Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
This work addresses the challenge of analyzing complex excitations in ultracold-atom experiments for researchers in physics, though it is incremental as it builds on existing methods by integrating them.
The paper tackles the problem of identifying and tracking multiple solitonic excitations in images of Bose-Einstein condensates, which suffer from information loss, by developing a framework that combines machine learning with physics-based analyses, resulting in the open-source tool SolDet that is applicable to feature identification in cold-atom images.
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.