Photo-z induced selection biases the cosmic magnification coefficient by factors up to 3, and a Random Forest-based re-calibration of that coefficient yields nearly unbiased convergence reconstructions at bright magnitude cuts.
Mapping dark matter with cosmic magnification
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abstract
We develop a new tool to generate statistically precise dark matter maps from the cosmic magnification of galaxies with distance estimates. We show how to overcome the intrinsic clustering problem using the slope of the luminosity function, because magnificability changes strongly over the luminosity function, while intrinsic clustering only changes weakly. This may allow precision cosmology beyond most current systematic limitations. SKA is able to reconstruct projected matter density map at smoothing scale $\sim 10^{'}$ with S/N$\geq 1$, at the rate of 200-4000 deg$^2$ per year, depending on the abundance and evolution of 21cm emitting galaxies. This power of mapping dark matter is comparable to, or even better than that of cosmic shear from deep optical surveys or 21cm surveys.
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Meta-Calibration of the Cosmic Magnification Coefficient: Toward Unbiased Weak Lensing Reconstruction by Counting Galaxies
Photo-z induced selection biases the cosmic magnification coefficient by factors up to 3, and a Random Forest-based re-calibration of that coefficient yields nearly unbiased convergence reconstructions at bright magnitude cuts.