Kavli Affiliate: Katrin Heitmann
| First 5 Authors: Katrin Heitmann, David Higdon, Charles Nakhleh, Salman Habib,
| Summary:
The complexity and accuracy of current and future precision cosmology observational campaigns has made it essential to develop an efficient technique for directly combining simulation and observational datasets to determine cosmological and model parameters; a procedure we term calibration. Once a satisfactory calibration of the underlying cosmological model is achieved, independent predictions for new observations become possible. For this procedure to be effective, robust characterization of the uncertainty in the calibration process is highly desirable. In this Letter, we describe a statistical methodology which can achieve both of these goals. An application example based around dark matter structure formation simulations and a synthetic mass power spectrum dataset is used to demonstrate the approach.
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