Abstract
Context.Galaxy imaging surveys observe a vast number of objects, which are ultimately affected by the instrument’s point spread function (PSF). It is weak lensing missions in particular that are aimed at measuring the shape of galaxies and PSF effects represent an significant source of systematic errors that must be handled appropriately. This requires a high level of accuracy at the modelling stage as well as in the estimation of the PSF at galaxy positions.Aims.The goal of this work is to estimate a PSF at galaxy positions, which is also referred to as a non-parametric PSF estimation and which starts from a set of noisy star image observations distributed over the focal plane. To accomplish this, we need our model to precisely capture the PSF field variations over the field of view and then to recover the PSF at the chosen positions.Methods.In this paper, we propose a new method, coined Multi-CCD (MCCD) PSF modelling, which simultaneously creates a PSF field model over the entirety of the instrument’s focal plane. It allows us to capture global as well as local PSF features through the use of two complementary models that enforce different spatial constraints. Most existing non-parametric models build one model per charge-coupled device, which can lead to difficulties in capturing global ellipticity patterns.Results.We first tested our method on a realistic simulated dataset, comparing it with two state-of-the-art PSF modelling methods (PSFEx and RCA) and finding that our method outperforms both of them. Then we contrasted our approach with PSFEx based on real data from the Canada-France Imaging Survey, which uses the Canada-France-Hawaii Telescope. We show that our PSF model is less noisy and achieves a ∼22% gain on the pixel’s root mean square error with respect toPSFEx.Conclusions.We present and share the code for a new PSF modelling algorithm that models the PSF field on all the focal plane that is mature enough to handle real data.
Highlights
Current galaxy imaging surveys, such as DES (Jarvis et al 2016), KIDS (Kuijken et al 2015), and Canada-France Imaging Survey (CFIS) (Ibata et al 2017), as well as future surveys, such as the Vera C
This has been widely used for the Hubble Space Telescope (HST) as in the case of TinyTim software (Krist et al 1995), it was later shown that a relatively simple point spread function (PSF) estimation based on the data, which does not assume a model for the instrument, provides better fits to stars with regard to both photometry and astrometry measurements (Hoffmann & Anderson 2017)
A new family of MCCD methods The MCCD methods we propose in this study are aimed at exploiting all the information available in a single exposure, which requires the handling of all charge-coupled devices (CCDs) simultaneously
Summary
Current galaxy imaging surveys, such as DES (Jarvis et al 2016), KIDS (Kuijken et al 2015), and CFIS (Ibata et al 2017), as well as future surveys, such as the Vera C. For some scientific applications, such as weak gravitational lensing (Kilbinger 2015), low-surface brightness studies (Infante-Sainz et al 2019), or analyses of diffractionlimited images in crowded stellar fields (Beltramo-Martin et al 2020), the PSF must be reconstructed with a high level of accuracy. A preliminary approach is to derive a PSF model using available information about the instrument, whereupon the model parameters are chosen by fitting observed stars in the field to yield a PSF model This has been widely used for the Hubble Space Telescope (HST) as in the case of TinyTim software (Krist et al 1995), it was later shown that a relatively simple PSF estimation based on the data, which does not assume a model for the instrument, provides better fits to stars with regard to both photometry and astrometry measurements (Hoffmann & Anderson 2017).
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