Analytical noise bias correction for weak lensing shear analysis with ERA

Okura, Yuki; Futamase, Toshifumi

doi:10.1093/mnras/sty1746

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…We find that our results are generally consistent with prior studies of estimates of pixelnoise bias for isolated galaxies [23][24][25][26][27][28]. 3.…”

Section: Validation Tests For Isolated Galaxiessupporting

confidence: 91%

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Sánchez

Mendoza

Kirkby

et al. 2021

J. Cosmol. Astropart. Phys.

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The next generation of dark-energy imaging surveys — so called “Stage-IV” surveys, such as that of the Rubin Observatory Legacy Survey of Space and Time (LSST) — will cross a threshold in the number density of detected sources on the sky that requires qualitatively different image analysis and measurement techniques compared to the current generation of Stage-III surveys. In Stage-IV surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. We focus on the weak gravitational lensing probe, for which we expect the largest impact since the cosmic shear signal is primarily encoded in the estimated shapes of observed galaxies and thus directly impacted by overlaps. We introduce a framework based on the Fisher formalism to analyze the effect of the overlapping sources (“blending”) on the estimation of cosmic shear. This method gives concrete predictions for the minimum loss of information due to noise and blending for any choice of “deblending” scheme and shape-measurement algorithm. Our studies account for undetected sources but do not address their full effects and biases they may introduce. We use simulated images and predict this impact of blending for three surveys: the Dark Energy Survey (DES), the Hyper-Suprime Cam Subaru Strategic Program (HSC-SSP), and the Rubin LSST. Our methodology successfully estimates the statistical sensitivity to weak lensing for DES and HSC early results. For LSST, we present the expected loss in statistical sensitivity for the ten-year survey due to blending. We find that for approximately 62% of galaxies that are likely to be detected in full-depth LSST images, at least 1% of the flux in their pixels is from overlapping sources. We also find that the statistical correlations between measures of overlapping galaxies and, to a much lesser extent (0.2%) the higher shot noise level due to their presence, decrease the effective number density of galaxies, N eff, by ∼ 18%. We calculate an upper limit on N eff of 39.4 galaxies per arcmin2 in r band. We study the impact of stars on as a function of stellar density and illustrate the diminishing returns of extending the survey into lower Galactic latitudes. We extend the simulation-based Fisher formalism to predict the expected increase in pixel-noise bias due to blending for maximum-likelihood (ML) shape estimators. We find that noise bias depends sensitively on the particular shape estimator and measure of ensemble-average shape that is used, and properties of the galaxy that include redshift-dependent quantities such as size and luminosity. The source code for these studies is available online.[The documented software developed for the catalog-level studies are available in the open-source LSST DESC github repository https://github.com/LSSTDESC/WeakLensingDeblending. The software for analyzing one or two galaxies with user-defined parameters is in the open-source github repository https://github.com/ismael-mendoza/ShapeMeasurementFish...

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“…We find that our results are generally consistent with prior studies of estimates of pixelnoise bias for isolated galaxies [23][24][25][26][27][28]. 3.…”

Section: Validation Tests For Isolated Galaxiessupporting

confidence: 91%

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Sánchez

Mendoza

Kirkby

et al. 2021

J. Cosmol. Astropart. Phys.

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“…The background light level is much smaller than the Poisson noise of each star, and it is unlikely to significantly affect our photometry of bright stars. However the shape-fitting algorithm is known to be sensitive to background light levels, especially in the low signal-to-noise ratio (S/N) regime (Hirata & Seljak 2003;Refregier et al 2012;Okura & Futamase 2018). We attempted to model and subtract the background in each exposure using a 65 px box size and 5 px median filter while masking out the footprints of the artificial stars.…”

Section: Correction Of the Bfe In Artificial Starsmentioning

confidence: 99%

Mitigation of the Brighter-fatter Effect in the LSST Camera

Broughton,

Utsumi,

Plazas Malagón

et al. 2024

PASP

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Thick, fully depleted charge-coupled devices are known to exhibit nonlinear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect (BFE). The effect results in distorted images of bright calibration stars, creating a flux-dependent point-spread function that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). In this paper, we analyze image measurements of flat fields and artificial stars taken at different illumination levels with the LSST Camera (LSSTCam) at SLAC National Accelerator Laboratory in order to quantify this effect in the LSSTCam before and after a previously introduced correction technique. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs, which violates the fundamental assumption of this correction method. We then introduce a new method based on a physically motivated model to account for these higher-order terms in the correction, and then we test the modified correction on both data sets. We find that the new method corrects the effect in flat fields better than it corrects the effect in artificial stars, which we suggest is the result of sub-pixel physics not included in this correction model. We use these results to define a new metric for the full-well capacity of our sensors and advise image processing strategies to further limit the impact of the effect on LSST WL science pathways.

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“…A concern with measuring shapes on a coadded image is that noise correlations can result in a bias in the shape measurement even if the PSF has been made round and is exactly known (Kaiser 2000;Bernstein & Jarvis 2002;Hirata et al 2004;Refregier et al 2012;Melchior & Viola 2012;Mandelbaum et al 2015;Gurvich & Man-delbaum 2016;Herbonnet et al 2017;Okura & Futamase 2018). For example, if there are noise correlations in the 𝑥-direction, then there are different centroid uncertainties in the 𝑥 and 𝑦 directions; this propagates into different biases in object moments 𝑥 2 and 𝑦 2 , and hence a bias in the ellipticity 𝑔 1 .…”

Section: Estimation Of Additive Noise Biasmentioning

confidence: 99%

Simulating image coaddition with the Nancy Grace Roman Space Telescope: II. Analysis of the simulated images and implications for weak lensing

Yamamoto¹,

Laliotis²,

Macbeth³

et al. 2023

Preprint

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One challenge for applying current weak lensing analysis tools to the Nancy Grace Roman Space Telescope is that individual images will be undersampled. Our companion paper presented an initial application of I -an algorithm that builds an optimal mapping from input to output pixels to reconstruct a fully sampled combined image -on the Roman image simulations. In this paper, we measure the output noise power spectra, identify the sources of the major features in the power spectra, and show that simple analytic models that ignore sampling effects underestimate the power spectra of the coadded noise images. We compute the moments of both idealized injected stars and fully simulated stars in the coadded images, and their 1-and 2-point statistics. We show that the idealized injected stars have root-mean-square ellipticity errors (1 − 6) × 10 −4 per component depending on the band; the correlation functions are ≥ 2 orders of magnitude below requirements, indicating that the image combination step itself is using a small fraction of the overall Roman 2nd moment error budget, although the 4th moments are larger and warrant further investigation. The stars in the simulated sky images, which include blending and chromaticity effects, have correlation functions near the requirement level (and below the requirement level in a wide-band image constructed by stacking all 4 filters). We evaluate the noise-induced biases in the ellipticities of injected stars, and explain the resulting trends with an analytical model. We conclude by enumerating the next steps in developing an image coaddition pipeline for Roman.

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Analytical noise bias correction for weak lensing shear analysis with ERA

Cited by 7 publications

References 40 publications

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Mitigation of the Brighter-fatter Effect in the LSST Camera

Simulating image coaddition with the Nancy Grace Roman Space Telescope: II. Analysis of the simulated images and implications for weak lensing

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