Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.
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Gaussian Process Reconstruction of Cosmological Parameters with Gravitational Wave Sirens using Machine Learning
Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.