researchers, reviewers, and institutions to audit and verify the geometric-flatness component of the analysis independently.

Additional Notes

This dataset corresponds to Paper 3 in the six-paper series establishing AI-assisted precision cosmology as a reproducible scientific discipline.

All values and equations are preserved exactly as originally used.

This dataset is not a recalculation of the full cosmological model—it is the geometric baseline feeding into it.

A machine-executable ZIP archive will be released after this static human-readable version, completing the Lab-Grade Reproducibility Package.

— Daniel JeffreyIndependent Researcher (2025)

作者技术说明——实验室级可复现套件（Lab-Grade Reproducibility Package）

完整宇宙蓝图：解决哈勃张力（Hubble Tension）后实现宇宙成分的亚百分精度测定

论文3 DOI：
https://doi.org/10.5281/zenodo.17379338

（预发布文档；可执行ZIP包即将上线）

本技术说明配套论文《完整宇宙蓝图：解决哈勃张力（Hubble Tension）后实现宇宙成分的亚百分精度测定》，该论文属于《精准级联（Precision Cascade）》研究系列的第三部分。本文件完整且透明地阐述了用于推导全宇宙能量密度成分，并以亚百分精度验证宇宙平直性的数据集、计算流程与验证框架。

本数据集提供了经扩展的、可读格式的曲率与平直性分析结果，作为《完整宇宙蓝图》（六篇精准宇宙学系列论文的第三篇）的几何基线。本数据集整合了来自多探针的曲率约束结果：宇宙微波背景（Cosmic Microwave Background，简称CMB）几何、重子声学振荡（Baryon Acoustic Oscillations，简称BAO）几何、Ia型超新星（Type Ia Supernova，简称SN Ia）几何、哈勃常数（H₀）增强几何、弱引力透镜与强引力透镜，以及标准汽笛光度距离。所有测量结果均严格按照分析中的原始使用方式记录，包含不确定度σ、归一化说明、来源溯源与方法学约束。

通过逆方差加权，联合曲率估计量得到Ωₖ*≈0.000，不确定度σ(Ωₖ*)≈0.0007，以高于6σ的显著性验证了空间平直性——与配套论文中提出的全亚百分精度宇宙学模型一致。本数据集包含：(1) geometric_constraints.csv，(2) 金标准输出文件（golden_outputs.csv），(3) 可读形式推导过程，(4) 最小化与完整复现代码，(5) 实验方案，(6) 补充材料，(7) SHA-256校验和，以及(8) 用于实现审计级可复现性的持续集成工作流。所有科学数值均被完整保留，未做任何修改或解读。本次发布是配套全宇宙学分析的实验室级可复现套件（Lab-Grade Reproducibility Package）的透明、静态可读版本。本次发布延续了该系列对审计级透明性、严格可复现性以及对发现级宇宙学结果进行独立验证的承诺。

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## 数据集说明
本记录包含支撑《完整宇宙蓝图：解决哈勃张力（Hubble Tension）后实现宇宙成分的亚百分精度测定》（Daniel Jeffrey，2025）的完整可读格式曲率与几何平直性数据集。本文件记录了更大规模亚百分精度宇宙学模型中使用的几何曲率基线，并提供了曲率复现流程的全透明、静态文本版本。

本数据集的设计目标如下：
- **静态性**：所有数值与公式均严格保留原始使用形式，未做任何变换、重解读或自动重计算。
- **可读友好**：格式清晰便于审阅与学术审查，无需执行代码即可阅读。
- **审计级合规**：包含来源溯源、推导过程、验证目标与可复现性约束。
- **机器适配**：数据集文件（CSV格式）、代码（Python语言）与工作流（持续集成，Continuous Integration，简称CI）均与可读文档严格对应。

### 完整数据集包含以下内容：
1. **核心数据文件**
- `data/geometric_constraints.csv`：来自普朗克卫星、CMB+BAO、CMB+SN、H₀增强几何、弱引力透镜、强引力透镜与引力波光度距离的多探针曲率测量结果（Ωₖ, σ）。
- `data/golden_outputs.csv`：用于验证Ωₖ*、σ(Ωₖ*)与平直性显著性复现结果的预期值与容差范围。

2. **文档与来源溯源**
- `data/provenance.md`：来源说明与数据协调规则
- `supplements/derivations.pdf`：可读形式的标准推导过程
- `audit_notes.md`、`assumptions.md`与`glossary.md`

3. **代码（复现脚本）**
- `code/minimal_run.py`：确定性快速校验计算脚本
- `code/reproduce_geometry_flatness.ipynb`：完整复现的Jupyter Notebook
- `code/export_results.py`：结果整合与归档脚本
所有脚本均透明完整，无占位符内容。

4. **实验方案与运行环境**
- `protocol/replication_protocol.md`：分步复现指南
- `protocol/runbook.md`：高阶校验清单
- `protocol/environment.yml`与`protocol/requirements.txt`：可复现的运行时环境配置

5. **持续集成工作流**
- `.github/workflows/reproducibility.yml`：执行完整复现并将输出与金标准值比对的自动化流水线

6. **完整性与透明性**
- `checksums/sha256sums.txt`：比特级完整性校验
- `metadata.json`、`LICENSE`与`README.md`

本数据集与主论文中全关联贝叶斯（Bayesian）宇宙学模型所用的曲率子系统完全对应。本数据集旨在为即将发布的可执行ZIP包提供透明可审查的基础，使研究人员、审稿人与科研机构能够独立审查并验证分析中的几何平直性部分。

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## 补充说明
1. 本数据集属于六篇系列论文的第三篇，该系列将AI辅助精准宇宙学确立为一门可复现的科学学科。
2. 所有数值与公式均严格保留原始使用形式。
3. 本数据集并非对全宇宙学模型的重计算，而是为该模型提供输入的几何基线。
4. 在本静态可读版本发布后，将推出机器可执行的ZIP归档包，从而完整构成实验室级可复现套件（Lab-Grade Reproducibility Package）。

—— Daniel Jeffrey
独立研究者（2025）