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LeanPool.Superdiffusion.Algsuperdiff.Section4.Provider.GoodEvents.ObservableTranslate

ObservableTranslate #

Ported from scottnarmstrong/Superdiffusion at 7f20476385d4af418f110303085ad0740c9eba46. Module imports, support namespace and local option directives adapted for Lean Pool.

The per-observable translation identities of the Section 4.2 lattice maxima #

Provider/GoodEvents/Translate.lean supplies the law-level half of the translation calculus and states explicitly that the remaining half — the per-observable pointwise identities observable(z+□)(ω) = observable(□)(translate z ω) — is supplied by the individual Section 4 modules. This module discharges that half for the three observables the §4.2 lattice maxima read, and packages the compositions with the proved measure preservation.

What the frozen displays actually contain #

Throughout, the sample is translated, never the cube: the manuscript's 𝓔(z+□_m), λ_{γ,2}^{-1}(z+□_{k−2}) and 𝒢(m, y; s, ε) are rendered as compositions with Cutoff.translateCutoffSample. Consequently

Not required by any frozen statement. The lift of that coefficient covariance through the Ch02 error functional — i.e. a theorem "the (∞,2) error on y+□_m at a(ω) equals the error on □_m at a(translate y ω)" — is not proved and is not proved here. No frozen §4 statement needs it: every one of them composes with translateCutoffSample. Any future statement that wants to read the composition as a literal cube translation must prove that lemma first.

Consumers #

References #

1. The flux-corrected error observables at a translated sample #

The two public observables of p.mathcalE.annular.decomp are the ones the frozen minimal_scale_separation composes with Cutoff.translateCutoffSample. All four identities per observable are definitional (rfl, or congrArg over the proved group action).

The indicator-composition identity. Verifies and uses the preimage shape of the frozen definition (Api.goodEventAt_eq_preimage).

The identity at the observable of minimal_scale_separation's first conclusion: the k-th summand at centre y is the origin summand at the y-translated sample.

The indicator-composition identity on the bad event.

The identity at the constant 1 of minimal_scale_separation's second conclusion: the bad-event count at centre y is the origin bad-event count at the y-translated sample.

The level sets of an observable at a translated sample carry the origin mass. The general form; the two threshold specializations follow.

The t < · level set transfers: the shape a union bound over centres consumes: the Z₁ tail and the Z₂ centre union.

The transport wrapper. If f is pointwise the origin observable g read at the translated sample, its t < · set carries the origin mass. The hypotheses are discharged by §2's indicator identities and by the measurability lemmas below.

The two clauses of minimal_scale_separation at a centre #

The window sum of minimal_scale_separation's first conclusion at centre y is the origin window sum at the y-translated sample.

The Cesàro-normalized form, with the frozen prefactor left free.

The bad-event count of the second conclusion at centre y is the origin count at the y-translated sample.

The Cesàro-normalized bad-event count.

The origin window average is measurable.

The origin bad-event count is measurable.

The Z₂ centre union, first clause. The bad set of the window average at centre y has exactly the mass of the bad set at the origin — the per-centre input of the union bound over z ∈ 3^{n-1}ℤ^d ∩ □_m.

The Z₂ centre union, second clause. The same transfer for the bad-event count.