  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 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 Bose-Einstein condensates
(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.
