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Title: Sky Survey Scheduling Using Reinforcement Learning

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.

Authors:
 [1];  [2];  [3]
  1. Northern Illinois U.
  2. Chicago U., Astron. Astrophys. Ctr.
  3. Fermilab
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1527381
Report Number(s):
FERMILAB-POSTER-19-013-AE-CD-LDRD
1739879
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Alba Hernandez, A. F., Nord, B., and Neilsen, E. Sky Survey Scheduling Using Reinforcement Learning. United States: N. p., 2019. Web.
Alba Hernandez, A. F., Nord, B., & Neilsen, E. Sky Survey Scheduling Using Reinforcement Learning. United States.
Alba Hernandez, A. F., Nord, B., and Neilsen, E. Tue . "Sky Survey Scheduling Using Reinforcement Learning". United States. https://www.osti.gov/servlets/purl/1527381.
@article{osti_1527381,
title = {Sky Survey Scheduling Using Reinforcement Learning},
author = {Alba Hernandez, A. F. and Nord, B. and Neilsen, E.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
Other availability
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