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Near-IR Weak-lensing (NIRWL) Measurements in the CANDELS Fields. I. Point-spread Function Modeling and Systematics

Finner et al. (2023)

캔들스 필드에서의 적외선 약 중력렌즈 측정: I. 점확산함수 모델링과 시스테마틱스

Bomee Lee #2 Co-author
THE ASTROPHYSICAL JOURNAL IF 5.521 A 등급 International collaboration

Abstract

We have undertaken a near-IR weak-lensing (NIRWL) analysis of the CANDELS HST/WFC3-IR F160W observations. With the Gaia proper motion-corrected catalog as an astrometric reference, we updated the astrometry of the five CANDELS mosaics and achieved an absolute alignment within 0farcs02 ± 0farcs02, on average, which is a factor of several superior to existing mosaics. These mosaics are available to download (https://drive.google.com/drive/folders/1k9WEV3tBOuRKBlcaTJ0-wTZnUCisS__r). We investigated the systematic effects that need to be corrected for weak-lensing measurements. We find that the largest contributing systematic effect is caused by undersampling. We find a subpixel centroid dependence on the PSF shape that causes the PSF ellipticity and size to vary by up to 0.02% and 3%, respectively. Using the UDS as an example field, we show that undersampling induces a multiplicative shear bias of -0.025. We find that the brighter-fatter effect causes a 2% increase in the size of the PSF and discover a brighter-rounder effect that changes the ellipticity by 0.006. Based on the small range of slopes in a galaxy's spectral energy distribution (SED) within the WFC3-IR bandpasses, we suggest that the impact of the galaxy SED on the PSF is minor. Finally, we model the PSF of WFC3-IR F160W for weak lensing using a principal component analysis. The PSF models account for temporal and spatial variations of the PSF. The PSF corrections result in residual ellipticities and sizes, -de1- < 0.0005 ± 0.0003, -de2- < 0.0005 ± 0.0003, and -dR- < 0.0005 ± 0.0001, that are sufficient for the upcoming NIRWL search for massive overdensities in the five CANDELS fields.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

other

supporting

Techniques

classical-ML

Tasks

dimensionality-reduction

Subfield

galaxies

image

Flags

Develops AI method
· Classification confidence: 90%