We investigate the impact of point spread function (PSF) fitting errors on cosmic shear measurements using the concepts of complexity and sparsity. Complexity, introduced in a previous paper, characterizes the number of degrees of freedom of the PSF. For instance, fitting an underlying PSF with a model with low complexity will lead to small statistical errors on the model parameters, however these parameters could suffer from large biases. Alternatively, fitting with a large number of parameters will tend to reduce biases at the expense of statistical errors. We perform an optimisation of scatters and biases by studying the mean squared error of a PSF model. We also characterize a model sparsity, which describes how efficiently the model is able to represent the underlying PSF using a limited number of free parameters. We present the general case and illustrate it for a realistic example of PSF fitted with shapelet basis sets. We derive the relation between complexity and sparsity of the PSF model, signal-to-noise ratio of stars and systematic errors on cosmological parameters. With the constraint of maintaining the systematics below the statistical uncertainties, this lead to a relation between the required number of stars to calibrate the PSF and the sparsity. We discuss the impact of our results for current and future cosmic shear surveys. In the typical case where the biases can be represented as a power law of the complexity, we show that current weak lensing surveys can calibrate the PSF with few stars, while future surveys will require hard constraints on the sparsity in order to calibrate the PSF with 50 stars.
Weak gravitational lensing is one of the most powerful tools for cosmology, while subject to challenges in quantifying subtle systematic biases. The Point Spread Function (PSF) can cause biases in weak lensing shear inference when the PSF model does not match the true PSF that is convolved with the galaxy light profile. Although the effect of PSF size and shape errors - i.e., errors in second moments - is well studied, weak lensing systematics associated with errors in higher moments of the PSF model require further investigation. The goal of our study is to estimate their potential impact for…
Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies
CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）；AIM（天体物理与宇宙学研究所）；University College London (UCL)（伦敦大学学院）；Mullard Space Science Laboratory（穆拉德空间科学实验室）；Institute of Astrophysics（天体物理研究所）
A fraction of the light observed from edge-on disk galaxies is polarized due to two physical effects: selective extinction by dust grains aligned with the magnetic field, and scattering of the anisotropic starlight field. Since the reflection and transmission coefficients of the reflecting and refracting surfaces in an optical system depend on the polarization of incoming rays, this optical polarization produces both (a) a selection bias in favor of galaxies with specific orientations and (b) a polarization-dependent PSF. In this work we build toy models to obtain for the first time an estimat…
Lensing in the Blue III: Weak Lensing Shape Catalogs of 30 Merging Galaxy Clusters
Northeastern University（东北大学）；California Institute of Technology（加州理工学院）；New York University（纽约大学）；University of Cambridge（剑桥大学）；University of Toronto（多伦多大学）；Durham University（杜伦大学）；Princeton University（普林斯顿大学）
We introduce a novel approach to reconstruct dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two components:(1) a sparsity-based component that captures the non-Gaussian structure of the field, such as peaks or halos at different spatial scales; and (2) a Gaussian random field, which is known to well represent the linear characteristics of the field.Methods. We propose an algorithm called MCALens which jointly estimates these two components. MCAlens is …
CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute of Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿交叉科学研究院）；Zhejiang Lab（浙江实验室）；Center For Astrophysics, Guangzhou University（广州大学天体物理中心）
D$_4$CNN$\times$AnaCal: Physics-Informed Machine Learning for Accurate and Precise Weak Lensing Shear Estimation
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Brookhaven National Laboratory（布鲁克海文国家实验室）；National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校超级计算应用国家中心）；Center for Artificial Intelligence Innovation, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校人工智能创新中心）
Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a so-called "physics-informed deep learning" approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys. We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM), in which a neural network learns…
The Nancy Grace Roman Space Telescope (Roman) mission is expected to launch in the mid-2020s. Its weak lensing program is designed to enable unprecedented systematics control in photometric measurements, including shear recovery, point-spread function (PSF) correction, and photometric calibration. This will enable exquisite weak lensing science and allow us to adjust to and reliably contribute to the cosmological landscape after the initial years of observations from other concurrent Stage IV dark energy experiments. This potential requires equally careful planning and requirements validation …
RPCA: a Resolved PSF reconstruction method for undersampled images
Shanghai Astronomical Observatory (SHAO)（上海天文台）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；IRFU, CEA, Université Paris-Saclay（法国原子能和替代能源署 IRFU 实验室，巴黎萨克雷大学）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
We introduce a new software package for modeling the point-spread function (PSF) of astronomical images, called Piff (PSFs In the Full FOV), which we apply to the first three years (known as Y3) of the Dark Energy Survey (DES) data. We describe the relevant details about the algorithms used by Piff to model the PSF, including how the PSF model varies across the field of view (FOV). Diagnostic results show that the systematic errors from the PSF modeling are very small over the range of scales that are important for the DES Y3 weak lensing analysis. In particular, the systematic errors from the…
