Sélection de la langue

Gouvernement du Canada / Government of Canada

Recherche

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

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1093/mnras/stab3243
AuteurRechercher : Identifiant ORCID : https://orcid.org/0000-0002-3645-4501; Rechercher : ; Rechercher : 1; Rechercher : ; Rechercher : ; Rechercher : ; Rechercher : ; Rechercher : ; Rechercher :
Affiliation
  1. Conseil national de recherches du Canada. Herzberg en astronomie et en astrophysique
Bailleur de fondsRechercher : University of Hawaii; Rechercher : University of Southern California; Rechercher : National Aeronautics and Space Administration; Rechercher : Space Telescope Science Institute
FormatTexte, Article
Sujetinstrumentation: miscellaneous; methods: analytical; methods: observational; methods: statistical; telescopes
Résumé
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.
Date de publication
Maison d’éditionOxford University Press
Royal Astronomical Society
Dans
  • Monthly Notices of the Royal Astronomical Society 510, nº 1 (11 novembre 2021) : 870–902.
Langueanglais
Publications évaluées par des pairsOui
Exporter la noticeExporter en format RIS
Signaler une correctionSignaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrementf0feeb21-7146-468b-a7c7-2dfcfd5a39a9
Enregistrement créé2024-02-06
Enregistrement modifié2024-02-06
Date de modification :