A comparative study of deconvolution techniques for quantum-gas microscope images
arXiv:2207.08663 · doi:10.1088/1367-2630/aced65
Abstract
Quantum-gas microscopes are used to study ultracold atoms in optical lattices at the single particle level. In these system atoms are localised on lattice sites with separations close to or below the diffraction limit. To determine the lattice occupation with high fidelity, a deconvolution of the images is often required. We compare three different techniques, a local iterative deconvolution algorithm, Wiener deconvolution and the Lucy-Richardson algorithm, using simulated microscope images. We investigate how the reconstruction fidelity scales with varying signal-to-noise ratio, lattice filling fraction, varying fluorescence levels per atom, and imaging resolution. The results of this study identify the limits of singe-atom detection and provide quantitative fidelities which are applicable for different atomic species and quantum-gas microscope setups.
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- An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
- Three-dimensional imaging of single atoms in an optical lattice via helical point-spread-function engineering
- Microsecond-scale high-survival and number-resolved detection of ytterbium atom arrays
- Commensurate and incommensurate 1D interacting quantum systems
- Realistic Neutral Atom Image Simulation
- Fast, accurate, and predictive method for atom detection in site-resolved images of microtrap arrays
- Comparison of Atom Detection Algorithms for Neutral Atom Quantum Computing
- Multi-state detection and spatial addressing in a microscope for ultracold molecules