Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
Lensing Bias in Cosmic Shear
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abstract
Only galaxies bright enough and large enough to be unambiguously identified and measured are included in galaxy surveys used to estimate cosmic shear. We demonstrate that because gravitational lensing can scatter galaxies across the brightness and size thresholds, cosmic shear experiments suffer from lensing bias. We calculate the effect on the shear power spectrum and show that - unless corrected for - it will lead analysts to cosmological parameters estimates that are biased at the 2-3\sigma level in DETF Stage III experiments, such as the Dark Energy Survey.
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.