  We present a toolbox of new techniques and concepts for the efficient
forecasting of experimental sensitivities. These are applicable to a large
range of scenarios in (astro-)particle physics, and based on the Fisher
information formalism. Fisher information provides an answer to the question
what is the maximum extractable information from a given observation?. It is a
common tool for the forecasting of experimental sensitivities in many branches
of science, but rarely used in astroparticle physics or searches for particle
dark matter. After briefly reviewing the Fisher information matrix of general
Poisson likelihoods, we propose very compact expressions for estimating
expected exclusion and discovery limits (equivalent counts method). We
demonstrate by comparison with Monte Carlo results that they remain
surprisingly accurate even deep in the Poisson regime. We show how correlated
background systematics can be efficiently accounted for by a treatment based on
Gaussian random fields. Finally, we introduce the novel concept of Fisher
information flux. It can be thought of as a generalization of the commonly used
signal-to-noise ratio, while accounting for the non-local properties and
saturation effects of background and instrumental uncertainties. It is a
powerful and flexible tool ready to be used as core concept for informed
strategy development in astroparticle physics and searches for particle dark
matter.
