COLA-based hybrid emulator reproduces nonlinear power spectrum boosts in w0wa models to <2% error vs EuclidEmulator2 and produces <0.3σ shifts in LSST-like cosmic shear parameter constraints.
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Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
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Modeling nonlinear scales for dynamical dark energy cosmologies with COLA
COLA-based hybrid emulator reproduces nonlinear power spectrum boosts in w0wa models to <2% error vs EuclidEmulator2 and produces <0.3σ shifts in LSST-like cosmic shear parameter constraints.
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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.