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April 23, 20260 citationsOpen Access

The Fisher Information Sector of Perelman Functionals on Black Hole Spatial Slices

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LLLark Laflamme

Key Points

  • The aim is to compute the Fisher information component of Perelman functionals on black hole spatial slices, bridging mathematics and gravitational physics.
  • Utilized the heat kernel to derive the Boltzmann measure from first principles.
  • Computed Perelman F and W functionals on the BTZ spatial slice.
  • Derived the identification tau = 1/(2 kappa) through three independent approaches.
  • Discovered that the curvature component carries no state-dependent information.
  • Found that the Fisher information component contains all the state-dependent information.
  • Upgraded the result F_nabla = kappa from a conditional to an unconditional identification.

Abstract

We study the Fisher information component Fₙabla of Perelman F-functional on black hole spatial slices, using the heat kernel to determine the Boltzmann measure from first principles. Perelman Ricci flow functionals (2002) have a natural statistical mechanics structure: the F-functional decomposes into a curvature component FR and a gradient component Fₙabla, where the latter is a Fisher information in the sense of Amari (1985). Despite significant interest in connecting Perelman mathematics to gravitational physics (Vacaru et al. 2013-2025, Li 2013), no explicit computation of these functionals on a black hole geometry has appeared in the literature. This paper fills that gap. We compute Perelman F and W on the BTZ spatial slice and discover a structural result: on constant-curvature backgrounds, the curvaturecomponent carries no state-dependent information, and the Fisher information component carries all of it. We then derive the identification tau = 1/ (2 kappa) from three independent routes (Lichnerowicz spectrum, Euclidean BTZ periodicity, Wiesbrock modular structure), upgrading the main result Fₙabla = kappa from conditional to unconditional.

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Cite This Study

Lark Laflamme (2026) studied this question.

synapsesocial.com/papers/69e9b91385696592c86ebfcchttps://doi.org/10.5281/zenodo.19687139
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