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

Fritsch, Amilson R.; Guo, Shangjie; Koh, Sophia M.; Spielman, I.B.; Justyna P. Zwolak

doi:10.1088/2632-2153/ac9454

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

May 17, 2022

16 pages

Published in:

Mach.Learn.Sci.Tech. 3 (2022) 4, 047001

Published: Dec 21, 2022

e-Print:

2205.09114 [cond-mat.quant-gas]

DOI:

10.1088/2632-2153/ac9454

View in:

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Abstract: (IOP)

We establish a dataset of over experimental images of Bose–Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About % 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 object detector 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.

Note:

16 pages, 4 figures

dataset
dark solitons
machine learning
supervised learning

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