Dark soliton detection using persistent homology

Abstract

Classifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers, but require extensive data and computational resources to train. We show how a topological data analysis technique, persistent homology, can be used to rapidly and reliably identify qualitative features in experimental image data. The identified features can be used as inputs to simple supervised machine learning models such as logistic regression models, which are easier to train. As an example we consider the identification of dark solitons using a dataset of 6257 labelled atomic Bose-Einstein condensate density images.

Published version. 8 pages, 6 figures

References in corpus (3)

• Creating solitons with controllable and near zero velocity in Bose-Einstein condensates
• An Introduction to Topological Data Analysis for Physicists: From LGM to FRBs
• Photonic band structure design using persistent homology

Cited by in corpus (4)

• Topological data analysis and machine learning
• Detecting defect dynamics in relativistic field theories far from equilibrium using topological data analysis
• Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
• Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research