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2026
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| Online Access: | https://doi.org/10.5281/zenodo.18846065 |
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| _version_ | 1866901066184916992 |
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| author | Sukhdevbhai Shanabhai Harijan |
| author_facet | Sukhdevbhai Shanabhai Harijan |
| contents | <p>This research integrates AI with theoretical cosmology to investigate residual Time-field (γ) effects post-Hilltop inflation. </p> <p>The study combines a deep neural network surrogate model with Bayesian MCMC inference using Planck PR4 and Pantheon+ datasets.</p> <p>Key results indicate a residual energy density γ ≈ 0.0015, which partially alleviates the Hubble tension. </p> <p>The workflow is fully reproducible and includes defense-ready visualization (H(z) deviation, likelihood contours, project progress chart).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18846065 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Assisted Detection of Residual Time-Field (γ) in Hilltop Inflationary Cosmology Sukhdevbhai Shanabhai Harijan <p>This research integrates AI with theoretical cosmology to investigate residual Time-field (γ) effects post-Hilltop inflation. </p> <p>The study combines a deep neural network surrogate model with Bayesian MCMC inference using Planck PR4 and Pantheon+ datasets.</p> <p>Key results indicate a residual energy density γ ≈ 0.0015, which partially alleviates the Hubble tension. </p> <p>The workflow is fully reproducible and includes defense-ready visualization (H(z) deviation, likelihood contours, project progress chart).</p> |
| title | AI-Assisted Detection of Residual Time-Field (γ) in Hilltop Inflationary Cosmology |
| url | https://doi.org/10.5281/zenodo.18846065 |