The functions in redshifts provide useful routines for making predictions for LSST-specific observables. These include routines for predicting the linear bias of the clustering sample, and for predicting the redshift distribution of a given tomographic photometric redshift bin. We also provide functionality for the user to incorporate their own photo-z and true dNdz model and to split the redshift distributions in tomographic bins based on photo-z cuts.
These routines are based on the LSST Science book and the Chang et al. (2013) paper. These provide several options to model the expected redshift distributions of LSST galaxies that we use for the tomographic photo-z binning. The options are as follows.
- dNdz options
- ‘nc’: redshift distribution for number counts, i.e., the clustering sample.
- ‘wl_cons’: redshift distribution for galaxies with shapes for lensing. This
- option adopts a conservative cut on shape quality criteria.
- ‘wl_fid’: redshift distribution for galaxies with shapes for lensing. This
- option adopts a fiducial cut on shape quality criteria.
- ‘wl_opt’: redshift distribution for galaxies with shapes for lensing. This
- option adopts an optimistic cut on shape quality criteria.
Gaussian photo-z function with sigma(z) = sigma_z0 (1 + z).
dNdz_tomog(z, zmin, zmax, pz_func, dNdz_func)¶
Calculates dNdz in a particular tomographic bin, convolved with a photo-z model (defined by the user), and normalized.
tomographic dNdz values evalued at each z.
dNdz (float or array_like)