# Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons

arXiv: https://arxiv.org/abs/2111.04881 · HTML: https://mlsanity.com/paper/2111.04881/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 77 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Learning to Perform Physics Experiments via Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01843/) (2016)
- [Attention-based Quantum Tomography](https://mlsanity.com/paper/2006.12469/) (2020)
- [Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data](https://mlsanity.com/paper/2011.03474/) (2020)
- [Toward Robust Autotuning of Noisy Quantum Dot Devices](https://mlsanity.com/paper/2108.00043/) (2021)
- [Smart Grid Cyber Attacks Detection using Supervised Learning and Heuristic Feature Selection](https://mlsanity.com/paper/1907.03313/) (2019)
- [Machine-learning enhanced dark soliton detection in Bose-Einstein condensates](https://mlsanity.com/paper/2101.05404/) (2021)
- [Dark soliton detection using persistent homology](https://mlsanity.com/paper/2107.14594/) (2021)

## Built on by (most cited first, 4 of 4)

- [Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research](https://mlsanity.com/paper/2205.09114/) (2022)
- [Extending Explainable Boosting Machines to Scientific Image Data](https://mlsanity.com/paper/2305.16526/) (2023)
- [Learning Effective Soliton Dynamics from Scattering Data](https://mlsanity.com/paper/2607.01545/) (2026)
- [Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications](https://mlsanity.com/paper/2607.02413/) (2026)

## Abstract (arXiv)

In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, SolDet, is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.