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

arXiv: https://arxiv.org/abs/2205.09114 · HTML: https://mlsanity.com/paper/2205.09114/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Machine-learning enhanced dark soliton detection in Bose-Einstein condensates](https://mlsanity.com/paper/2101.05404/) (2021)
- [Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons](https://mlsanity.com/paper/2111.04881/) (2021)
- [Single-exposure absorption imaging of ultracold atoms using deep learning](https://mlsanity.com/paper/2003.01643/) (2020)
- [Dark soliton detection using persistent homology](https://mlsanity.com/paper/2107.14594/) (2021)

## Built on by (most cited first, 2 of 2)

- [Can machine learning for quantum-gas experiments be explainable?](https://mlsanity.com/paper/2605.18689/) (2026)
- [Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications](https://mlsanity.com/paper/2607.02413/) (2026)

## 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.