Optimized observable readout from single-shot images of ultracold atoms via machine learning

@article{Lode2021OptimizedOR,
  title={Optimized observable readout from single-shot images of ultracold atoms via machine learning},
  author={Axel U. J. Lode and Rui Lin and Miriam B{\"u}ttner and Luca Papariello and Camille L'eveque and Ramasubramanian Chitra and Marios C. Tsatsos and Dieter Jaksch and Paolo Molignini},
  journal={Physical Review A},
  year={2021}
}
Single-shot images are the standard readout of experiments with ultracold atoms -- the tarnished looking glass into their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here, we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images… 
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