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LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)
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Invariant square integrable kernel produces a compact intertwiner

Statement

Assume AC. Suppose T preserves a completed Lebesgue probability space and kL2(X2) satisfies k(Tx,Ty)=k(x,y) a.e. Its compact kernel operator satisfies KU=UK and KU=UK, where U=UT, even if U is not surjective. If both marginal integrals of k vanish a.e., then K1=K1=0, both operators preserve H0, and k0 implies KH00.

Facts & Assumptions

[F1]

Kernel operators are compact and the kernel-to-operator map is bounded and injective Square integrable kernels define bounded compact integral operators.

[F2]

Conjugate-transpose kernels give adjoints Conjugate transpose kernels give adjoints.

[F3]

A linear isometry has the explicit adjoint V with VU=I Hilbert cesaro averages converge to the fixed subspace.

[F4]

Finite rectangle combinations are dense in product L2 Product rectangle kernels are dense in complex l two.

[F5]

The local canonical-simple and monotone-convergence argument proves that Koopman pullback is an isometry on complex L2 Eigenfunction for a probability system.

[F6]

Preservation on generating rectangles implies preservation on the product sigma-algebra Measure preservation can be checked on a generating pi-system.

[F8]

The centered space is H0={f:f=0} Eigenfunction for a probability system.

[F9]

Proof

Given: T,k and AC as in the statement.

1.1

The preimage under T×T of E×F is T1E×T1F, of the same measure. These rectangles generate the product sigma-algebra and include the whole probability square, so F6 applies. Preimages of completed null subsets lie in the preimages of product-measurable null covers, hence are measurable and null in the completion. Thus T×T preserves the completed product. Its Koopman operator W and the factor operator U are isometries. Obtain V=U and VU=I from F3, under AC.

F3F5F6F9
2.1

For a rectangle tensor l(x,y)=a(x)b(y), the adjoint identity and its conjugate give b(Ty)f(y)dμ(y)=f,Ub=Vf,b=b(y)Vf(y)dμ(y). Multiplying by a(Tx) yields KWl=UKlV. Finite linear combinations obey the same identity. For any lL2, approximate by such combinations in kernel norm; isometry of W and F1 make both sides converge in operator norm. Hence the identity holds for every kernel.

F1F4step 1.1
3.1

Since Wk=k, step 2.1 gives K=UKV, and multiplying on the right by U gives KU=UK. The swapped conjugate kernel obeys Wk=k too: F2 makes conjugate transpose a well-defined operation on completed L2 kernel classes, algebraically W(k)=(Wk), and Wk=k. Applying the same identity to k yields KU=UK. Compactness of both operators comes from F1–F2.

F1F2step 2.1
4.1

Vanishing y-marginal gives K1=0. Vanishing x-marginal gives K1=0 after conjugation. For fH0, Kf,1=f,K1=0, and similarly for K using its double adjoint. Thus both preserve H0. If k0, F1 gives K0. Every f splits as (f)1+f0 with f0H0; since K1=0, a vector with Kf0 supplies Kf00. Therefore the restriction is nonzero. All marginal equalities are a.e. equalities of integrable sections, justified by F7.

F1F2F7F8step 3.1

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