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DOE OSTI · 1527381

Sky Survey Scheduling Using Reinforcement Learning

Alba Hernandez, A. F.·Nord, B.·Neilsen, E.
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Abstract

Modern cosmic sky surveys (e.g., CMB S4, DES, LSST) collect a complex diversity of astronomical objects. Each of class objects presents different requirements for observation time and sensitivity. For determining the best sequence of exposures for mapping the sky systematically, conventional scheduling methods do not optimize the use of survey time and resources. Dynamic Sky survey scheduling is an np-hard problem that has been therefore treated primarily with heuristic methods. We present an alternative scheduling method based on reinforcement learning (RL) that aims to optimize the use of telescope resources for scheduling sky surveys.

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BibTeXRIS

Alba Hernandez, A. F., Nord, B., Neilsen, E.. 2019-01-01. Sky Survey Scheduling Using Reinforcement Learning. https://www.osti.gov/biblio/1527381

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