
TL;DR
This paper models aging in glassy systems using tree structures to represent energy landscapes, explaining phenomena like aging, memory effects, and phase transitions through a hierarchical, level-based approach.
Contribution
It introduces a multi-level tree model for off-equilibrium dynamics in spin glasses, linking energy landscape features to aging and experimental observations.
Findings
Two-level tree models fit experimental data well
Deepest levels correspond to equilibrium, upper levels to aging
Temperature influences the transition between equilibrium and aging levels
Abstract
We present a detailed study of simple `tree' models for off equilibrium dynamics and aging in glassy systems. The simplest tree describes the landscape of a random energy model, whereas multifurcating trees occur in the solution of the Sherrington-Kirkpatrick model. An important ingredient taken from these models is the exponential distribution of deep free-energies, which translate into a power-law distribution of the residence time within metastable `valleys'. These power law distributions have infinite mean in the spin-glass phase and this leads to the aging phenomenon. To each level of the tree are associated an overlap and the exponent of the time distribution. We solve these models for a finite (but arbitrary) number of levels and show that a two level tree accounts very well for many experimental observations (thermoremanent magnetisation, a.c susceptibility, second noise…
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