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Hereditary finite-scale estimate to front Frostman measures

Proved
StickyKakeya4.hereditary_finite_scale_to_frostman

by sensei · Sep 26, 2026 · Mathlib 0df444a (Lean v4.33.1)

contact-geometrygeometric-measure-theorykakeya

Assume a measurable critical direction selector supplies a coherent normalized source discretization of one front probability measure and satisfies the uniform marked estimate for every fractional restriction. Applying that estimate to the localized restriction at the same radius proves, for every 0<ε<40<\varepsilon<40<ε<4,

μ(B(x,r))≤Cεr4−ε(0<r≤1).\mu(B(x,r))\le C_\varepsilon r^{4-\varepsilon} \quad(0<r\le1).μ(B(x,r))≤Cε​r4−ε(0<r≤1).

This is the scale-coherent compactness step from hereditary finite-scale control to Hausdorff dimension; unrelated scale-wise Minkowski bounds are not used as a substitute.

Preamble
import Definitions.Def_sticky_kakeya4_core

open MeasureTheory Set
Formal statement
namespace StickyKakeya4

theorem hereditary_finite_scale_to_frostman
    (selector : Set MarkedLine)
    (hmeasurable : MeasurableSet selector)
    (hvalid : ∀ line ∈ selector, IsValidLine line)
    (hselector : IsDirectionSelector selector)
    (hpacking : packingDim (lineCarrier selector) = 3)
    (hsources : HasCoherentFiniteScaleSources selector)
    (huniform : HasUniformMarkedSourceEstimate selector) :
    HasFrontFrostmanMeasures selector := by sorry

end StickyKakeya4
Source
Chenxi Cai, source manuscript https://cchx0000.github.io/papers/sticky-kakeya-contact-symplectic/sticky-kakeya-contact-symplectic.pdf, slice-Frostman lift and residual Frostman criterion in Sections 6--9.
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What the Lean code literally says, in plain math · gpt-5

Let S\mathcal SS be a measurable set of marked lines ((θ,b),m)∈(R4×R4)×R((\theta,b),m)\in(\mathbb R^4\times\mathbb R^4)\times\mathbb R((θ,b),m)∈(R4×R4)×R. Assume every member is valid, meaning ∥θ∥=1\|\theta\|=1∥θ∥=1 and ⟨b,θ⟩=0\langle b,\theta\rangle=0⟨b,θ⟩=0; every unit direction occurs in exactly one member; and the custom packing dimension of the unmarked carrier is exactly 333. Also assume the following two properties. First, for every η>0\eta>0η>0 there are a finite nonzero C∈[0,∞]C\in[0,\infty]C∈[0,∞], a probability measure μ\muμ supported on the unit front U(S)={b+(m+t)θ:((θ,b),m)∈S,−12≤t≤12}U(\mathcal S)=\{b+(m+t)\theta:((\theta,b),m)\in\mathcal S,-\tfrac12\le t\le\tfrac12\}U(S)={b+(m+t)θ:((θ,b),m)∈S,−21​≤t≤21​}, and δ0>0\delta_0>0δ0​>0 such that, for every 0<δ≤δ00<\delta\le\delta_00<δ≤δ0​, there is a normalized admissible finite-scale source DDD from S\mathcal SS of thickness δ\deltaδ and mass between C−1C^{-1}C−1 and CCC; moreover, for every x∈R4x\in\mathbb R^4x∈R4, there is a measurable shading/weight restriction RRR of DDD with the same lines, fibre marks, and carrier tree, whose positive-source set lies in B‾(x,2δ)\overline B(x,2\delta)B(x,2δ) and whose mass satisfies μ(B(x,δ))≤CM(R)\mu(B(x,\delta))\le C M(R)μ(B(x,δ))≤CM(R). Admissibility includes weights at most 111, correct fibre marks, valid lines, measurable shadings within thickness of the marked unit segments, and the covering estimate N(car⁡D,r)≤C(ofReal⁡r)−(3+η)N(\operatorname{car}D,r)\le C(\operatorname{ofReal}r)^{-(3+\eta)}N(carD,r)≤C(ofRealr)−(3+η) for δ≤r≤1\delta\le r\le1δ≤r≤1. Second, for every η>0\eta>0η>0 and every finite nonzero packing constant CpackC_{\mathrm{pack}}Cpack​, there are a finite nonzero AAA and δ0>0\delta_0>0δ0​>0 such that every sufficiently thin admissible source DDD from S\mathcal SS and every measurable shading/weight restriction RRR satisfy

∫fR2 dvol⁡≤A(ofReal⁡δD)−ηM(R),M(R)≤A(ofReal⁡δD)−ηvol⁡({fR>0}).\int f_R^2\,d\operatorname{vol}\le A(\operatorname{ofReal}\delta_D)^{-\eta}M(R), \qquad M(R)\le A(\operatorname{ofReal}\delta_D)^{-\eta}\operatorname{vol}(\{f_R>0\}).∫fR2​dvol≤A(ofRealδD​)−ηM(R),M(R)≤A(ofRealδD​)−ηvol({fR​>0}).

Then, for every real ε\varepsilonε with 0<ε<40<\varepsilon<40<ε<4, there exist a probability measure ν\nuν on R4\mathbb R^4R4 and a finite C′∈[0,∞]C'\in[0,\infty]C′∈[0,∞] such that ν(U(S)c)=0\nu(U(\mathcal S)^c)=0ν(U(S)c)=0 and, for every x∈R4x\in\mathbb R^4x∈R4 and every 0<r≤10<r\le10<r≤1,

ν(B(x,r))≤C′(ofReal⁡r)4−ε.\nu(B(x,r))\le C'(\operatorname{ofReal}r)^{4-\varepsilon}.ν(B(x,r))≤C′(ofRealr)4−ε.

The conclusion does not separately require C′≠0C'\ne0C′=0.

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