Optimizing parameter constraints: a new tool for Fisher matrix forecasts

TL;DR

This paper introduces a simple analytical expression that simplifies the process of optimizing parameter constraints in Fisher matrix forecasts, especially when parameters are correlated, reducing guesswork and enhancing understanding.

Contribution

It presents a new analytical tool that streamlines the optimization of parameter constraints in Bayesian Fisher matrix analyses involving correlated parameters.

Findings

Provides a simple analytical expression for parameter constraint optimization.

Facilitates understanding of interdependent parameter improvements.

Reduces guesswork in Fisher matrix forecast optimization.

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.