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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 R. and Becklin, Eric and Matthews, Keith (2022) Analyzing long-term performance of the Keck-II adaptive optics system. Journal of Astronomical Telescopes, Instruments, and Systems, 8 (2). Art. No. 028004. ISSN 2329-4124. doi:10.1117/1.jatis.8.2.028004. https://resolver.caltech.edu/CaltechAUTHORS:20220721-8163000

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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. The Keck telescopes have two of the longest-running LGS-AO systems in use today, and as such they represent an excellent test-bed for processing large amounts of AO data. We use a Keck-II near infrared camera 2 (NIRC2) LGSAO surve of the Galactic Center (GC) from 2005 to 2019 for our analysis, combining image metrics with AO telemetry files, multiaperture scintillation sense/differential imaging motion monitor turbulence profiles, seeing information, weather data, and temperature readings in a compiled dataset to highlight areas of potential performance improvement. We find that image quality trends downward over time, despite multiple improvements made to Keck-II and its AO system, resulting in a 9 mas increase in the average full width at half maximum (FWHM) and a 3% decrease in the average Strehl ratio over the course of the survey. Image quality also trends upward with ambient temperature, possibly indicating the presence of uncorrected turbulence in the beam path. Using nine basic features from our dataset, we train a simple machine learning (ML) algorithm to predict the delivered image quality of NIRC2 given current atmospheric conditions, which could eventually be used for real-time observation planning and exposure time adjustments. A random forest algorithm trained on this data can predict the Strehl ratio of an image to within 18% and the FWHM to within 7%, which is a solid baseline for future applications involving more advanced ML techniques. The assembled dataset and coding tools are released to the public as a resource for testing new predictive control and point spread function-reconstruction algorithms.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1117/1.JATIS.8.2.028004DOIArticle
https://resolver.caltech.edu/CaltechAUTHORS:20201217-092406030Related ItemConference Paper
ORCID:
AuthorORCID
Lu, Jessica R.0000-0001-9611-0009
Wizinowich, Peter0000-0002-1646-442X
Ragland, Sam0000-0002-0696-1780
Lyke, Jim0000-0001-7809-7867
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, Mark R.0000-0002-6753-2066
Additional Information:© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE). Received: 13 September 2021; Accepted: 22 February 2022; Published: 30 May 2022. This paper has been previously published in SPIE conference proceedings.48 The material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 2018268910 and NSF Award AST-1518273. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. We also acknowledge support from the W. M. Keck Foundation and the Heising Science Foundation.
Funders:
Funding AgencyGrant Number
NSF Graduate Research Fellowship2018268910
NSFAST-1518273
W. M. Keck FoundationUNSPECIFIED
Heising-Simons FoundationUNSPECIFIED
Subject Keywords:adaptive optics; machine learning; predictive modeling
Issue or Number:2
DOI:10.1117/1.jatis.8.2.028004
Record Number:CaltechAUTHORS:20220721-8163000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220721-8163000
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, Andrea Ghez, Mark R. Morris, Eric Becklin, and Keith Matthews "Analyzing long-term performance of the Keck-II adaptive optics system," Journal of Astronomical Telescopes, Instruments, and Systems 8(2), 028004 (30 May 2022). https://doi.org/10.1117/1.JATIS.8.2.028004
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115731
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:22 Jul 2022 22:16
Last Modified:22 Jul 2022 22:20

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