2022
DOI: 10.5802/aif.3438
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The CR Ahlfors derivative and a new invariant for spherically equivalent CR maps

Abstract: We study a CR analogue of the Ahlfors derivative for conformal immersions of Stowe that generalizes the CR Schwarzian derivative studied earlier by the second-named author. This notion possesses several important properties similar to those of the conformal counterpart and provides a new invariant for spherically equivalent CR maps from strictly pseudoconvex CR manifolds into a sphere. The invariant is computable and distinguishes many well-known sphere maps. In particular, it vanishes precisely when the map i… Show more

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Cited by 2 publications
(5 citation statements)
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“…This is similar to Reiter-Son [26] and is ultimately motivated by Huang [15]. Based on an idea originated in Lamel-Son [19] and Reiter-Son [26], we introduce a tensorial invariant for CR transversal maps from a sphere or hyperquadric into the boundary of a classical domain of type IV and prove a version of Huang-Lu-Tang-Xiao [16] boundary characterization of isometric embeddings.…”
Section: Normalization Geometric Rank and Isometric Embeddingsmentioning
confidence: 83%
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“…This is similar to Reiter-Son [26] and is ultimately motivated by Huang [15]. Based on an idea originated in Lamel-Son [19] and Reiter-Son [26], we introduce a tensorial invariant for CR transversal maps from a sphere or hyperquadric into the boundary of a classical domain of type IV and prove a version of Huang-Lu-Tang-Xiao [16] boundary characterization of isometric embeddings.…”
Section: Normalization Geometric Rank and Isometric Embeddingsmentioning
confidence: 83%
“…The notion of geometric rank is very useful in the study of sphere and hyperquadric maps, as exhibited in Huang [15] and Huang et al [16]. Moreover, it is related to the rank of the Hermitian part of the CR Ahlfors tensor of sphere and hyperquadric maps, as shown in Lamel-Son [19] and Reiter-Son [27]. Motivated by these works, we introduce the following tensor.…”
Section: Normalization Geometric Rank and Isometric Embeddingsmentioning
confidence: 99%
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