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Smooth compact tests determine phase-space probabilities

Statement

Use Z=Td×Rd and dZ((x,v),(y,w))=dTd(x,y)+∣v−w∣ from Wasserstein-1 coupling cost on dimensionless phase space. Let μ and ν be Borel probability measures on Z. If

∫Zφ dμ=∫Zφ dνfor every φ∈Cc∞(Z),

where Cc∞(Z) means smooth and periodic in position with compact velocity support, then μ=ν on all Borel subsets of Z.

Facts & Assumptions

Given: The phase metric and Borel structure on Z, two Borel probability measures agreeing on smooth compactly supported tests, and the suppliers below.

[F1]

Wasserstein-1 coupling cost on dimensionless phase space: dZ is a metric generating the product Borel structure on Z.

[F2]

Explicit compactly supported smooth cutoffs: there is a smooth cutoff χR on Rd with 0≤χR≤1, equal to one on the radius-R ball and supported in the radius-2R ball; for each fixed v, χR(v)→1 as R→∞.

[F3]

Dominated convergence: bounded pointwise-convergent integrands under a finite measure may be passed to the limit in their integrals.

[F4]

Dynkin's pi-lambda theorem: probability measures agreeing on a generating pi-system agree on its generated sigma-algebra.

[F5]

The one-dimensional torus and its normalized Haar integral: for every d≥1, the normalized torus probability mTd is defined by push-forward of Lebesgue measure on [0,1)d, and λd is Lebesgue measure on Rd; the product topology has the product Borel sigma algebra.

Proof

1.1F1F2F3F5givenconstruct

Fix a bounded Lipschitz function f:Z→R. Use λd and mTd from [F5]. Choose the smooth cutoff χ of [F2] and set η(u)=cχ(2u) on Rd, where c=(∫Rdχ(2u) dλd(u))−1; its integral is positive and finite because χ=1 on the unit ball and is supported in the radius-two ball. Thus η is a nonnegative smooth probability density supported in the unit ball. For 0<ϵ<1/4, define ηϵR(v)=ϵ−dη(v/ϵ) and periodize it to Td by ηϵT(x)=∑n∈Zdϵ−dη((x~+n)/ϵ), where x~ is any representative. The sum is locally finite and independent of the representative; the fundamental-domain formula for mTd in [F5], followed by the change of variables u=(x~+n)/ϵ, gives integral one, and its support satisfies dTd(x,0)≤ϵ. Set fϵ(x,v)=∫Td∫RdηϵT(x−y)ηϵR(v−w)f(y,w) dλd(w) dmTd(y). Differentiating the compactly supported smooth kernels under the integral shows that fϵ is smooth, and their nonnegativity and unit integrals give ∥fϵ∥∞≤∥f∥∞. On their support, dTd(x,y)≤ϵ and ∣v−w∣≤ϵ, so ∥fϵ−f∥∞≤2ϵLip⁡(f). For each R>0, χR(v)fϵ(x,v) is in Cc∞(Z), so its integrals agree by the hypothesis. Let R→∞ and apply [F3] to each probability measure; then let ϵ↓0 and use uniform convergence. Thus μ and ν have equal integrals for every bounded Lipschitz f.

2.1F1F3F4step 1.1∎

If C⊆Z is a nonempty closed set, distance to C is 1-Lipschitz by the triangle inequality. Hence hn(z)=max⁡(0,1−ndZ(z,C)) is bounded Lipschitz and decreases pointwise to 1C. Step 1.1 gives equal integrals of hn, and [F3] gives μ(C)=ν(C). The empty closed set has mass zero for both. Let D:={E∈B(Z):μ(E)=ν(E)}. Since μ and ν are probabilities, Z∈D; equality of total mass makes D closed under complements in Z, and countable additivity makes it closed under countable disjoint unions. Thus D is a lambda-system. The dZ sum metric induces the product topology on Z, so its closed sets form a pi-system containing Z and generating B(Z). Apply [F4] to this pi-system and D to get μ=ν.

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