Authors: Kevin Napier, Vital Fernandez
Physical Science Research
Kevin Napier and Vital Fernández, both Schmidt AI in Science Fellows, participated in a Carpentry on Reinforcement Learning and are part of a group addressing the challenge of optimizing the scheduling of telescope tasks. Astronomers worldwide depend on a limited number of extremely large telescopes for their research. Due to the vast number of compelling questions in astronomy and the scarcity of available telescopes, telescope time is a highly valuable resource. Many research telescopes attempt to maximize their utility by operating in a queue mode, striving to handle a list of tasks as efficiently as possible. However, determining the most efficient order of operations presents a significant challenge due to real-time obstacles such as adverse weather, atmospheric turbulence, or cloud cover, which can alter the optimal solution.
Napier and Fernández’s team is tackling the problem of optimal queue management for telescopes. They are utilizing reinforcement learning algorithms combined with a telescope simulator to devise an optimal policy that accounts for real-time sky conditions. Their project, called “roboqueue,” aims to enable astronomers to conduct more scientific research with the limited time available on large telescopes.