Unified framework proves the score function yields the minimum-variance unbiased shear estimator and that response-weighted inverse-variance weights minimize shape noise independent of galaxy shape distributions, with RDSM reducing noise by ~17.5% at LSST depth.
Forklens: Accurate weak-lensing shear measurement with deep learning
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Simulation pipeline for SKA-Mid radio galaxies shows RadioLensfit and DeepShape recover shapes with multiplicative shear bias of a few 10^{-2} and additive bias of 10^{-4}.
The paper reviews standard derivations of light deflection in curved spacetime and presents a unified geometric approach for static and rotating gravitational fields.
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Weak Lensing with the SKAO: Radio Shear Measurement
Simulation pipeline for SKA-Mid radio galaxies shows RadioLensfit and DeepShape recover shapes with multiplicative shear bias of a few 10^{-2} and additive bias of 10^{-4}.