Constraining the point spread function by using galaxy images

Nie, Lin; Li, Guoliang; Zhang, Jun; Peterson, J. R.

doi:10.1093/mnras/stab2824

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…This may be caused by the limited number of stars used for PSF modeling, and some SCAs were dropped due to the limited number of stars. This issue may be alleviated by considering all star images in the entire field, as done in Liaudat et al (2021) and Finner et al (2023a), and galaxy images may also provide some help in solving this problem (Nie et al 2021b). Additionally, SED information of the stellar objects can help WebbPSF construct more accurate PSF models, which we will explore in future work.…”

Section: Discussionmentioning

confidence: 99%

HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image

Nie,

Shan,

et al. 2024

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The James Webb Space Telescope (JWST) marks a new era of astronomical observation and discovery, providing unrivaled precision in various measurements, including photometry, morphology, and shear measurement. Accurate point-spread function (PSF) models are essential for many of these measurements. In this paper, we introduce HybPSF, a hybrid PSF construction method for JWST NIRCam imaging data that combines the WebbPSF software, which simulates the PSF for JWST, with observed data to produce more accurate and reliable PSF models. We apply this method to the SMACS J0723 imaging data and construct supplementary structures from residuals obtained by subtracting the WebbPSF PSF model from the data. Our results show that HybPSF significantly reduces discrepancies between the PSF model and the data compared to WebbPSF. Specifically, the PSF shape parameter ellipticity and size comparisons indicate that HybPSF improves precision by approximately 10 times for R 2 and 50% for e. This improvement has important implications for astronomical measurements using JWST NIRCam imaging data.

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Section: Discussionmentioning

confidence: 99%

HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image

Nie,

Shan,

et al. 2024

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“…To produce the mock samples, we first combine the fine PSFs and true PSFs measured in the 100 VOICE frames. The fine PSFs, which have a dimension of 59 × 59 pixels, are generated by a Principal Component Analysis (PCA) based method developed by our team's cooperators Nie et al (2021aNie et al ( , 2021b. Next, we shift the image using the same position shifts as the VOICE frames and down-sample the true image to the same coarse grids as in the VOICE frames.…”

Section: Mock-iii: Vst Mock and Image Restorationmentioning

confidence: 99%

How to Coadd Images. II. Anti-aliasing and PSF Deconvolution

Wang,

Shan,

Nie

et al. 2024

Res. Astron. Astrophys.

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We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the CSST/VST telescope and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, because our method relies on the inherent advantages of least squares fitting, it is more versatile and does not depend on the local uniformity hypothesis for the PSF. However, the new method consumes much more computation than the other approaches.

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“…space telescopes. Of course, if one can measure the fine PSFs on each exposure well by using a set of stars (Nie et al 2021a;Symons et al 2021) or even galaxies (Nie et al 2021b) nearby the objects, the two methods are able to provide an amazing effect on the image recovery (see the next section).…”

Section: Combination Of Up-sampling and Psf Deconvolutionmentioning

confidence: 99%

Towards Super-resolution via Iterative multi-exposure Coaddition

Wang¹,

Li²,

Kang³

2022

Preprint

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In this article, we provide an alternative up-sampling and PSF deconvolution method for the iterative multi-exposure coaddition. Different from the previous works, the new method has a ratio-correction term, which allows the iterations to converge more rapidly to an accurate representation of the underlying image than those with difference-correction terms. By employing this method, one can coadd the under-sampled multi-exposures to a super-resolution and obtain a higher peak signal-to-noise ratio. A set of simulations show that we can take many advantages of the new method, e.g. in the signal-to-noise ratio, the average deviation of all source fluxes, super-resolution, and source distortion ratio, etc., which are friendly to astronomical photometry and morphology, and benefits faint source detection and shear measurement of weak gravitational lensing. It provides an improvement in fidelity over the previous works tested in this paper.

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Constraining the point spread function by using galaxy images

Cited by 8 publications

References 51 publications

HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image

HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image

How to Coadd Images. II. Anti-aliasing and PSF Deconvolution

Towards Super-resolution via Iterative multi-exposure Coaddition

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