Manifolds in random media: A variational approach to the spatial probability distribution

TL;DR

This paper introduces a variational method to accurately approximate the spatial probability distribution of a zero-dimensional manifold in a random medium, improving tail estimates over previous Hamiltonian approaches.

Contribution

It develops a position-dependent variational scheme for better tail approximation of the distribution, utilizing the replica method and focusing on the replica symmetric solution.

Findings

The variational approach outperforms Hamiltonian methods in tail estimation.

Variational parameters are functions of position, enhancing accuracy.

Results are validated against numerical simulations across temperatures.

Abstract

We develop a new variational scheme to approximate the position dependent spatial probability distribution of a zero dimensional manifold in a random medium. This celebrated 'toy-model' is associated via a mapping with directed polymers in 1+1 dimension, and also describes features of the commensurate-incommensurate phase transition. It consists of a pointlike 'interface' in one dimension subject to a combination of a harmonic potential plus a random potential with long range spatial correlations. The variational approach we develop gives far better results for the tail of the spatial distribution than the hamiltonian version, developed by Mezard and Parisi, as compared with numerical simulations for a range of temperatures. This is because the variational parameters are determined as functions of position. The replica method is utilized, and solutions for the variational parameters are presented. In this paper we limit ourselves to the replica symmetric solution.