Description / Abstract: This dataset accompanies the updated technical note Residual Scalar Response and Late-Time Growth Suppression in the 7-phi Framework. It contains a first-pass audit of a residual-response Model 1 growth-suppression extension. Model 1 starts from the entropy response function: mu (u) = u / (1 + u) and identifies the residual inactive response fraction as: 1 - mu (u) = 1 / (1 + u). The effective growth response is modeled as: Gₑff (k, z) = G 1 - epsilonS B (k) / (1 + u0 (1 + z) ᵖ) with optional scale protection through: B (k) = k² / (k² + kₜ²). The package includes the updated PDF note, LaTeX source, CSV outputs, RSD audit results, S8 sanity check, scale-window scan, CMB-lensing amplitude check, plots, and README. The best first-pass residual-response case improves the illustrative uncorrelated RSD f sigma 8 comparison relative to Model 0, shifts S8 from approximately 0. 834 to approximately 0. 805, and gives a CMB-lensing-style amplitude sigma8 Omegaₘ⁰. 25 approximately equal to 0. 588, close to the Planck-lensing reference value 0. 589 +/- 0. 020. A scale-windowed version also preserves the useful signal in a k8-effective approximation. This dataset is exploratory. It does not use a full covariance matrix, does not implement the model in CLASS/CAMB, and does not perform a joint likelihood fit with CMB, BAO, SNe, weak lensing, CMB lensing, or RSD data. The result should be interpreted as an audit-level indication that Model 1 deserves harder testing. DISCLAIMER Generative AI was used to assist with literature screening / coding support / draft language revision. All AI-assisted outputs were independently checked by the author, and the author takes full responsibility for the final analysis and text. This is encompassing all the work that has been done and will be done. All code is under MIT licensing. All research papers are under Creative Commons License. All code, outputs and notes are included in the reproducibility bundle zip file.
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Malin Hess (Wed,) studied this question.
synapsesocial.com/papers/69fd7f3abfa21ec5bbf07a08 — DOI: https://doi.org/10.5281/zenodo.20053583
Malin Hess
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