Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
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.
Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons; S. Guo, S. M. Koh, A. R. Fritsch, I. B. Spielman, and J. P. Zwolak; Phys. Rev. Research 4 023163 (2022). oi:10.1103/PhysRevResearch.4.023163