# Dark soliton detection using persistent homology

arXiv: https://arxiv.org/abs/2107.14594 · HTML: https://mlsanity.com/paper/2107.14594/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2021.

## Built on by (most cited first, 3 of 3)

- [Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons](https://mlsanity.com/paper/2111.04881/) (2021)
- [Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research](https://mlsanity.com/paper/2205.09114/) (2022)
- [Notes on Applicability of Explainable AI Methods to Machine Learning Models Using Features Extracted by Persistent Homology](https://mlsanity.com/paper/2310.09780/) (2023)

## Abstract (arXiv)

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.