The Fisher gAlaxy suRvey cOde ($\texttt{FARO}$)

TL;DR

FARO is a new Python tool that efficiently computes Fisher matrices for galaxy survey observables, enabling flexible, model-independent forecasts of survey sensitivities across redshift and scale.

Contribution

The paper introduces FARO, a fast, user-friendly Python code for Fisher matrix analysis of galaxy surveys, supporting multiple observables and flexible parameterizations.

Findings

FARO can forecast sensitivities for surveys like DESI, Euclid, J-PAS, and LSST.

The code allows for tomographic and model-independent analysis.

It demonstrates the comparative performance of different surveys across redshift and scale.

Abstract

The Fisher gAlaxy suRvey cOde ($\texttt{FARO}$) is a new public Python code that computes the Fisher matrix for galaxy surveys observables. The observables considered are the linear multitracer 3D galaxy power spectrum, the linear convergence power spectrum for weak lensing, and the linear multitracer power spectrum for the correlation between galaxy distribution and convergence. The code allows for tomographic and model-independent analysis in which, for scale-independent growth, the following functions of redshift $A_a (z) \equiv \sigma_{8}(z) \, b_a (z)$, $R(z) \equiv \sigma_{8}(z) \, f(z)$, $L(z) \equiv \Omega_{m} \, \sigma_{8}(z) \, \Sigma (z)$ and $E(z) \equiv H(z)/H_0$, together with the function of scale $\hat{P}(k)$, are taken as free parameters in each redshift and scale bins respectively. In addition, a module for change of variables is provided to project the Fisher matrix on any particular set of parameters required. The code is built to be as fast as possible and user-friendly. As an application example, we forecast the sensitivity of future galaxy surveys like DESI, Euclid, J-PAS and LSST and compare their performance on different redshift and scale ranges.