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

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.