# Dark soliton detection using persistent homology.

@article{Leykam2022DarkSD, title={Dark soliton detection using persistent homology.}, author={Daniel Leykam and Irving Rond{\'o}n and D. G. Angelakis}, journal={Chaos}, year={2022}, volume={32 7}, pages={ 073133 } }

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…

## 3 Citations

### Combining Machine Learning with Physics: A Framework for Tracking and Sorting Multiple Dark Solitons

- PhysicsPhysical Review Research
- 2022

This paper presents a probabilistic simulation of the response of the immune system to quantum entanglement and shows clear patterns in response to the proton-proton collision.

### Topological data analysis and machine learning

- Computer Science, Physics
- 2022

A concise yet (the authors hope) comprehensive review of applications of topological data analysis to physics and machine learning problems in physics including the detection of phase transitions is presented.

### Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research

- Physics, Computer ScienceMachine Learning: Science and Technology
- 2022

A dataset of over 1.6 x 10^4 experimental images of Bose-Einstein condensates containing solitonic excitations is established to enable machine learning (ML) for many-body physics research, providing an opportunity for the data science community to develop more sophisticated analysis tools.

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