Abstract
Context. We study astrometric residuals from a simultaneous fit of Hyper Suprime-Cam images. Aims. We aim to characterize these residuals and study the extent to which they are dominated by atmospheric contributions for bright sources. Methods. We used Gaussian process interpolation with a correlation function (kernel) measured from the data to smooth and correct the observed astrometric residual field. Results. We find that a Gaussian process interpolation with a von Kármán kernel allows us to reduce the covariances of astrometric residuals for nearby sources by about one order of magnitude, from 30 mas2 to 3 mas2 at angular scales of ∼1 arcmin. This also allows us to halve the rms residuals. Those reductions using Gaussian process interpolation are similar to recent result published with the Dark Energy Survey dataset. We are then able to detect the small static astrometric residuals due to the Hyper Suprime-Cam sensors effects. We discuss how the Gaussian process interpolation of astrometric residuals impacts galaxy shape measurements, particularly in the context of cosmic shear analyses at the Rubin Observatory Legacy Survey of Space and Time.
Highlights
Astrometry refers to the determination of the position of astronomical sources on the sky
We studied astrometric residuals for bright stars measured in exposures acquired with the Hyper Suprime-Cam (HSC) instrument on the Subaru telescope and we find that these residuals are dominated by E-modes
We have developed a fast Gaussian process (GP) interpolation method to model the astrometric residual field induced by atmospheric turbulence
Summary
Astrometry refers to the determination of the position of astronomical sources on the sky. For ground-based wide-field imaging, atmospheric turbulence contributes to the astrometric uncertainty budget, for Rubin Observatory observing mode, which consists of two back-to-back 15 s exposures: distortions induced by the atmosphere appear to scale empirically as Te−x1p/2 (Heymans et al 2012; Bernstein et al 2017, hereafter B17), where Texp is the integration time of an exposure This turbulence contribution correlates measured positions in an anisotropic fashion (as we will show later), with a spatial correlation pattern that varies from exposure to exposure. 6, we evaluate the expected size of turbulence-induced position offsets for Rubin Observatory and estimate the spurious shear correlations this causes if not corrected under some reduction scheme We present our conclusions in Sect. We make comparisons between our results and F20 when relevant in this paper
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