Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research
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Abstract (arXiv)
We establish a dataset of over 1.6×104 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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 17 May 2022.
Builds on
7 of 25 references.
References · 7
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
- 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
- 2021Machine-learning enhanced dark soliton detection in Bose-Einstein condensatescited by 5
- 2021Combining machine learning with physics: A framework for tracking and sorting multiple dark solitonscited by 4
- 2020Single-exposure absorption imaging of ultracold atoms using deep learningcited by 3
- 2021Dark soliton detection using persistent homologycited by 3
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Citing papers per year: 2026: 2.
2 papers.