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PropositionStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-10
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Pullback of a riemannian metric is riemannian exactly for immersions

Statement

Fh is Riemannian if and only if F is an immersion. In general it is positive semidefinite, with radical kerdFp at p.

Facts & Assumptions

Given: A smooth map F:MN and a Riemannian metric h.

[F1]

Pullback of a riemannian metric as a tensor: For smooth F:MN and a Riemannian metric h on N, its pullback tensor is (Fh)p(v,w)=hF(p)(dFpv,dFpw). This is def-pullback-of-a-covariant-tensor-field for the tensor in def-riemannian-metric-and-riemannian-manifold. It is always symmetric and positive semidefinite; the name does not assert positive definiteness. Smoothness and the precise immersion criterion are established next.

[F2]

Pullback of covariant tensors is smooth and functorial: If F:MN is smooth and T is a smooth covariant tensor field on N, then FT is a smooth covariant tensor field on M. Moreover, (idM)T=T,(GF)T=F(GT) for every composable smooth map G.

[F3]

Immersions, submersions, and constant-rank maps: Let F:MmNn be a smooth map. - F is an immersion at pM when dFp is injective. - F is a submersion at pM when dFp is surjective. - F has constant rank r on AM when rankpF=r for every pA (def-rank-of-a-smooth-map-at-a-point). The map is an immersion or submersion without qualification when the corresponding pointwise condition holds at every point of M.

Proof

technique · direct
1.1

Tensor pullback is smooth, and (Fh)p(v,v)=hF(p)(dFpv,dFpv)0, with equality exactly when dFpv=0. If dFp is injective at every point, the value is positive for every nonzero v, so the pullback is Riemannian.

F1F2F3given
2.1

Conversely, positive definiteness forces dFpv=0 to imply v=0, hence F is an immersion. A vector in kerdFp pairs to zero with every vector; if it is in the radical, its pairing with itself is zero, so the preceding equality forces it into kerdFp. This proves the radical assertion as well.

F1F3step 1.1

Source locator

Lee, Introduction to Smooth Manifolds, 2nd ed., Chapter 13, pp.328–332 and 341–342.

Depends on

Used by

Dependency tree · two levels

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Sources