The Fisher gAlaxy suRvey cOde (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, errors for each parameter in each redshift bin and for the matter power spectrum today in scale bins are obtained. 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 build to be as fast as possible and user-friendly.
The new era of precision cosmology will unfold thanks primarily to galaxy surveys. Galaxy maps can be classified into three types: spectroscopic, photometric and spectro-photometric surveys. Spectroscopic surveys are able to obtain very precise redshifts for pre-selected samples of galaxies, whereas photometric surveys can create much larger samples but with poorer redshift accuracies. Spectro-photometric surveys have somehow the best of both worlds, i.e. the ability to obtain large catalogues with precise redshifts. In addition, photometric and spectro-photometric surveys can determine galaxy shapes and then are suited to perform weak lensing studies. The combined measurement of 3D galaxy clustering and weak lensing will help break degeneracies and will allow to measure cosmological parameters with unprecedented precision. In this framework, forecast analysis are a very useful tool to analyze the impact of future galaxy surveys like DESI, JPAS, Euclid or LSST. The Fisher gAlaxy suRvey cOde (FARO) is built to make Fisher forecast analysis as fast as possible in a user-friendly way.
All the detailed information about the math and physics of the code is explained in: Arxiv
FARO is a Fisher matrix code for galaxy surveys that is totally public and made to be easy to use and modify. The code is thought to analyze spectroscopic, photometric and spectro-photometric surveys; and also to combine different surveys. The main characteristics of FARO are the following:
- Python code: easy to use and manipulate. FARO makes extensive use of the Python function np.einsum which is basic in the code design. The code employs the python CLASS functions through the classy wrapper.
- Multitracer observables: multitracer 3D galaxy power spectrum, lensing convergence power spectrum and mulitracer cross-correlation power spectrum in the linear regime.
- Model independent: derivatives are calculated analytically for the set of model-independent parameters making the code faster.
- Tomographic errors: error information is provided for each redhsift and k bin.
- Assumptions: flat FRW background and scale-independent growth function.
To use FARO you basically need Python and some modules of it:
- Python 2.7: to download Python 2.7
- Modules: The code uses the following python modules: numpy, scipy, math, os, shutil and matplotlib. The straightforward way to install them is downloading Anaconda
- CLASS code: to download CLASS code. If not previously compiled, in order to ensure classy is properly installed, either use "make" instead "make class" or directly "make classy".
Once you have the requisites, you can download FARO code here as a compressed file or it can be cloned from the GitHub repository. If you use it in a publication/preprint please cite at least the original FARO paper.