Holomorphic maps acting as Kobayashi isometries on a family of geodesics
Filippo Bracci, Łukasz Kosiński, Włodzimierz Zwonek
TL;DR
This work characterizes when a holomorphic map $F:D\to G$ that is an isometry for the Kobayashi distance on a complete family of geodesics must be biholomorphic, proving two sharp regimes: (i) $D$ complete hyperbolic with geodesics from a fixed point, and (ii) $D,G$ $C^{2+\alpha}$-smooth bounded strongly pseudoconvex with geodesic rays landing at a boundary point. The authors deploy the scaling method to compare with the unit ball, invoke persistence and convergence of geodesics, and use a combination of properness, differential nondegeneracy, and degree arguments to deduce biholomorphism. They also demonstrate the optimality of the assumptions via holomorphic coverings and Reinhardt-domain constructions, where Kobayashi-isometric behavior along a complete geodesic family does not imply biholomorphism. The results illuminate the limits of rigidity for Kobayashi-isometries and provide concrete model arguments that connect internal and boundary geodesic geometry to holomorphic equivalence. These findings have implications for understanding when geometric constraints on invariant metrics determine complex-analytic structure.
Abstract
Consider a holomorphic map $F: D \to G$ between two domains in ${\mathbb C}^N$. Let $\mathcal F$ denote a family of geodesics for the Kobayashi distance, such that $F$ acts as an isometry on each element of $\mathcal F$. This paper is dedicated to characterizing the scenarios in which the aforementioned condition implies that $F$ is a biholomorphism. Specifically, we establish this when $D$ is a complete hyperbolic domain, and $\mathcal F$ comprises all geodesic segments originating from a specific point. Another case is when $D$ and $G$ are $C^{2+α}$-smooth bounded pseudoconvex domains, and $\mathcal F$ consists of all geodesic rays converging at a designated boundary point of $D$. Furthermore, we provide examples to demonstrate that these assumptions are essentially optimal.
