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Earliest physical returns distinguish labelled source points

Proved
BianchiIX.axisFamily_firstReturn_pair_injective

by trppppp · Nov 6, 2026 · Mathlib 0df444a (Lean v4.33.1)

bianchi-ix-era-statisticsdynamical-systems

For real variables p,s,a,b,cp,s,a,b,cp,s,a,b,c, set

K=a2+b2+c2−2(ab+bc+ca),S=p2+s2,C=S+K.K=a^2+b^2+c^2-2(ab+bc+ca),\qquad S=p^2+s^2,\qquad C=S+K.K=a2+b2+c2−2(ab+bc+ca),S=p2+s2,C=S+K.

The shear components and wall rates are

P=Kp−2a2+b2+c2+ab−2bc+ac,P=Kp-2a^2+b^2+c^2+ab-2bc+ac,P=Kp−2a2+b2+c2+ab−2bc+ac, Q=Ks+3(−b2+c2+ab−ac),Q=Ks+\sqrt{3}(-b^2+c^2+ab-ac),Q=Ks+3​(−b2+c2+ab−ac), r1=−S+2p,r2=−S−p+3s,r3=−S−p−3s.r_1=-S+2p,\qquad r_2=-S-p+\sqrt{3}s,\qquad r_3=-S-p-\sqrt{3}s.r1​=−S+2p,r2​=−S−p+3​s,r3​=−S−p−3​s.

These functions define the normalized vacuum Bianchi IX vector field (P,Q,r1a,r2b,r3c)(P,Q,r_1a,r_2b,r_3c)(P,Q,r1​a,r2​b,r3​c).

Let X=R5X=\mathbb R^5X=R5 with coordinates x=(p,s,a,b,c)x=(p,s,a,b,c)x=(p,s,a,b,c) and the sup norm. Write

F(x)=(P,Q,r1a,r2b,r3c),C(x)=C(p,s,a,b,c),F(x)=(P,Q,r_1a,r_2b,r_3c),\qquad \mathcal C(x)=C(p,s,a,b,c),F(x)=(P,Q,r1​a,r2​b,r3​c),C(x)=C(p,s,a,b,c),

and let W={(p,s,a,b,c)∈X:a>0, b>0, c>0}W=\{(p,s,a,b,c)\in X:a>0,\ b>0,\ c>0\}W={(p,s,a,b,c)∈X:a>0, b>0, c>0}.

Write N1(x)=aN_1(x)=aN1​(x)=a, N2(x)=bN_2(x)=bN2​(x)=b, and N3(x)=cN_3(x)=cN3​(x)=c. For initial data (x0,t0)(x_0,t_0)(x0​,t0​), let Imax⁡(x0,t0)I_{\max}(x_0,t_0)Imax​(x0​,t0​) be the union of all open convex domains of solutions with these initial data, and let αx0,t0\alpha_{x_0,t_0}αx0​,t0​​ be their compatible curve. A solution SSS has such a domain ISI_SIS​, a curve αS\alpha_SαS​, and αS(t0)=x0\alpha_S(t_0)=x_0αS​(t0​)=x0​.

Let L(X,X)\mathcal L(X,X)L(X,X) be the continuous linear operators on XXX and set

A(x)=dFx,F~(x,J)=(F(x),A(x)∘J).A(x)=dF_x,\qquad \widetilde F(x,J)=(F(x),A(x)\circ J).A(x)=dFx​,F(x,J)=(F(x),A(x)∘J).

For a fixed initial time t0t_0t0​, put

Dt0={(x,t):t∈Imax⁡(x,t0)},Φt0(x,t)=αx,t0(t),Jx,t0(t)=dxΦt0(x,t).\mathcal D_{t_0}=\{(x,t):t\in I_{\max}(x,t_0)\},\qquad \Phi_{t_0}(x,t)=\alpha_{x,t_0}(t),\qquad J_{x,t_0}(t)=d_x\Phi_{t_0}(x,t).Dt0​​={(x,t):t∈Imax​(x,t0​)},Φt0​​(x,t)=αx,t0​​(t),Jx,t0​​(t)=dx​Φt0​​(x,t).

Write λx=dCx:X→R\lambda_x=d\mathcal C_x:X\to\mathbb Rλx​=dCx​:X→R for the constraint differential.

For x=(p,s,a,b,c)x=(p,s,a,b,c)x=(p,s,a,b,c), define

H(x)=(abc)2/3,w(x)=abc(1+4H(x)),δ(x)=2K−3S,δˉ(x)=2(K+4H(x))1+4H(x),H(x)=(abc)^{2/3},\quad w(x)=abc(1+4H(x)),\quad \delta(x)=2K-3S,\quad \bar\delta(x)=\frac{2(K+4H(x))}{1+4H(x)},H(x)=(abc)2/3,w(x)=abc(1+4H(x)),δ(x)=2K−3S,δˉ(x)=1+4H(x)2(K+4H(x))​, V(x,t0,t)=w(x)det⁡Jx,t0(t)w(αx,t0(t)).V(x,t_0,t)=\frac{w(x)\det J_{x,t_0}(t)}{w(\alpha_{x,t_0}(t))}.V(x,t0​,t)=w(αx,t0​​(t))w(x)detJx,t0​​(t)​.

Write [L][L][L] for the matrix of a continuous linear operator L:X→XL:X\to XL:X→X in the standard basis. Set Ω(v0,…,v4)=det⁡(v0,…,v4)\Omega(v_0,\ldots,v_4)=\det(v_0,\ldots,v_4)Ω(v0​,…,v4​)=det(v0​,…,v4​),

ωx(v0,…,v3)=Ω(x/2,v0,…,v3)w(x),φx(v0,v1,v2)=ωx(F(x),v0,v1,v2).\omega_x(v_0,\ldots,v_3)=\frac{\Omega(x/2,v_0,\ldots,v_3)}{w(x)},\qquad \varphi_x(v_0,v_1,v_2)=\omega_x(F(x),v_0,v_1,v_2).ωx​(v0​,…,v3​)=w(x)Ω(x/2,v0​,…,v3​)​,φx​(v0​,v1​,v2​)=ωx​(F(x),v0​,v1​,v2​).

A normalized symmetry is a continuous linear equivalence L:X→XL:X\to XL:X→X satisfying

C(Lx)=C(x),Lx∈W ⟺ x∈W,F(Lx)=LF(x),w(Lx)=w(x),det⁡L=1.\mathcal C(Lx)=\mathcal C(x),\quad Lx\in W\ \Longleftrightarrow\ x\in W, \quad F(Lx)=LF(x),\quad w(Lx)=w(x),\quad\det L=1.C(Lx)=C(x),Lx∈W ⟺ x∈W,F(Lx)=LF(x),w(Lx)=w(x),detL=1.

These symmetries include the identity, composition, inverse, and the two maps

R(p,s,a,b,c)=((−p+3s)/2,(−3p−s)/2,b,c,a),S0(p,s,a,b,c)=(p,−s,a,c,b).R(p,s,a,b,c)=((-p+\sqrt3s)/2,(-\sqrt3p-s)/2,b,c,a),\qquad S_0(p,s,a,b,c)=(p,-s,a,c,b).R(p,s,a,b,c)=((−p+3​s)/2,(−3​p−s)/2,b,c,a),S0​(p,s,a,b,c)=(p,−s,a,c,b).

Transforming a solution replaces its curve α\alphaα by LαL\alphaLα and keeps its time domain.

Write Φts(x)=αx,s(t)\Phi^s_t(x)=\alpha_{x,s}(t)Φts​(x)=αx,s​(t), with domain I(x,s)I(x,s)I(x,s), and X={x:C(x)=1, Ni(x)>0 (i=1,2,3)}\mathcal X=\{x:\mathcal C(x)=1,\ N_i(x)>0\ (i=1,2,3)\}X={x:C(x)=1, Ni​(x)>0 (i=1,2,3)}. For y=(v,l,r)∈R3y=(v,l,r)\in\mathbb R^3y=(v,l,r)∈R3 and a wall height hhh, put

b=e−l,c=e−r,d=1−v2−K(h,b,c),Uh={y:d>0},ch(y)=(−d,v,h,b,c).b=e^{-l},\quad c=e^{-r},\quad d=1-v^2-K(h,b,c),\quad U_h=\{y:d>0\},\quad c_h(y)=(-\sqrt d,v,h,b,c).b=e−l,c=e−r,d=1−v2−K(h,b,c),Uh​={y:d>0},ch​(y)=(−d​,v,h,b,c).

For a normalized axis symmetry AAA, the chart is cA,h=A−1chc_{A,h}=A^{-1}c_hcA,h​=A−1ch​, with coordinates πA(x)=((Ax)1,−log⁡(Ax)3,−log⁡(Ax)4)\pi_A(x)=((Ax)_1,-\log (Ax)_3,-\log (Ax)_4)πA​(x)=((Ax)1​,−log(Ax)3​,−log(Ax)4​). At p=−dp=-\sqrt dp=−d​ its flux density and measure are

ρh(y)=p2+v2−2p2∣p∣(1+4(hbc)2/3),dμh=1Uhρh+ dy.\rho_h(y)=\frac{p^2+v^2-2p}{2|p|(1+4(hbc)^{2/3})}, \qquad d\mu_h={\bf1}_{U_h}\rho_h^+\,dy.ρh​(y)=2∣p∣(1+4(hbc)2/3)p2+v2−2p​,dμh​=1Uh​​ρh+​dy.

Write φ\varphiφ for the signed section flux and δˉ\bar\deltaδˉ for the corrected divergence. The three primary frames interchange axis zero with the chosen dominant axis.

For target frames BiB_iBi​, heights kik_iki​, and coordinate sets EiE_iEi​ put c(i,y)=cBi,ki(y)c(i,y)=c_{B_i,k_i}(y)c(i,y)=cBi​,ki​​(y), E⊔={(i,y):y∈Ei}E^\sqcup=\{(i,y):y\in E_i\}E⊔={(i,y):y∈Ei​} and νk=∑i(y↦(i,y))∗μki\nu_k=\sum_i(y\mapsto(i,y))_*\mu_{k_i}νk​=∑i​(y↦(i,y))∗​μki​​. A hit from z=(i,y)z=(i,y)z=(i,y) at time ttt starts with y∈Ukiy\in U_{k_i}y∈Uki​​ at t0t_0t0​, exists in the maximal domain, and reaches a target frame with its first wall at the prescribed height, negative first shear, and coordinates in the corresponding target set. Write H(z)H(z)H(z) for these times t>t0t>t_0t>t0​, and DE={z∈E⊔:H(z)≠∅}D_E=\{z\in E^\sqcup:H(z)\ne\varnothing\}DE​={z∈E⊔:H(z)=∅}. A wall arrival atlas from (A,h)(A,h)(A,h) gives countably many injective C1C^1C1 branches covering each crossing time in each target chart. For a branch q=(i,n)q=(i,n)q=(i,n) with map RqR_qRq​ and time τq\tau_qτq​, put

jq(y)=ρki(Rqy)∣det⁡DRq(y)∣/ρh(y).j_q(y)=\rho_{k_i}(R_qy)|\det DR_q(y)|/\rho_h(y).jq​(y)=ρki​​(Rq​y)∣detDRq​(y)∣/ρh​(y).

Given a time set III, an eligible point lies on the branch, has time in III and target in EiE_iEi​. Its delay is (τq−t0)+(\tau_q-t_0)^+(τq​−t0​)+ there and +∞+\infty+∞ otherwise. The first piece LqL_qLq​ consists of eligible points attaining the infimum of all delays with the least branch encoding.

Suppose ki>0k_i>0ki​>0 for every label and ccc is injective on E⊔E^\sqcupE⊔. For x,y∈E⊔x,y\in E^\sqcupx,y∈E⊔ let s=min⁡H(x)s=\min H(x)s=minH(x), t=min⁡H(y)t=\min H(y)t=minH(y), assuming both minima exist. Then

Φst0(c(x))=Φtt0(c(y))⟹x=y.\Phi^{t_0}_s(c(x))=\Phi^{t_0}_t(c(y))\quad\Longrightarrow\quad x=y.Φst0​​(c(x))=Φtt0​​(c(y))⟹x=y.
Preamble
import Definitions.Def_BianchiIX_Dynamics_FlowComposition
import Definitions.Def_BianchiIX_AxisAndOrbitData
import Definitions.Def_BianchiIX_AxisSectionMeasureData
import Mathlib.Analysis.Calculus.Deriv.Pow
import Mathlib.Analysis.Calculus.Deriv.Mul
import Mathlib.Analysis.Calculus.Deriv.Add
import Mathlib.Analysis.Real.Sqrt
import Mathlib.Tactic.Linarith
import Mathlib.Tactic.Ring
import Mathlib.Analysis.Calculus.ContDiff.Operations
import Mathlib.Analysis.Calculus.Deriv.Prod
import Mathlib.Analysis.Calculus.MeanValue
import Mathlib.Data.Fin.VecNotation
import Mathlib.Tactic.FinCases
import Mathlib.Tactic.FunProp
import Mathlib.Analysis.Normed.Module.FiniteDimension
import Mathlib.LinearAlgebra.Determinant
import Mathlib.Analysis.SpecialFunctions.Pow.Deriv
import Mathlib.Tactic.FieldSimp
import Mathlib.Analysis.SpecialFunctions.Integrals.Basic
import Mathlib.Analysis.ODE.ExistUnique
import Mathlib.Analysis.Calculus.FDeriv.Partial
import Mathlib.Topology.Instances.Matrix
import Mathlib.LinearAlgebra.Matrix.Trace
import Mathlib.Analysis.Calculus.FDeriv.Analytic
import Mathlib.Topology.Algebra.Module.Determinant
import Mathlib.Analysis.Calculus.Deriv.Pi
import Mathlib.Data.Real.Basic
import Mathlib.Analysis.SpecialFunctions.Sqrt
import Mathlib.Analysis.SpecialFunctions.Log.Deriv
import Mathlib.LinearAlgebra.Alternating.Curry
import Mathlib.Analysis.Calculus.ImplicitContDiff
import Mathlib.Analysis.Calculus.InverseFunctionTheorem.ContDiff
import Mathlib.Topology.Compactness.Lindelof
import Mathlib.MeasureTheory.Function.Jacobian
import Mathlib.MeasureTheory.Measure.WithDensity

noncomputable section
open BianchiIX
open Set MeasureTheory

Formal statement
theorem BianchiIX.axisFamily_firstReturn_pair_injective {ι : Type*} (B : ι → NormalizedSymmetry)
    {k : ι → ℝ} {t₀ s t : ℝ} {E : ι → Set SectionCoordinates} {x y : ι × SectionCoordinates}
    (hk : ∀ i, 0 < k i) (hcharts : InjOn (axisFamilyChart B k) (axisFamilySection E))
    (hx : x ∈ axisFamilySection E) (hy : y ∈ axisFamilySection E)
    (hs : IsLeast (axisFamilyReturnTimes B k t₀ E x) s)
    (ht : IsLeast (axisFamilyReturnTimes B k t₀ E y) t)
    (heq : normalizedMaxCurve (axisFamilyChart B k x) t₀ s =
      normalizedMaxCurve (axisFamilyChart B k y) t₀ t) : x = y := by sorry
Source
https://github.com/l46430640-del/ERA-STATISTICS-FORMAL/blob/d74c241819f896ab8d050b21ace80c8e90eabd9e/BianchiIX/Measure/AxisReturnInjectivity.lean#L30-L76

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