Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

19 Nov 2019Hector J. HortuaRiccardo VolpiDimitri MarinelliLuigi Malagò

In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Background temperature and polarization maps. We focus our analysis on four different methods to sample the weights of the network during training: Dropout, DropConnect, Reparameterization Trick (RT), and Flipout... (read more)

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