English

Avoiding lensing bias in cosmic shear analysis

Cosmology and Nongalactic Astrophysics 2025-08-12 v2

Abstract

We show, using the pseudo-CC_\ell technique, how to estimate cosmic shear and galaxy-galaxy lensing power spectra that are insensitive to the effects of multiple sources of lensing bias including source-lens clustering, magnification bias and obscuration effects. All of these effects are of significant concern for ongoing and near-future Stage-IV cosmic shear surveys. Their common attribute is that they all introduce a cosmological dependence into the selection of the galaxy shear sample. Here, we show how a simple adaptation of the pseudo-CC_\ell method can help to suppress these biases to negligible levels in a model-independent way. Our approach is based on making pixelised maps of the shear field and then using a uniform weighting of those shear maps when extracting power spectra. To produce unbiased measurements, the weighting scheme must be independent of the cosmological signal, which makes the commonly-used inverse-variance weighting scheme unsuitable for cosmic shear measurements. We demonstrate this explicitly. A frequently-cited motivation for using inverse-variance weights is to minimize the errors on the resultant power spectra. We find that, for a Stage-IV-like survey configuration, this motivation is not compelling: the precision of power spectra recovered from uniform-weighted maps is only very slightly degraded compared to those recovered from an inverse-variance analysis, and we predict no degradation in cosmological parameter constraints. We suggest that other 2-point statistics, such as real-space correlation functions, can be rendered equally robust to these lensing biases by applying those estimators to pixelised shear maps using a uniform weighting scheme.

Keywords

Cite

@article{arxiv.2411.15063,
  title  = {Avoiding lensing bias in cosmic shear analysis},
  author = {Christopher A. J. Duncan and Michael L. Brown},
  journal= {arXiv preprint arXiv:2411.15063},
  year   = {2025}
}

Comments

12 pages, 5 figures, 1 table. Accepted to MNRAS. Comments welcomed

R2 v1 2026-06-28T20:09:12.656Z