EUCLID photometric window functions

This repository builts EUCLID photometric window functions

Installation

git clone https://github.com/paganol/euclid_windows.git 
cd euclid_windows
pip install -e .

Usage

import euclid_windows as EW

#This initializes the container 
Win = EW.Windows()

#This computes the windows and other useful variables
Win.get_distributions()

#This returns the list of window functions ready for camb
sources = Win.get_camb_distributions()

Parameters

• zmin: minimum redshift, replaced if you provide bin ranges in the variable bintype.

• zmax: maximum redshift, replaced if you provide bin ranges in the variable bintype.

• zmaxsampled: maximum redshift sampled, if not provied zmax is used.

• nbin: number of bins, replaced if you provide bin ranges in the variable bintype.

• use_true_galactic_dist: use the true galactic distribution for the windows, no convolution with photo z distribution.

• dz: integration step in redshift.

• cb, zb, sigmab, c0, z0, sigma0, fout: parameters of the photo z distribution, see equation 115 and table 5 of 1910.09273.

• bintype: three options here, "equipopulated", "equispaced", numpy array or list with bin edges.

• normalize: normalization of the windows.

• biastype: several options here:

  • "stepwise" with a different constant value for each bin, using $f(z)=\sqrt{1+z}$;
  • "continuous" which implements a continuous function $f(z)=\sqrt{1+z}$;
  • "tutusaus_Flag1" which implements Tutusaus bias (Flagship1);
  • "tutusaus_Flag2" which implements Tutusaus bias (Flagship2);
  • numpy array (or list) with bias provided by the user for each bin.

• errortype: the default option is "gauss_err", because we expect a gaussian error and then we can compute the galaxy selection functions via an erf function; if the error is not gaussian, we need to compute the integral of the probability distribution function to determine the galaxy selection functions.