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LeanPool.Superdiffusion.Algsuperdiff.Section5.Field.LocalizedTailProfile

LocalizedTailProfile #

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

The stream-field localized resolvent-tail profile #

The logarithmic local constants in the two scale ranges give a pointwise log-subexponential profile. This module keeps the algebraic prefactor explicit and proves its finite cubic layer-cake budget.

The radius-independent coefficient in the logarithmic upper bound for the localized weighted estimate.

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    The effective upper constant at radius r = s / sqrt mu is at most its point-dependent base times 1 + log (1+s), uniformly for mu ≥ 1.

    A positive lower floor for the dimensionless frozen radius when the outer dimensionless scale is beyond one and the mass is at least one.

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      The explicit algebraic amplitude. Its two summands respectively control the averaged/gradient terms and the bounded-forcing term of the frozen tail.

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        The explicit profile for item 3 of PACKET_M2_28b.

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          The exact localized two-scale tail is bounded by the explicit log-subexponential profile, uniformly in every mass mu ≥ 1.

          theorem Algsuperdiff.Section5.Field.mul_toReal_streamAnalyticMinimalResolvent_le_profile {d : ℕ} [NeZero d] (M : Section3.ABKModel d) (omega : FullSample d M.gamma) (mu : ↑MarkovProcess.Semigroup.PositiveShift) (hmu : 1 ≤ ↑mu) {f : HCPolySupport.Vec d → ℝ} (hf : Measurable f) (hf0 : ∀ (x : HCPolySupport.Vec d), 0 ≤ f x) (hf1 : ∀ (x : HCPolySupport.Vec d), |f x| ≤ 1) {x : HCPolySupport.Vec d} {r : ℝ} (hr : 0 < r) (hs : 1 < √↑mu * r) (hzero : ∀ y ∈ HCPolySupport.euclideanBall x r, f y = 0) :

          The analytic minimal resolvent inherits the explicit pointwise log-subexponential profile. The estimate is uniform over the exhaustion cubes used to define the minimal resolvent.