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We present a further development of a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological Bayesian inference. The algorithm, called CosmoNet, is based on training a multilayer perceptron neural network. We compute CMB power spectra (up to =2000) and matter transfer functions over a hypercube in parameter space encompassing the 4 confidence region of a selection of CMB (WMAP + high resolution experiments) and large scale structure surveys (2dF and SDSS). We work in the framework of a generic 7 parameter non-flat cosmology. Additionally we use CosmoNet to compute the WMAP 3-year, 2dF and SDSS likelihoods over the same region. We find that the average error in the power spectra is typically well below cosmic variance for spectra, and experimental likelihoods calculated to within a fraction of a log unit. We demonstrate that marginalised posteriors generated with CosmoNet spectra agree to within a few percent of those generated by CAMB parallelised over 4 CPUs, but are obtained 2-3 times faster on just a single processor. Furthermore posteriors generated directly via CosmoNet likelihoods can be obtained in less than 30 minutes on a single processor, corresponding to a speed up of a factor of 32. We also demonstrate the capabilities of CosmoNet by extending the CMB power spectra and matter transfer function training to a more generic 10 parameter cosmological model, including tensor modes, a varying equation of state of dark energy and massive neutrinos. CosmoNet and interfaces to both CosmoMC and Bayesys are publically available at www. mrao. cam. ac. uk/software/cosmonet.
Auld et al. (Thu,) studied this question.