IndisputableMonolith.Cosmology.PrimordialSpectrum
The Cosmology.PrimordialSpectrum module assembles observed values and phi-based relations for the primordial power spectrum in Recognition Science cosmology. Cosmologists comparing RS predictions to Planck data would cite it. The module organizes declarations around the scalar spectral index without containing proofs.
claim$n_s ≈ 0.9649$ (Planck 2018). The power spectrum $P(k)$ is parameterized via tilt, amplitude, and J-cost fluctuations on the phi-ladder.
background
The module sits in the cosmology domain and imports the RS time quantum τ_{0} = 1 tick from Constants together with the J-cost framework from Cost. It introduces the primordial spectrum through declarations for the observed spectral index, tilt, scalar amplitude, tensor-to-scalar bound, and related phi predictions. The module doc-comment identifies n_s ≈ 0.9649 as the central observational landmark.
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
The module supplies the observational anchor for sibling declarations such as phi_prediction_tilt and spectral_tilt_phi_connection. It provides the Planck 2018 scalar index against which Recognition Science phi-ladder predictions are compared. No used_by edges are recorded.
scope and limits
- Does not derive n_s from the forcing chain T0-T8.
- Does not compute efolds or fluctuation amplitudes from first principles.
- Does not address tensor modes beyond the declared upper bound.
- Does not include explicit mass-ladder or Berry-threshold calculations.
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