The Point Spread Function Reconstruction –II The Smooth PCA

2021 ◽  
Author(s):  
Lin Nie ◽  
Guoliang Li ◽  
John R Peterson ◽  
Chengliang Wei
Keyword(s):  
Principal Components ◽  
Point Spread Function ◽  
Expectation Maximization ◽  
High Efficiency ◽  
Cosmological Parameters ◽  
Weak Lensing ◽  
Reconstruction Accuracy ◽  
Point Spread ◽  
Spread Function ◽  
Star Images

Abstract Accurate shear measurement is a key topic in weak lensing community. Point Spread Function (PSF), which smears the observed galaxy image, plays one of the main roles in the systematic errors in shear measurement and must be treated carefully to avoid bias and errors in cosmological parameters. In this paper, we present new PSF measurement methods, Smooth-PCA (SPCA) and Improved-SPCA (iSPCA), which can reconstruct smooth PSFs with high efficiency. Our methods decompose the star images into smooth principal components by using the Expectation-Maximization-PCA (EMPCA) method, and the smooth principal components are composed by Moffatlets basis functions, which are derived from the Moffat function. We demonstrate our approaches based on simulated Moffat PSFs and PhoSim star images. The constructed smooth principal components show flexible and efficient as the same as EMPCA, and have more stable patterns than EMPCA under noises contamination. We then check the reconstruction accuracy on the shape of PSFs. We find that our methods are able to reconstruct the PSFs at the same precision as the EMPCA method which indicates and iSPCA are promising for weak lensing shear measurement.

scholarly journals Surface Brightness Profiles of 68 Globular Clusters Derived From Electronic Camera Recordings

1980 ◽  
Vol 85 ◽  
pp. 459-460
Author(s):  
Gerald E. Kron ◽  
Katherine C. Gordon ◽  
Anthony V. Hewitt
Keyword(s):  
Point Spread Function ◽  
Globular Clusters ◽  
Surface Brightness ◽  
Naval Observatory ◽  
Point Spread ◽  
Spread Function ◽  
Spacial Information ◽  
Cluster Profiles ◽  
Star Images ◽  
The U.S

Images of 68 globular clusters have been recorded in 125 exposures made with the electronic camera of the U.S. Naval Observatory on the 24-inch, 40-inch and 61-inch reflecting telescopes at the Flagstaff Station. The images were electronically malfocussed to allow the integration of light from the fainter cluster stars without saturation of the central portions of the brighter star images. Spacial information thus lost was partly regained by subsequent linear deconvolution of the cluster profiles by means of a star profile used as the point spread function.

scholarly journals Euclid: Nonparametric point spread function field recovery through interpolation on a graph Laplacian

2020 ◽  
Vol 636 ◽  
pp. A78 ◽  
Author(s):  
M. A. Schmitz ◽  
J.-L. Starck ◽  
F. Ngole Mboula ◽  
N. Auricchio ◽  
J. Brinchmann ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Super Resolution ◽  
Statistical Error ◽  
Graph Laplacian ◽  
Weak Lensing ◽  
Shape Measurement ◽  
Model Errors ◽  
Point Spread ◽  
Spread Function ◽  
The Impact

Context. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surveys will attain very low levels of statistical error, and systematic errors must be extremely well controlled. In particular, the point spread function (PSF) must be estimated using stars in the field, and recovered with high accuracy. Aims. The aims of this paper are twofold. Firstly, we took steps toward a nonparametric method to address the issue of recovering the PSF field, namely that of finding the correct PSF at the position of any galaxy in the field, applicable to Euclid. Our approach relies solely on the data, as opposed to parametric methods that make use of our knowledge of the instrument. Secondly, we studied the impact of imperfect PSF models on the shape measurement of galaxies themselves, and whether common assumptions about this impact hold true in an Euclid scenario. Methods. We extended the recently proposed resolved components analysis approach, which performs super-resolution on a field of under-sampled observations of a spatially varying, image-valued function. We added a spatial interpolation component to the method, making it a true 2-dimensional PSF model. We compared our approach to PSFEx, then quantified the impact of PSF recovery errors on galaxy shape measurements through image simulations. Results. Our approach yields an improvement over PSFEx in terms of the PSF model and on observed galaxy shape errors, though it is at present far from reaching the required Euclid accuracy. We also find that the usual formalism used for the propagation of PSF model errors to weak lensing quantities no longer holds in the case of an Euclid-like PSF. In particular, different shape measurement approaches can react differently to the same PSF modeling errors.

scholarly journals Atmospheric point spread function interpolation for weak lensing in short exposure imaging data

2012 ◽  
Vol 427 (3) ◽  
pp. 2572-2587 ◽  
Author(s):  
C. Chang ◽  
P. J. Marshall ◽  
J. G. Jernigan ◽  
J. R. Peterson ◽  
S. M. Kahn ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Short Exposure ◽  
Weak Lensing ◽  
Imaging Data ◽  
Point Spread ◽  
Spread Function

scholarly journals Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

2020 ◽  
Vol 493 (1) ◽  
pp. 651-660 ◽  
Author(s):  
Peng Jia ◽  
Xiyu Li ◽  
Zhengyang Li ◽  
Weinan Wang ◽  
Dongmei Cai
Keyword(s):  
Point Spread Function ◽  
Wide Field ◽  
Calibration Data ◽  
Post Processing ◽  
Denoising Autoencoder ◽  
Small Aperture ◽  
Point Spread ◽  
Spread Function ◽  
Function Modelling ◽  
Star Images

ABSTRACT The point spread function reflects the state of an optical telescope and it is important for the design of data post-processing methods. For wide-field small-aperture telescopes, the point spread function is hard to model because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose the use of a denoising autoencoder, a type of deep neural network, to model the point spread function of wide-field small-aperture telescopes. The denoising autoencoder is a point spread function modelling method, based on pure data, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them realizations of the point spread function (i.e. simulated star images). Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and it can map any star images obtained by wide-field small-aperture telescopes directly to its point spread function. This could be used to design data post-processing or optical system alignment methods.

scholarly journals Point spread function reconstruction of adaptive-optics imaging: meeting the astrometric requirements for time-delay cosmography

2021 ◽  
Vol 508 (1) ◽  
pp. 755-761
Author(s):  
Geoff C-F Chen ◽  
Tommaso Treu ◽  
Christopher D Fassnacht ◽  
Sam Ragland ◽  
Thomas Schmidt ◽  
...  
Keyword(s):  
Time Delay ◽  
Adaptive Optics ◽  
Point Spread Function ◽  
Cosmological Parameters ◽  
Hubble Constant ◽  
Point Sources ◽  
Multiple Point ◽  
Point Spread ◽  
Wide Range ◽  
Spread Function

ABSTRACT Astrometric precision and knowledge of the point spread function are key ingredients for a wide range of astrophysical studies including time-delay cosmography in which strongly lensed quasar systems are used to determine the Hubble constant and other cosmological parameters. Astrometric uncertainty on the positions of the multiply-imaged point sources contributes to the overall uncertainty in inferred distances and therefore the Hubble constant. Similarly, knowledge of the wings of the point spread function is necessary to disentangle light from the background sources and the foreground deflector. We analyse adaptive optics (AO) images of the strong lens system J 0659+1629 obtained with the W. M. Keck Observatory using the laser guide star AO system. We show that by using a reconstructed point spread function we can (i) obtain astrometric precision of <1 mas, which is more than sufficient for time-delay cosmography; and (ii) subtract all point-like images resulting in residuals consistent with the noise level. The method we have developed is not limited to strong lensing, and is generally applicable to a wide range of scientific cases that have multiple point sources nearby.

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