Fast cosmological parameter estimation using neural networks

We present a method for accelerating the calculation of cosmic microwave background (CMB) power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called COSMONET, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released PICO algorithm of Fendt & Wandelt, but has several additional benefits in terms of simplicity, computational speed, memory requirements and ease of training. We demonstrate the capabilities of COSMONET by computing CMB power spectra over a box in the parameter space of flat Lambda cold dark matter (Lambda CDM) models containing the 3 sigma WMAP 1-year confidence region. We also use COSMONET to compute the WMAP 3-year (WMAP3) likelihood for flat Lambda CDM models and show that marginalized posteriors on parameters derived are very similar to those obtained using CAMB and the WMAP3 code. We find that the average error in the power spectra is typically 2-3 per cent of cosmic variance, and that COSMONET is similar to 7 x 10(4) faster than CAMB (for flat models) and similar to 6 x 10(6) times faster than the official WMAP3 likelihood code. COSMONET and an interface to COSMOMC are publically available at www.mrao.cam.ac.uk/software/cosmonet.