Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research

Abstract

We establish a dataset of over $1.6\times 10^4$ experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About $33\%$ of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and OD as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.

16 pages, 4 figures

References in corpus (4)

• Observation of Solitonic Vortices in Bose-Einstein Condensates
• Creating solitons with controllable and near zero velocity in Bose-Einstein condensates
• Single-exposure absorption imaging of ultracold atoms using deep learning
• Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons