# A Fresh Approach to Forecasting in Astroparticle Physics and Dark Matter Searches

## TL;DR

This paper introduces a set of new Fisher information-based techniques for forecasting experimental sensitivities in astroparticle physics and dark matter searches, emphasizing efficiency, accuracy, and the handling of systematic uncertainties.

## Contribution

It presents compact formulas for sensitivity estimation, accounts for correlated systematics with Gaussian fields, and introduces the Fisher information flux as a novel strategic tool.

## Key findings

- Compact exclusion and discovery limit estimates are accurate compared to Monte Carlo.
- The Fisher information flux generalizes signal-to-noise ratio considering non-local effects.
- Efficient treatment of background systematics improves forecasting reliability.

## Abstract

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.