



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/2111.04881