OpenAlex ·

Noise-Induced Restoration of Entropy Concavity Near Gaussian Equilibrium

Fayez Abu-Ajamieh

Published
Source
OpenAlex
Published in
arXiv (Cornell University)
DOI
10.48550/arxiv.2610.22386
Original
Read it at the publisher
Topics
Statistical MechanicsNumerical Analysis

Abstract

We study a quantitative form of noise-induced restoration of entropy concavity for one-dimensional nonequilibrium states relaxing toward a Gaussian thermal equilibrium. A partially thermalized state is modeled by $X_q=\sqrt q\,X+\sqrt{1-q}\,G$, where $G$ is Gaussian equilibrium noise and $q$ is the retained signal fraction. For two independently prepared copies we ask when the entropy of the mixed state $h(\sqrt{1-t}\,X_q+\sqrt t\,Y_q)$ is concave in the mixing parameter. The problem is motivated by entropy geometry along Ornstein--Uhlenbeck/Fokker--Planck relaxation, where the relative score is the nonequilibrium thermodynamic force and relative Fisher information is entropy-production rate. We derive an exact symmetric endpoint-curvature formula, separating a nonnegative Ornstein--Uhlenbeck restoring term from a fourth-cumulant correction, and extend it to nonsmooth generalized-Gaussian states $e^{-a|x|^p}$ for $p>3/2$. For half thermalization of a symmetric log-concave input, posterior-variance identities give a quantitative outer-region theorem confining any endpoint obstruction to two explicitly bounded near-equilibrium sectors. We also give spectral and MMSE reformulations of the auxiliary inequality $A\ge2δ$, prove it in an explicit nonlinear neighborhood of Gaussian equilibrium, and show how it sharpens the endpoint bounds. The main all-mixing result concerns the leading symmetric non-Gaussian Hermite mode: for $p_a=\phiG(1+a\He_4)$, $0

The authors’ abstract, as arXiv lists it. arXiv’s listings, abstracts included, are free for anyone to reuse.

Read it on ReadSearch

Each opens after you create an account, and brings you back to this paper.

Already a reader? Sign in

All new papers in Statistical Mechanics

Get the few papers worth your time in Statistical Mechanics every morning.

ReadSearch reads the new papers in your field each day and brings you the few worth reading, each with its key finding in a sentence.

Papers belong to their authors and publishers; every page here links to the original. All research topics