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arXiv · 2007.12795

Photo-z outlier self-calibration in weak lensing surveys

Emmanuel Schaan·Simone Ferraro·Uroš Seljak
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Abstract

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. We show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state $w_0$, but with a 65% degradation when both $w_0$ and $w_a$ are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3x2 analysis, combining cosmic shear, projected galaxy clustering and galaxy - galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed "null correlations", which are usually ignored in 3x2 analyses. Despite contributing only $\sim$ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to $\Lambda$CDM, is publicly available at https://github.com/EmmanuelSchaan/LaSSI .

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Emmanuel Schaan, Simone Ferraro, Uroš Seljak. 2020-07-24. Photo-z outlier self-calibration in weak lensing surveys. https://doi.org/10.1088/1475-7516/2020/12/001

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