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Evidence and Tests - Geometric Universe

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Mapping Claims to Observations
This page connects the Geometric Universe claims to concrete observational signatures. For each claim, we note what to look for, where to find the data, and how to test it.

How to read:
  • Claim → conceptual statement from the Framework.
  • Observation → measurable signature (e.g., spectrum feature, scaling, correlation).
  • Dataset → survey or archive to query.
  • Method → analysis or model comparison approach.
  • Confounders → competing explanations or systematics to control.

Examples:
  • C1: Geometry-driven early-time correlation length predicts a slight, scale-dependent shift in CMB peak ratios. Datasets: Planck, WMAP. Method: power spectrum fit with alternative priors. Confounders: beam systematics, calibration.
  • C2: Large-scale structure two-point function shows a mild anisotropy aligned with derived geometric axes. Datasets: BOSS/eBOSS, DESI. Method: multipole analysis. Confounders: redshift-space distortions, selection.
Falsifiable Predictions
Datasets & Access
Primary sources:
  • CMB: Planck PR3/PR4, WMAP; public via ESA and LAMBDA.
  • LSS: BOSS/eBOSS, DESI early data; catalogs via SDSS/DESI portals.
  • SN/BAO: Pantheon+, SH0ES, eBOSS/SDSS BAO; public archives.
Access tips:
  • Use official mirrors; verify checksums.
  • Prefer FITS/CSV with documented schema.
  • Record data provenance in notebooks.
Methods & Confounders
Pipeline outline:
  1. Data quality cuts and masks.
  2. Model comparison: ΛCDM vs Geometric priors.
  3. Uncertainty: bootstrap/MCMC with priors.
Key confounders:
  • Instrumental systematics (beam, calibration).
  • Selection functions and redshift errors.
  • Astrophysical foregrounds and dust.
Documentation:
Track parameters and random seeds; export YAML config per run.
Results & Interpretation
Current highlights:
  • Preliminary CMB peak-ratio re-fit shows consistency within uncertainties; no decisive deviation yet.
  • Early DESI-like mock catalogs indicate small anisotropy is not excluded; requires better control of selection.
  • Distance-duality cross-checks are sensitive to calibration assumptions; robustness tests ongoing.
Interpretation principles:
  • Favor effect-size estimates with uncertainty bands over binary outcomes.
  • Report Bayes factors and likelihood ratios alongside posterior summaries.
  • Pre-register analysis choices where possible.
Related Updates
Telescope and experimental setup illustrating planned tests
An outline of observational and experimental tests designed to potentially falsify or support the theory.
Read all
Notes highlighting updates to the Geometric Universe theory
A concise summary of the most significant revisions, clarifications, and new results added to the theory.
Read all
Replication & Materials
Reproducibility checklist:
  • Environment: Python 3.11, NumPy/SciPy, Astropy, CAMB/CLASS as needed.
  • Data: download links in Datasets & Access above; verify checksums.
  • Configs: YAML files track seeds, priors, and cuts.
  • Notebooks: one per claim (C1–C4) with narrative and outputs.
  • Outputs: store figures and JSON summaries under results/.
How to run:
  1. Clone the repository (link to be added).
  2. Create conda environment from environment.yml.
  3. Run notebooks in order C1→C4; compare to baseline ΛCDM fits.
Attribution: cite the Framework parameters & constraints and relevant Derivations.
Project Status
Board:
  • Planned: SN–BAO distance-duality cross-check; DESI DR1 anisotropy test.
  • In progress: CMB peak-ratio re-fit with alternative priors; mock-catalog validation.
  • Complete (draft): Parameter-estimation pipeline with priors and MCMC diagnostics.
Checklist links:
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