IndisputableMonolith.Cosmology.PrimordialSpectrum
This module collects the observed parameters of the primordial scalar power spectrum in Recognition Science cosmology, anchoring the framework to Planck 2018 data with n_s ≈ 0.9649. Cosmologists comparing RS-derived fluctuations to CMB spectra would cite these constants. The module is a collection of definitions and numerical anchors with no internal proofs.
claimThe scalar spectral index satisfies $n_s ≈ 0.9649$, the power spectrum takes the form $P(k) = A_s (k/k_0)^{n_s-1}$, and the tensor-to-scalar ratio is bounded above by the supplied observational limit.
background
Recognition Science derives cosmology from the J-cost functional and the phi-ladder of mass scales. The imported Constants module fixes the RS time quantum at τ₀ = 1 tick. The Cost module supplies the J-cost definition used to generate fluctuations. This module translates those structures into the standard cosmological observables for the primordial spectrum.
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
The module supplies the observational targets that downstream results such as fluctuations_from_jcost and amplitude_derivation must reproduce. It closes the link between the RS forcing chain (T5–T8) and CMB data by fixing the spectral index near 0.965.
scope and limits
- Does not derive n_s from the J-cost equation.
- Does not compute the full k-dependence of the power spectrum.
- Does not address reheating or post-inflationary evolution.
- Does not provide error bars or likelihood functions.
depends on (2)
declarations in this module (20)
-
def
spectral_index_observed -
def
spectral_tilt_observed -
def
scalar_amplitude_observed -
def
tensor_to_scalar_upper_bound -
structure
PowerSpectrum -
def
power -
def
observedSpectrum -
def
phi_prediction_tilt -
theorem
spectral_tilt_phi_connection -
def
efolds_typical -
theorem
fluctuations_from_jcost -
theorem
amplitude_derivation -
structure
TensorSpectrum -
def
tensor_to_scalar -
def
rs_prediction_r -
theorem
r_prediction -
def
running_observed -
def
fNL_observed -
def
predictions -
structure
SpectrumFalsifier