In this paper, we introduce \emph{-information geometry}, an infinite dimensional framework that shares key features with the geometry of the space of probability densities on a closed manifold, while also incorporating aspects of measure-valued information geometry. We define the \emph{-probability simplex} with a noncanonical differentiable structure induced via the \emph{-root transform} from an open subset of the -sphere. This structure renders the -root map an \emph{isometry}, enabling the definition of \emph{Amari–Čencov -connections} in this setting. We further construct \emph{gradient flows} with respect to the Fisher–Rao metric, which solve an infinite-dimensional linear optimization problem. These flows are intimately linked to an \emph{integrable Hamiltonian system} via a \emph{momentum map} arising from a Hamiltonian group action on the infinite-dimensional complex projective space.

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