Reproducibility Archive for "Is the S₈ Tension Structural? A Provenance-Based Reanalysis of Cross-Survey Covariance" (v1.0.0)

This archive contains the complete deterministic reproducibility package for:

Is the S₈ Tension Structural? A Provenance-Based Reanalysis of Cross-Survey Covariance (Paper S).

It provides all data, code, correlation-matrix construction logic, residual systematic implementation, and validation procedures required to regenerate every numerical result reported in the paper.

The archive implements a provenance-encoded correlation framework across 36 published S₈ measurements spanning CMB, weak lensing, 3×2pt, RSD, cluster, and joint analyses. Residual systematic corrections are applied using literature-supported ranges without parameter fitting.

All computations are executed through a deterministic pipeline with golden-output validation (tolerance = 0.0), SHA-256 checksum manifests, and ROOT_HASH chain-of-custody enforcement.

All numerical values reported in Paper S correspond exactly to the outputs generated by this archived version 1.0.0 pipeline.

This repository contains the full reproducibility package accompanying Paper S. The archive provides a deterministic implementation of the provenance-based correlation framework and residual systematic model used to evaluate cross-survey structural dependencies in published S₈ measurements.

Archive contents

Data

• 36-measurement S₈ dataset
• conservative and upper-bound residual-corrected datasets
• provenance-encoded 36×36 correlation matrix
• synthesis and sensitivity-analysis outputs

Code

• correlation-matrix construction
• residual systematic application
• inverse-covariance synthesis
• sensitivity sweeps over correlation parameters
• consolidated metric export and validation

Validation

• golden-output files (tolerance = 0.0)
• schema and unit validation
• SHA-256 checksum manifest
• ROOT_HASH integrity file
• continuous-integration workflow

Documentation

• methodology overview
• provenance notes
• eigenvalue diagnostics
• robustness tests
• replication protocol and runbook

Deterministic regeneration

From the archive root:

python code/minimal_run.py

python code/validate_metrics.py

python validation/rebuild_checksums.py

Successful execution with no errors confirms full deterministic reproduction of the canonical archived results.

Structural context

This archive applies the deterministic reproducibility and covariance-governance framework defined in Paper 0 and implements the cross-probe correlation synthesis methodology used in Paper 1 within the late-time structure-growth domain.