Papers
Topics
Authors
Recent
2000 character limit reached

Effective Free Energy Landscapes and Hawking-Page Transitions (2509.25039v1)

Published 29 Sep 2025 in gr-qc, cond-mat.stat-mech, math-ph, and math.MP

Abstract: A recent interesting development on the dynamics of black hole phase transitions, the Hawking-Page transition, has been the so-called Gibbs free energy landscape approach. In this formalism, it is assumed that there exists a canonical ensemble of a series of black hole spacetimes with arbitrary horizon radius at a given ensemble temperature. An off-shell Gibbs free energy is defined for every spacetime state in the ensemble, with the horizon radius treated as the order parameter. The minima (maxima) of this function correspond to the various stable (unstable) black hole states. This off-shell Gibbs free energy is then treated as a classical effective drift potential of an associated Fokker-Planck equation used to study the stochastic dynamics of black hole phase transition under thermal fluctuations. Additive noise, which is independent of the black hole size, is assumed in obtaining the Fokker-Planck equation. In this work we extend the previous treatment by considering the effects of multiplicative noise, namely, noise that could scale with black hole size. This leads to an effective free energy function that can be used to study the modification of the Hawking-Page transition of a black hole system. It is realized that it is generally difficult to form black holes under a multiplicative noise, unless the effective and the original free energy become extremal at the same horizon radius. For this latter situation some theoretical noise profiles which are monotonically increasing/deceasing functions of the horizon radius are considered. It is found that stronger noise disfavors the formation of black hole.

Summary

We haven't generated a summary for this paper yet.

Whiteboard

Paper to Video (Beta)

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

Collections

Sign up for free to add this paper to one or more collections.