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Single-shot images are the standard readout of experiments with ultracoldatoms -- the tarnished looking glass into their many-body physics. Theefficient extraction of observables from single-shot images is thus crucial.Here, we demonstrate how artificial neural networks can optimize thisextraction. In contrast to standard averaging approaches, machine learningallows both one- and two-particle densities to be accurately obtained from adrastically reduced number of single-shot images. Quantum fluctuations andcorrelations are directly harnessed to obtain physical observables for bosonsin a tilted double-well potential at an unprecedented accuracy. Strikingly,machine learning also enables a reliable extraction of momentum-spaceobservables from real-space single-shot images and vice versa. This obviatesthe need for a reconfiguration of the experimental setup between in-situ andtime-of-flight imaging, thus potentially granting an outstanding reduction inresources.

Optimized Observable Readout from Single-shot Images of Ultracold Atoms via Machine Learning