Optimizing parameter constraints: a new tool for Fisher matrix forecasts

Understand

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.