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 Bose–Einstein condensate (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 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.

冷原子实验中的大多数数据来自图像，其分析受到我们对数据中可能存在的模式的先入之见的限制。我们专注于检测暗孤子的定义明确的案例——表现为玻色-爱因斯坦凝聚 (BEC) 中的局部密度耗尽——使用一种可扩展到冷原子图像中模式识别一般任务的方法。在广泛的参数范围内研究孤子动力学需要分析大型数据集，这使得现有的基于人工检查的方法成为一个重大瓶颈。在这里，我们描述了一种自动分类和定位系统，用于识别原子 BEC 中的局部激发，利用深度卷积神经网络消除对人体图像检查的需要。此外，