# Shear measurement bias due to spatially varying spectral energy   distributions in galaxies

**Authors:** Sowmya Kamath, Joshua E. Meyers, Patricia R. Burchat (for the LSST, Dark Energy Science Collaboration)

arXiv: 1907.04459 · 2019-07-11

## TL;DR

This paper investigates how galaxy color gradients affect weak lensing shear measurements, showing that the bias introduced is generally below LSST's systematic uncertainty threshold, with methods validated on simulations and real galaxy data.

## Contribution

It introduces techniques to quantify shear bias from galaxy color gradients and demonstrates these biases are within acceptable limits for LSST weak lensing analyses.

## Key findings

- Color gradients induce shear biases at least twice below LSST's systematic limit.
- Biases are quantified using both simulations and real galaxy images from AEGIS.
- Analysis code and data are publicly available for reproducibility.

## Abstract

Galaxy color gradients - i.e., spectral energy distributions that vary across the galaxy profile - will impact galaxy shape measurements when the modeled point spread function (PSF) corresponds to that for a galaxy with spatially uniform color. This paper describes the techniques and results of a study of the expected impact of galaxy color gradients on weak lensing measurements with the Large Synoptic Survey Telescope (LSST) when the PSF size depends on wavelength. The bias on cosmic shear measurements from color gradients is computed both for parametric bulge+disk galaxy simulations and for more realistic chromatic galaxy surface brightness profiles based on HST V- and I-band images in the AEGIS survey. For the parametric galaxies, and for the more realistic galaxies derived from AEGIS galaxies with sufficient SNR that color gradient bias can be isolated, the predicted multiplicative shear biases due to color gradients are found to be at least a factor of 2 below the LSST full-depth requirement on the total systematic uncertainty in the redshift-dependent shear calibration. The analysis code and data products are publicly available (https://github.com/sowmyakth/measure_cg_bias).

## Full text

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## Figures

27 figures with captions in the complete paper: https://tomesphere.com/paper/1907.04459/full.md

## References

42 references — full list in the complete paper: https://tomesphere.com/paper/1907.04459/full.md

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Source: https://tomesphere.com/paper/1907.04459