Improving the astrometric solution of the Hyper Suprime-Cam with anisotropic Gaussian processes

¹ LPNHE, CNRS/IN2P3, Sorbonne Université, Laboratoire de Physique Nucléaire et de Hautes Énergies, 75005 Paris, France
e-mail: pierrefrancois.leget@gmail.com
² Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics, Stanford University, Stanford, CA 94305, USA
³ Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
⁴ Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA
⁵ SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
⁶ Department of Physics and Astronomy, University of Hawai’i at Manoa, Honolulu, Hawai’i 96822, USA
⁷ E.O. Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720, USA

Received: 1 February 2021
Accepted: 17 March 2021

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.