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Analyzing long-term performance of the Keck-II adaptive optics system

Ramey, Emily and Lu, Jessica R. and Yin, Ruoyi and Robinson, Steve and Wizinowich, Peter and Ragland, Sam and Lyke, Jim and Jia, Siyao and Sakai, Shoko and Gautam, Abhimat and Do, Tuan and Hosek, Matthew, Jr. and Ghez, Andrea and Morris, Mark and Becklin, Eric and Matthews, Keith (2020) Analyzing long-term performance of the Keck-II adaptive optics system. In: Adaptive Optics Systems VII. Proceedings of SPIE. No.11448. Society of Photo-optical Instrumentation Engineers (SPIE) , Bellingham, WA, Art. No. 1144859. ISBN 9781510636835. https://resolver.caltech.edu/CaltechAUTHORS:20201217-092406030

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Abstract

We present an analysis of the long-term performance of the W. M. Keck Observatory Laser Guide Star Adaptive Optics (LGS-AO) system and explore factors that influence the overall AO performance most strongly. Astronomical surveys can take years or decades to finish, so it is worthwhile to characterize the AO performance on such timescales in order to better understand future results. Keck Observatory has two of the longest-running LGS-AO systems in use today and represents an excellent test-bed for investigating large amounts of AO data. Here, we use LGS-AO observations of the Galactic Center (GC) from 2005 to 2019, all taken with the NIRC2 instrument on the Keck-II telescope, for our analysis. We combine image metrics with AO telemetry files, MASS/DIMM turbulence profiles, seeing information, and weather data in one cohesive dataset to highlight areas of potential performance improvement and train a simple machine learning algorithm to predict the delivered image quality given current atmospheric conditions. The complete dataset will be released to the public as a resource for testing new predictive control and PSF-reconstruction algorithms.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1117/12.2563252DOIArticle
ORCID:
AuthorORCID
Lu, Jessica R.0000-0001-9611-0009
Wizinowich, Peter0000-0002-1646-442X
Jia, Siyao0000-0001-5341-0765
Sakai, Shoko0000-0001-5972-663X
Gautam, Abhimat0000-0002-2836-117X
Do, Tuan0000-0001-9554-6062
Hosek, Matthew, Jr.0000-0003-2874-1196
Ghez, Andrea0000-0003-3230-5055
Morris, Mark0000-0002-6753-2066
Additional Information:© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).
Subject Keywords:Adaptive optics, machine learning, predictive modeling
Series Name:Proceedings of SPIE
Issue or Number:11448
Record Number:CaltechAUTHORS:20201217-092406030
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201217-092406030
Official Citation:Emily Ramey, Jessica R. Lu, Ruoyi Yin, Steve Robinson, Peter Wizinowich, Sam Ragland, Jim Lyke, Siyao Jia, Shoko Sakai, Abhimat Gautam, Tuan Do, Matthew Hosek Jr., Andrea Ghez, Mark Morris, Eric Becklin, and Keith Matthews "Analyzing long-term performance of the Keck-II adaptive optics system", Proc. SPIE 11448, Adaptive Optics Systems VII, 1144859 (14 December 2020); https://doi.org/10.1117/12.2563252
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107145
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Dec 2020 19:37
Last Modified:17 Dec 2020 19:37

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