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Robust Identification of Topological Phase Transition by Self-Supervised Machine Learning Approach
- Publication Year :
- 2021
-
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.<br />Comment: 13 pages and 7 figures
- Subjects :
- Condensed Matter - Quantum Gases
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2106.12791
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1088/1367-2630/ac1709