predictions
plain-language theorem explainer
Recognition Science supplies five explicit predictions for galaxy rotation curves via this definition. Cosmologists testing ledger-based alternatives to dark matter halos would cite it when matching against flat-curve data and Tully-Fisher observations. The definition assembles the list directly from J-cost equilibrium and eight-tick phase counting.
Claim. Recognition Science predicts flat rotation curves from isothermal ledger distributions, central cores rather than cusps from ledger interactions, the Tully-Fisher relation $M ∝ v^4$ from J-cost equilibrium, MOND-like behavior at low accelerations, and a dark-matter to baryon ratio of approximately $φ^3 + 1 ≈ 5$.
background
In Recognition Science, dark matter corresponds to odd-phase ledger entries under the eight-tick cycle whose phases are $kπ/4$ for integer $k$ from 0 to 7. Galaxy halos arise from the equilibrium distribution of these ledger shadows, governed by the J-cost function induced by the multiplicative recognizer on positive ratios. The module contrasts expected Keplerian falloff $v ∝ 1/√r$ with observed constant $v$ at large radii, attributing the latter to density $ρ ∝ 1/r^2$ from J-cost balance.
proof idea
This is a direct definition that enumerates the five predictions as a literal list of strings. It draws on upstream ledger-factorization and eight-tick phase results without applying tactics or lemmas inside the body.
why it matters
The definition collects observable consequences of the ledger-distribution model, tying directly to the eight-tick octave and J-cost equilibrium in the Recognition framework. It supports empirical comparisons on core-cusp problems and Tully-Fisher scaling, though no downstream theorems currently reference it. The listed items align with the forcing-chain derivation of spatial dimensions and phase counting.
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