Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research
We establish a dataset of over $1.6\times10^4$ 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 automatic...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ac9454 |
| _version_ | 1797632969924411392 |
|---|---|
| author | Amilson R Fritsch Shangjie Guo Sophia M Koh I B Spielman Justyna P Zwolak |
| author_facet | Amilson R Fritsch Shangjie Guo Sophia M Koh I B Spielman Justyna P Zwolak |
| author_sort | Amilson R Fritsch |
| collection | DOAJ |
| description | We establish a dataset of over $1.6\times10^4$ 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 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. |
| first_indexed | 2024-03-11T11:45:15Z |
| format | Article |
| id | doaj.art-116bb028ab514bc2b94150c94ba7ba56 |
| institution | Directory Open Access Journal |
| issn | 2632-2153 |
| language | English |
| last_indexed | 2024-03-11T11:45:15Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj.art-116bb028ab514bc2b94150c94ba7ba562023-11-09T13:53:52ZengIOP PublishingMachine Learning: Science and Technology2632-21532022-01-013404700110.1088/2632-2153/ac9454Dark solitons in Bose–Einstein condensates: a dataset for many-body physics researchAmilson R Fritsch0https://orcid.org/0000-0003-1419-8591Shangjie Guo1https://orcid.org/0000-0002-6910-636XSophia M Koh2https://orcid.org/0000-0001-5481-0285I B Spielman3https://orcid.org/0000-0003-1421-8652Justyna P Zwolak4https://orcid.org/0000-0002-2286-3208Joint Quantum Institute, National Institute of Standards and Technology, and University of Maryland , Gaithersburg, MD 20899, United States of AmericaJoint Quantum Institute, National Institute of Standards and Technology, and University of Maryland , Gaithersburg, MD 20899, United States of AmericaDepartment of Physics and Astronomy, Amherst College , Amherst, MA 01002, United States of AmericaJoint Quantum Institute, National Institute of Standards and Technology, and University of Maryland , Gaithersburg, MD 20899, United States of America; National Institute of Standards and Technology , Gaithersburg, MD 20899, United States of AmericaNational Institute of Standards and Technology , Gaithersburg, MD 20899, United States of AmericaWe establish a dataset of over $1.6\times10^4$ 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 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.https://doi.org/10.1088/2632-2153/ac9454datasetdark solitonsmachine learningsupervised learning |
| spellingShingle | Amilson R Fritsch Shangjie Guo Sophia M Koh I B Spielman Justyna P Zwolak Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research Machine Learning: Science and Technology dataset dark solitons machine learning supervised learning |
| title | Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research |
| title_full | Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research |
| title_fullStr | Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research |
| title_full_unstemmed | Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research |
| title_short | Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research |
| title_sort | dark solitons in bose einstein condensates a dataset for many body physics research |
| topic | dataset dark solitons machine learning supervised learning |
| url | https://doi.org/10.1088/2632-2153/ac9454 |
| work_keys_str_mv | AT amilsonrfritsch darksolitonsinboseeinsteincondensatesadatasetformanybodyphysicsresearch AT shangjieguo darksolitonsinboseeinsteincondensatesadatasetformanybodyphysicsresearch AT sophiamkoh darksolitonsinboseeinsteincondensatesadatasetformanybodyphysicsresearch AT ibspielman darksolitonsinboseeinsteincondensatesadatasetformanybodyphysicsresearch AT justynapzwolak darksolitonsinboseeinsteincondensatesadatasetformanybodyphysicsresearch |