Abstract:Simulation-based inference has seen increasing interest in the past few years as a promising approach to modelling the non-linear scales of galaxy clustering.
The common approach, using the Gaussian process, is to train an emulator over the cosmological and galaxy--halo connection parameters independently for every scale. We present a new Gaussian process model that allows the user to extend the input parameter space dimensions and to use a non-diagonal noise covariance matrix.
We use our new framework t… Show more
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