160 citations · 618 across the 56 of their papers we have counts for
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AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
Omid Vaheb, Sebastien Fabbro, Stark Draper
In astronomical imaging, the low photon count of exposures necessitates extensive post-processing steps, including contamination removal and denoising. This paper evaluates deep-le…
Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope
Sankalp Gilda, Stark C. Draper, Sebastien Fabbro +6
We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and ob…
Astronomical Image Quality Prediction based on Environmental and Telescope Operating Conditions
Sankalp Gilda, Yuan-Sen Ting, Kanoa Withington +6
Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmo…