SMR uses multi-channel map-encoded reinforcement learning to achieve roughly 10% better time utilization than greedy baselines for single-dish radio telescope scheduling.
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A simulation-based procedure for cluster strong lensing that remaps uniform boxes and traces rays through resolved particles, finding uncorrelated line-of-sight structure shifts images by arcseconds and changes critical areas by 16+20-14 percent at zs=4.
citing papers explorer
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SMR: Scheduler with Multi-Channel Map-Encoded Reinforcement Learning for Radio Telescopes
SMR uses multi-channel map-encoded reinforcement learning to achieve roughly 10% better time utilization than greedy baselines for single-dish radio telescope scheduling.
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A Consistent Implementation of Cluster Strong Lensing in Cosmological Simulation Light Cones
A simulation-based procedure for cluster strong lensing that remaps uniform boxes and traces rays through resolved particles, finding uncorrelated line-of-sight structure shifts images by arcseconds and changes critical areas by 16+20-14 percent at zs=4.