We report the designs and the fabrication of optical intensity masks which enable trapping of two-dimensional arrays of cooled atoms of two atomic species, using single laser. Compared to previous realizations using active optical components, e.g., spatial light modulators, these passive optical masks reduce the complexity of neutral-atom experiments. The optical intensity masks are easily scalable to enable the trapping of large arrays of single atoms, enabling future applications in quantum sensing, networking, and computing.
A method of near real-time detection and tracking of resident space objects (RSOs) using a convolutional neural network (CNN) and linear quadratic estimator (LQE) is proposed. Advances in machine learning architecture allow the use of low-power/cost embedded devices to perform complex classification tasks. In order to reduce the costs of tracking systems, a low-cost embedded device will be used to run a CNN detection model for RSOs in unresolved images captured by a gray-scale camera and small telescope. Detection results computed in near real-time are then passed to an LQE to compute tracking updates for the telescope mount, resulting in a fully autonomous method of optical RSO detection and tracking.
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