  Quantum gas microscopes for ultracold atoms can provide high-resolution
real-space snapshots of complex many-body systems. We implement machine
learning to analyze and classify such snapshots of ultracold atoms.
Specifically, we compare the data from an experimental realization of the
two-dimensional Fermi-Hubbard model to two theoretical approaches: a doped
quantum spin liquid state of resonating valence bond type, and the geometric
string theory, describing a state with hidden spin order. This approach
considers all available information without a potential bias towards one
particular theory by the choice of an observable and can therefore select the
theory which is more predictive in general. Up to intermediate doping values,
our algorithm tends to classify experimental snapshots as
geometric-string-like, as compared to the doped spin liquid. Our results
demonstrate the potential for machine learning in processing the wealth of data
obtained through quantum gas microscopy for new physical insights.
