01499nas a2200181 4500008004100000245008200041210006900123260001400192300001100206490000600217520094800223100001801171700002501189700002101214700002101235700002401256856003701280 2021 eng d00aMachine-learning enhanced dark soliton detection in Bose-Einstein condensates0 aMachinelearning enhanced dark soliton detection in BoseEinstein c6/17/2021 a0350200 v23 a
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons -- appearing as local density depletions in a BEC -- using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic Bose-Einstein condensates (BECs) utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.
1 aGuo, Shangjie1 aFritsch, Amilson, R.1 aGreenberg, Craig1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2101.05404