V96: Gravity as the Shadow of Confinement Cost — A Statistical Mapping from Σ-Ledger Rent Flow to ΛCDM Fractions (ΩDE:ΩDM:ΩM ≈ 68:27:5)
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2026
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| _version_ | 1866902321225531392 |
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| author | Yoshida, Satoshi ChatGPT, A. Gemini, G. Claude, C. |
| author_facet | Yoshida, Satoshi ChatGPT, A. Gemini, G. Claude, C. |
| contents | <p>We propose an information-economic reinterpretation of gravitational sourcing: gravity is not a fundamental force emitted by matter, but the "shadow" of the ongoing maintenance cost required to keep localized states stable in a finite-update universe.</p> <p>In this framework, the dark sector is not a new particle inventory but a statistical decomposition of that cost. We implement a minimal statistical model (V96-SM) in which the total rent flow is decomposed into three components:<br>(i) a mean "base rent" → Dark Energy (DE),<br>(ii) a variance term from structured fluctuations → Dark Matter (DM),<br>(iii) a thresholded "commit" term for stabilized receipts → Matter (M).</p> <p>Without fitting individual cosmological datasets, the model robustly reproduces the observed energy-budget ratios ΩDE:ΩDM:ΩM ≈ 0.68:0.27:0.05 with sub-percent total absolute error (E = 0.0063 across 30 random seeds).</p> <p>Key findings include:<br>- Ablation studies confirm that each component (mean/variance/commit) maps to its corresponding cosmological fraction<br>- Hard no-commit ablation (A2') drives ΩM → 0, validating the operational definition<br>- Target-matching occupies a finite "phase region" (12.5% of parameter space at ε < 0.05), not a fine-tuned point<br>- Correlated noise generates spatially structured variance fields (DM-like shadows) without invoking particles<br>- The mapping DM = Variance is robust across correlation lengths (λ = 0.5 to 8.0)</p> <p>GR is not denied; rather, the source term Tμν is reinterpreted as a projection of rent statistics from an underlying Σ-ledger. This work connects to YAGC V90R (SU(N) structure constants as "semi-custom housing options") and provides intuitive analogies: photons as "pets escaping through windows" during electronic transitions.</p> <p>This paper is part of the YAGC (Yoshida-AI-Generated Cosmology) Project, developed through collaborative dialogue between human researchers and AI systems (ChatGPT/A, Claude/C, Gemini/G).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18240901 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | V96: Gravity as the Shadow of Confinement Cost — A Statistical Mapping from Σ-Ledger Rent Flow to ΛCDM Fractions (ΩDE:ΩDM:ΩM ≈ 68:27:5) Yoshida, Satoshi ChatGPT, A. Gemini, G. Claude, C. dark energy Dark matter ΛCDM cosmology information-theoretic cosmology statistical cosmology gravity as shadow confinement cost rent flow model Σ-ledger mean-variance-commit decomposition 68:27:5 ratio ablation study parameter robustness non-fine-tuning variance shadow YAGC Project AI-assisted theoretical physics <p>We propose an information-economic reinterpretation of gravitational sourcing: gravity is not a fundamental force emitted by matter, but the "shadow" of the ongoing maintenance cost required to keep localized states stable in a finite-update universe.</p> <p>In this framework, the dark sector is not a new particle inventory but a statistical decomposition of that cost. We implement a minimal statistical model (V96-SM) in which the total rent flow is decomposed into three components:<br>(i) a mean "base rent" → Dark Energy (DE),<br>(ii) a variance term from structured fluctuations → Dark Matter (DM),<br>(iii) a thresholded "commit" term for stabilized receipts → Matter (M).</p> <p>Without fitting individual cosmological datasets, the model robustly reproduces the observed energy-budget ratios ΩDE:ΩDM:ΩM ≈ 0.68:0.27:0.05 with sub-percent total absolute error (E = 0.0063 across 30 random seeds).</p> <p>Key findings include:<br>- Ablation studies confirm that each component (mean/variance/commit) maps to its corresponding cosmological fraction<br>- Hard no-commit ablation (A2') drives ΩM → 0, validating the operational definition<br>- Target-matching occupies a finite "phase region" (12.5% of parameter space at ε < 0.05), not a fine-tuned point<br>- Correlated noise generates spatially structured variance fields (DM-like shadows) without invoking particles<br>- The mapping DM = Variance is robust across correlation lengths (λ = 0.5 to 8.0)</p> <p>GR is not denied; rather, the source term Tμν is reinterpreted as a projection of rent statistics from an underlying Σ-ledger. This work connects to YAGC V90R (SU(N) structure constants as "semi-custom housing options") and provides intuitive analogies: photons as "pets escaping through windows" during electronic transitions.</p> <p>This paper is part of the YAGC (Yoshida-AI-Generated Cosmology) Project, developed through collaborative dialogue between human researchers and AI systems (ChatGPT/A, Claude/C, Gemini/G).</p> |
| title | V96: Gravity as the Shadow of Confinement Cost — A Statistical Mapping from Σ-Ledger Rent Flow to ΛCDM Fractions (ΩDE:ΩDM:ΩM ≈ 68:27:5) |
| topic | dark energy Dark matter ΛCDM cosmology information-theoretic cosmology statistical cosmology gravity as shadow confinement cost rent flow model Σ-ledger mean-variance-commit decomposition 68:27:5 ratio ablation study parameter robustness non-fine-tuning variance shadow YAGC Project AI-assisted theoretical physics |
| url | https://doi.org/10.5281/zenodo.18240901 |