2020/11/05 by Sankalp Gilda, Gilda, Sankalp, Yuan-Sen Ting +15
Computer Science · Physics and Astronomy · #Adaptive optics and wavefront sensing #Advanced Image Processing Techniques #Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · pdf · doi:10.48550/arxiv.2011.03132
openalex publication_date 2020/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmospheric conditions are constantly changing. We propose to guide observatory scheduling using machine learning. Leveraging a 15-year archive of exposures, environmental, and operating conditions logged by the Canada-France-Hawaii Telescope, we construct a probabilistic data-driven model that accurately predicts image quality. We demonstrate that, by optimizing the opening and closing of twelve vents placed on the dome of the telescope, we can reduce dome-induced turbulence and improve telescope image quality by (0.05-0.2 arc-seconds). This translates to a reduction in exposure time (and hence cost) of ∼ 10-15%. Our study is the first step toward data-based optimization of the multi-million dollar operations of current and next-generation telescopes.