  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.
