# Glassy dynamics on networks: local spectra and return probabilities

## TL;DR

This paper models glassy dynamics as a Markov process on a random network of energy minima, analyzing the spectral properties and return probabilities to understand slow relaxation and aging phenomena.

## Contribution

It introduces a spectral analysis approach using the cavity method to study local density of states and return probabilities in glassy systems with random network connectivity.

## Key findings

- Local density of states exhibits a power law behavior at small eigenvalues.
- Return probabilities have power law tails influenced by trap lifetime distributions.
- Results highlight the role of network connectivity and disorder in glassy dynamics.

## Abstract

The slow relaxation and aging of glassy systems can be modelled as a Markov process on a simplified rough energy landscape: energy minima where the system tends to get trapped are taken as nodes of a random network, and the dynamics are governed by the transition rates among these. In this work we consider the case of purely activated dynamics, where the transition rates only depend on the depth of the departing trap. The random connectivity and the disorder in the trap depths make it impossible to solve the model analytically, so we base our analysis on the spectrum of eigenvalues $\lambda$ of the master operator. We compute the local density of states $\rho(\lambda|\tau)$ for traps with a fixed lifetime $\tau$ by means of the cavity method. This exhibits a power law behaviour $\rho(\lambda|\tau)\sim\tau|\lambda|^T$ in the regime of small relaxation rates $|\lambda|$, which we rationalize using a simple analytical approximation. In the time domain, we find that the probabilities of return to a starting node have a power law-tail that is determined by the distribution of excursion times $F(t)\sim t^{-(T+1)}$. We show that these results arise only by the combination of finite configuration space connectivity and glassy disorder, and interpret them in a simple physical picture dominated by jumps to deep neighbouring traps.