Fast cosmological parameter estimation using neural networks

Abstract

We present a method for accelerating the calculation of 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 ΛCDM models containing the 3σWMAP1 confidence region. We also use CosmoNet to compute the WMAP3 likelihood for flat ΛCDM models and show that marginalised 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% of cosmic variance, and that CosmoNet is \sim 7 \times 10^4 faster than CAMB (for flat models) and \sim 6 \times 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.

5 pages, 5 figures, minor changes to match version accepted by MNRAS letters

Cited by in corpus (8)

• Cosmic Calibration: Constraints from the Matter Power Spectrum and the Cosmic Microwave Background
• Estimation of cosmological parameters using adaptive importance sampling
• Estimation of Primordial Spectrum with post-WMAP 3 year data
• {\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks
• Efficient Cosmological Parameter Estimation with Hamiltonian Monte Carlo
• Simulations and cosmological inference: A statistical model for power spectra means and covariances
• Foreground removal from CMB temperature maps using an MLP neural network
• Optimising Boltzmann codes for the Planck era