Optimizing parameter constraints: a new tool for Fisher matrix forecasts

Abstract

In a Bayesian context, theoretical parameters are correlated random variables. Then, the constraints on one parameter can be improved by either measuring this parameter more precisely - or by measuring the other parameters more precisely. Especially in the case of many parameters, a lengthy process of guesswork is then needed to determine the most efficient way to improve one parameter's constraints. In this short article, we highlight an extremely simple analytical expression that replaces the guesswork and that facilitates a deeper understanding of optimization with interdependent parameters.

6 pages, accepted for publication in MNRAS; v2: added an important earlier reference deriving the same formula for the case of a single parameter

References in corpus (3)

• Breaking the spell of Gaussianity: forecasting with higher order Fisher matrices
• A fast, always positive definite and normalizable approximation of non-Gaussian likelihoods
• Generalised Fisher Matrices

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