fisherA2Z

Introduction

FisherA2Z is a Fisher forecasting code initially developed by Husni Almoubayyed for his PhD thesis Chapter 4. Tianqing Zhang refurbished the code in 2024 and prepared Zhang et al in prep. "A2Z" stands for the initial of Almoubayyed and Zhang.

This repository includes code used for the Fisher Information matrix computation used to assess the impact of photo-z modeling errors on 3x2pt inferences.

Installation

First, initialize a conda environment by

conda create -n 'fisher_env' python=3.8
conda activate fisher_env

If you want to install from Pypi:

pip install fisher-a2z

If you want to install from source, clone the repository, and install by

pip install -e .

Then you can add your conda environment to your jupyterLab by

conda install -c anaconda ipykernel
python -m ipykernel install --user --name=fisher_env

Code

The code described in the paper is in fisher.py, and the Fisher class therein.

We developed a latest and greatest module that can do forecast for any n(z) and its realizations, survey configuration like fsky, number density, and shape noise, defined in fisher_flex.py

The Fisher class takes a CCL cosmo object and 3 iterables of length 5 each to specify the photo-z error model in terms of biases, standard deviations, and outlier fractions.

Examples

Flexible Fisher Forecast

To use the flexible fisher forecast, you need to define a bit more ingredient (but it is way more flexible)

from fisherA2Z.fisher_flex import FisherFlex
flex_y10_cs = FisherFlex(
    # -- the n(z) and its uncertainty ---------------------------------
    nz_source=nz_source,
    nz_realizations=nz_realizations,
    z_grid=z_grid,
    # -- the survey ---------------------------------------------------
    neff_source=[4, 5, 4, 2],   # arcmin^-2, per tomographic bin
    fsky=0.5,                   # ~0.5
    sigma_e=0.26,               # per-component ellipticity dispersion
    # -- the analysis -------------------------------------------------
    mode="cosmic_shear",        # or '2x2pt' / '3x2pt'
    nz_model="shift_stretch",   
)

flex_y10_cs.compute(parallel=True)

res_y10_cs = flex_y10_cs.forecast(ell_max_cs=1800, ell_min_cs=300)  # you can apply scale cuts here

See Tutorial_04 and Tutorial_05 for more examples on FisherFlex.

Original Fisher Forecast

To get the original fisher matrix for a certain case, it is sufficient to run

from fisher import Fisher
f = Fisher(cosmo=ccl_cosmo)
f.process()

then the Fisher information matrix will be stored in f.fisher. Also see Tutorial_01/02/03

Enjoy Fishering!