Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology
Sut-Ieng Tam,
Keiichi Umetsu,
Adam Amara
Abstract:Likelihood-free inference provides a rigorous approach to preform Bayesian analysis using forward simulations only. The main advantage of likelihood-free methods is its ability to account for complex physical processes and observational effects in forward simulations. Here we explore the potential of likelihood-free forward modeling for Bayesian cosmological inference using the redshift evolution of the cluster abundance combined with weak-lensing mass calibration. We use two complementary likelihood-free meth… Show more
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