



01635nas a2200181   4500008004100000245010500041210006900146260001500215300001200230490000600242520106000248100001801308700002001326700002501346700002101371700002401392856003701416       2022                        eng d00aCombining machine learning with physics: A framework for tracking and sorting multiple dark solitons0 aCombining machine learning with physics A framework for tracking  c06/01/2022  a023163 0 v43 a<p>In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, SolDet, is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.</p>1 aGuo, Shangjie1 aKoh, Sophia, M.1 aFritsch, Amilson, R.1 aSpielman, I., B.1 aZwolak, Justyna, P.  uhttps://arxiv.org/abs/2111.0488101358nas a2200157   4500008004100000245008900041210006900130260001500199520084100214100002501055700001801080700002001098700002101118700002401139856003701163       2022                        eng d00aDark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research0 aDark Solitons in BoseEinstein Condensates A Dataset for Manybody  c05/17/20223 a<p>We establish a dataset of over 1.6&times;104 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.</p>1 aFritsch, Amilson, R.1 aGuo, Shangjie1 aKoh, Sophia, M.1 aSpielman, I., B.1 aZwolak, Justyna, P.  uhttps://arxiv.org/abs/2205.09114