Neural Network Phase Estimation in Photon-Starved Coherent Beam Combination

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This work addresses phase estimation in photon-starved, multi-aperture coherent beam combination systems using neural network processing. By analyzing detection events at the dark port of a beam splitter, the model corrects random atmospheric phase fluctuations without requiring prohibitively expensive traditional adaptive optics. This approach provides a cost-effective method to combine signals from small apertures for deep-space communications.