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Deep Learning-Assisted Classification of Site-Resolved Quantum Gas Microscope Images
[article]
2019
arXiv
pre-print
We present a novel method for the analysis of quantum gas microscope images, which uses deep learning to improve the fidelity with which lattice sites can be classified as occupied or unoccupied. Our method is especially suited to addressing the case of imaging without continuous cooling, in which the accuracy of existing threshold-based reconstruction methods is limited by atom motion and low photon counts. We devise two feedforward neural network architectures which are both able to improve
arXiv:1904.08074v1
fatcat:hdznndhaqzcsho4vxpt6a476tm