Robust identification of topological phase transition by self-supervised machine learning approach

Robust identification of topological phase transition by self-supervised machine learning approach

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...

Full description

Bibliographic Details
Main Authors: Chi-Ting Ho, Daw-Wei Wang
Format: Article
Language:English
Published: IOP Publishing 2021-01-01
Series:New Journal of Physics
Subjects:
machine learning
topological phase transition
self-supervised learning
time-of-flight experiments
Online Access:https://doi.org/10.1088/1367-2630/ac1709

Internet

https://doi.org/10.1088/1367-2630/ac1709

Similar Items