Equivariant localization in supergravity in odd dimensions
Edoardo Colombo, Vasil Dimitrov, Dario Martelli, Alberto Zaffaroni
TL;DR
The work develops a localization framework for odd-dimensional manifolds with boundary and applies it to five-dimensional minimal gauged supergravity. By recasting the on-shell action as an integral of a Chern–Simons-type form plus a boundary term and employing an equivariant localization on toric geometries, the authors express the renormalized action in terms of purely topological and toric data, independent of the explicit metric. The key results include a metric-agnostic derivation of the AdS$_5$ rotating black hole entropy function from topology and a general formula linking the on-shell action to the analytic continuation of Sasakian volumes via the GMS master volume. The framework offers broad potential extensions to more general flux configurations, other dimensions, and holographic contexts, providing a powerful topological handle on holographic actions and entropy functionals.
Abstract
We discuss a localization formula for certain integrals on odd-dimensional manifolds with boundaries, equipped with a Killing vector, and employ this to localize the regularised on-shell action of a large class of supersymmetric solutions of five dimensional minimal gauged supergravity. Specifically, we consider asymptotically AdS_5 solutions in the time-like class, in which the transverse Kähler foliation is assumed to be toric. We find that the background subtraction regularization method leads to an intriguing formula for the on-shell action, in terms of an analytic continuation of the Martelli-Sparks-Yau Sasakian volume. In particular, we show that the regularised on-shell action is a function of the toric data of an effective compact five-dimensional manifold, as well as of the supersymmetric Killing vector, outside the corresponding dual cone. As our main example we provide a derivation of the well-known entropy function of supersymmetric and rotating black holes in AdS_5, using only topological data.
