Detecting ergodic bubbles at the crossover to many-body localization using neural networks

TL;DR

This paper introduces a neural network-based algorithm to detect ergodic bubbles in many-body localized systems, revealing their growth and distribution during the transition to delocalization, and distinguishing between different potential types.

Contribution

The study presents a novel neural network method to identify ergodic bubbles using measurable correlations, advancing understanding of the MBL transition and avalanche mechanism.

Findings

Logarithmic growth of ergodic bubbles in MBL regime

Exponential distribution of bubble sizes in MBL regime

Power-law distribution with thermal peak at criticality

Abstract

The transition between ergodic and many-body localized phases is expected to occur via an avalanche mechanism, in which \emph{ergodic bubbles} that arise due to local fluctuations in system properties thermalize their surroundings leading to delocalization of the system, unless the disorder is sufficiently strong to stop this process. We propose an algorithm based on neural networks that allows to detect the ergodic bubbles using experimentally measurable two-site correlation functions. Investigating time evolution of the system, we observe a logarithmic in time growth of the ergodic bubbles in the MBL regime. The distribution of the size of ergodic bubbles converges during time evolution to an exponentially decaying distribution in the MBL regime, and a power-law distribution with a thermal peak in the critical regime, supporting thus the scenario of delocalization through the avalanche mechanism. Our algorithm permits to pin-point quantitative differences in time evolution of systems with random and quasiperiodic potentials, as well as to identify rare (Griffiths) events. Our results open new pathways in studies of the mechanisms of thermalization of disordered many-body systems and beyond.