Robust Identification of Topological Phase Transition by Self-Supervised Machine Learning Approach

TL;DR

This paper introduces a self-supervised machine learning method to reliably identify topological phase transitions in ultracold atom systems using experimental data, overcoming limitations of traditional supervised approaches.

Contribution

The authors develop a robust self-supervised learning approach that accurately detects phase transitions without prior labeling, applicable to various models and experimental measurements.

Findings

Successfully applied to 1D and 2D exactly solvable models

Uses different input features like images and correlation functions

Demonstrates robustness against data labeling sensitivity

Abstract

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of ultracold atoms. Different from the conventional supervised learning approach, where the predicted phase transition point is very sensitive to the training region and data labeling, our self-supervised learning approach identifies the phase transition point by the largest deviation of the predicted results from the known system parameters and by the highest confidence through a systematic shift of the training regions. We demonstrate the robust application of this approach results in various 1D and 2D exactly solvable models, using different input features (time-of-flight images, spatial correlation function or density-density correlation function). As a result, our self-supervised approach should be a very general and reliable method for many condensed matter or solid-state systems to observe new states of matters solely based on experimental measurements, even without a priori knowledge of the phase transition models.