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Packing selector to coherent finite-scale sources

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StickyKakeya4.packing_selector_to_finite_scale_sources

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

contact-geometrygeometric-measure-theorykakeya

Let SSS be a measurable valid one-line-per-direction selector whose unmarked carrier has packing dimension 333. There is one probability measure on its unit front which, at every sufficiently small radius, has a normalized admissible shaded, weighted source discretization drawn from SSS. For every ball at that radius, a measurable shading/weight restriction dominates the measure of the ball and has physical union inside the doubled ball.

The source retains the affine fibre mark and a nested carrier tree. The fixed measure and same-radius localization supply the scale coherence needed for a Hausdorff, rather than merely Minkowski, conclusion.

Preamble
import Definitions.Def_sticky_kakeya4_core

open MeasureTheory Set
Formal statement
namespace StickyKakeya4

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

end StickyKakeya4
Source
Chenxi Cai, source manuscript https://cchx0000.github.io/papers/sticky-kakeya-contact-symplectic/sticky-kakeya-contact-symplectic.pdf, packing-piece discretization and weighted slice-retention steps in Sections 7--9.
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What the Lean code literally says, in plain math · gpt-5

Let E4=R4E^4=\mathbb R^4E4=R4 with its Euclidean norm and inner product, and let a marked line be a triple ℓ=((θ,b),m)∈(E4×E4)×R\ell=((\theta,b),m)\in(E^4\times E^4)\times\mathbb Rℓ=((θ,b),m)∈(E4×E4)×R, with direction θ\thetaθ, offset bbb, and mark mmm. Let SSS be a measurable set of marked lines such that every ℓ=((θ,b),m)∈S\ell=((\theta,b),m)\in Sℓ=((θ,b),m)∈S satisfies ∥θ∥=1\|\theta\|=1∥θ∥=1 and ⟨b,θ⟩=0\langle b,\theta\rangle=0⟨b,θ⟩=0; for every unit θ∈E4\theta\in E^4θ∈E4 there exists exactly one ℓ∈S\ell\in Sℓ∈S with that direction; and the custom packing dimension of the unmarked carrier {(θ,b):∃m,((θ,b),m)∈S}\{(\theta,b):\exists m,((\theta,b),m)\in S\}{(θ,b):∃m,((θ,b),m)∈S} is exactly 333. The custom packing dimension is the infimum of the bounds on upper Minkowski dimensions over countable covers, and upper Minkowski dimension is defined by finite open-ball covering numbers satisfying N(P,r)≤K(max⁡{r,0})−eN(P,r)\le K(\max\{r,0\})^{-e}N(P,r)≤K(max{r,0})−e for all sufficiently small positive rrr, with finite extended-nonnegative-real e,Ke,Ke,K. Then, for every real ε>0\varepsilon>0ε>0, there exist C∈[0,∞]C\in[0,\infty]C∈[0,∞], a measure μ\muμ on E4E^4E4, and δ0>0\delta_0>0δ0​>0 such that C≠0,∞C\ne0,\inftyC=0,∞, μ\muμ is a probability measure supported on

US={b+(m+t)θ:((θ,b),m)∈S,−12≤t≤12},U_S=\{b+(m+t)\theta:((\theta,b),m)\in S,-\tfrac12\le t\le\tfrac12\},US​={b+(m+t)θ:((θ,b),m)∈S,−21​≤t≤21​},

and, for every 0<δ≤δ00<\delta\le\delta_00<δ≤δ0​, there exist a natural number nnn (possibly 000) and finite-scale data DDD indexed by Fin⁡(n)\operatorname{Fin}(n)Fin(n). These data consist of thickness h=δh=\deltah=δ, marked lines ℓi=((θi,bi),mi)∈S\ell_i=((\theta_i,b_i),m_i)\in Sℓi​=((θi​,bi​),mi​)∈S, measurable shadings Yi⊆E4Y_i\subseteq E^4Yi​⊆E4, weights wi∈[0,∞]w_i\in[0,\infty]wi​∈[0,∞], recorded fibre marks qi∈Rq_i\in\mathbb Rqi​∈R, and a nested carrier tree whose child levels are larger, child cells are contained in parent cells, and whose iiith cell contains (θi,bi)(\theta_i,b_i)(θi​,bi​). They satisfy 0<h<10<h<10<h<1, wi≤1w_i\le1wi​≤1, qi=miq_i=m_iqi​=mi​, ∥θi∥=1\|\theta_i\|=1∥θi​∥=1, ⟨bi,θi⟩=0\langle b_i,\theta_i\rangle=0⟨bi​,θi​⟩=0, every y∈Yiy\in Y_iy∈Yi​ is within distance hhh of the marked unit segment of ℓi\ell_iℓi​, and for h≤r≤1h\le r\le1h≤r≤1,

N({(θi,bi):i∈Fin⁡(n)},r)≤C(max⁡{r,0})−(3+ε).N(\{(\theta_i,b_i):i\in\operatorname{Fin}(n)\},r)\le C(\max\{r,0\})^{-(3+\varepsilon)}.N({(θi​,bi​):i∈Fin(n)},r)≤C(max{r,0})−(3+ε).

Writing fD(y)=∑iwi1Yi(y)f_D(y)=\sum_i w_i\mathbf1_{Y_i}(y)fD​(y)=∑i​wi​1Yi​​(y) and M(D)=∫fD dvol⁡M(D)=\int f_D\,d\operatorname{vol}M(D)=∫fD​dvol, one has C−1≤M(D)≤CC^{-1}\le M(D)\le CC−1≤M(D)≤C. Finally, for every x∈E4x\in E^4x∈E4 there exist data RRR with the same index set, thickness, marked lines, fibre marks, and entire tree as DDD, with measurable YiR⊆YiY_i^R\subseteq Y_iYiR​⊆Yi​ and wiR≤wiw_i^R\le w_iwiR​≤wi​, such that, for fR(y)=∑iwiR1YiR(y)f_R(y)=\sum_iw_i^R\mathbf1_{Y_i^R}(y)fR​(y)=∑i​wiR​1YiR​​(y) and M(R)=∫fR dvol⁡M(R)=\int f_R\,d\operatorname{vol}M(R)=∫fR​dvol,

{y:0<fR(y)}⊆B‾(x,2δ),μ(B(x,δ))≤CM(R).\{y:0<f_R(y)\}\subseteq\overline B(x,2\delta), \qquad \mu(B(x,\delta))\le C M(R).{y:0<fR​(y)}⊆B(x,2δ),μ(B(x,δ))≤CM(R).

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