Language selection

Government of Canada / Gouvernement du Canada

Search

Uncertainty-aware learning for improvements in image quality of the Canada–France–Hawaii Telescope

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1093/mnras/stab3243
AuthorSearch for: ORCID identifier: https://orcid.org/0000-0002-3645-4501; Search for: ; Search for: 1; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for:
Affiliation
  1. National Research Council Canada. Herzberg Astronomy and Astrophysics
FunderSearch for: University of Hawaii; Search for: University of Southern California; Search for: National Aeronautics and Space Administration; Search for: Space Telescope Science Institute
FormatText, Article
Subjectinstrumentation: miscellaneous; methods: analytical; methods: observational; methods: statistical; telescopes
Abstract
We leverage state-of-the-art machine learning methods and a decade’s worth of archival data from Canada–France–Hawaii Telescope (CFHT) to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT’s wide-field camera, MegaCam. Our contributions are several-fold. First, we collect, collate, and reprocess several disparate data sets gathered by CFHT scientists. Second, we predict probability distribution functions of IQ and achieve a mean absolute error of ∼0.07 arcsec for the predicted medians. Third, we explore the data-driven actuation of the 12 dome ‘vents’ installed in 2013–14 to accelerate the flushing of hot air from the dome. We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modelling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed signal-to-noise ratio. On average, the reduction is ∼ 12 per cent. Finally, we rank input features by their Shapley values to identify the most predictive variables for each observation. Our long-term goal is to construct reliable and real-time models that can forecast optimal observatory operating parameters to optimize IQ. We can then feed such forecasts into scheduling protocols and predictive maintenance routines. We anticipate that such approaches will become standard in automating observatory operations and maintenance by the time CFHT’s successor, the Maunakea Spectroscopic Explorer, is installed in the next decade.
Publication date
PublisherOxford University Press
Royal Astronomical Society
In
  • Monthly Notices of the Royal Astronomical Society 510, no. 1 (11 November 2021): 870–902.
LanguageEnglish
Peer reviewedYes
Export citationExport as RIS
Report a correctionReport a correction (opens in a new tab)
Record identifierf0feeb21-7146-468b-a7c7-2dfcfd5a39a9
Record created2024-02-06
Record modified2024-02-06
Date modified: