Simulation-Aided Inference in Cosmology

• Activity Number: 377
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Dave Higdon*+ and Earl Lawrence
• Companies: Los Alamos National Laboratory and Los Alamos National Laboratory
• Keywords: gaussian processes ; computer model calibration ; emulation

Abstract

In this paper we combine computationally intensive simulation results with measurements from the Sloan Digital Sky Survey (SDSS) to infer a subset of the parameters that control the Lambda-CDM model, cosmology's standard model. We describe two Bayesian approaches for carrying out this analysis. First, we describe a statistical framework adapted from Kennedy and O'Hagan (2001) and Higdon et al. (2008) to determine a posterior distribution for these cosmological parameters given the simulation output and the physical observations. Second, we show how to use the ensemble Kalman filter (Evensen, 2009) to estimate these cosmological parameters. We briefly contrast these two basic approaches for model calibration (i.e. parameter estimation).