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
- the public flux observables
Support.fluxCorrectedErrorObservableSup/…SqSupare hard-wired to the origin cube (originCube d mthroughoutfluxCorrectedErrorRepresentative), and no public Section 4 observable exposes a cube parameter, so there is no second, cube-indexed observable that a translation identity could compare them to. (The atom layer beneath them is cube-indexed —Support.fluxCorrectedCoeffFamily M L m Q omegatakesQ : TriadicCube d— so a literal𝓔(z+□_m)could in principle be written at the triadic cube of scalemand indexv; no frozen §4 statement does.) For these two observables the frozen composition is the rendering, so the identities below are the definitional ones a proof actually needs: the translation passes inside theL-supremum, the translations compose additively, andy = 0is the identity;
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 #
mul_sum_indicator_goodEventAt_comp_translateandmeasure_lt_mul_sum_indicator_goodEventAtare what the centre-union step reads: the two clauses ofFrozen.Section4.minimal_scale_separationat the centreSupport.triadicLatticePoint (n-1) zhave the same law as at the origin.measure_lt_comp_translateCutoffSampleis the law-transport estimate used by the tail step.
References #
- ABK26,
d.good.event.for.lambda, (the translate convention). - ABK26,
p.minimal.scale.separation.sec4.
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.