Model Comparison of Dark Energy models Using Deep Network
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This work uses the combination of the variational auto-encoder and the generative adversarial network to compare different dark energy models in the light of the observations, e.g., the distance modulus from SNIa. The network finds the analytical variational approximation to the true posterior of the latent parameters of the models, yielding consistent model comparison results to those derived by the standard Bayesian method which suffers from the computationally expensive integral over the parameters in the product of the likelihood and the prior. The parallel computation nature of the network together with the stochastic gradient descent optimization technique lead to an efficient way to comparison the physical models given a set of observations. The converged network also provides interpolation to dataset which is useful for data reconstruction.
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