rail_bpz

RAIL interface to BPZ algorithms via the DESC_BPZ package implementation (also available via PyPI with pip install desc-bpz). Anyone using BPZ via either rail_bpz or DESC_BPZ should cite both Benitez (2000) and Coe et al. (2006).

As the "lite" name implies, not all features of BPZ are implemented, the main product is the marginalized redshift PDF, which is output for a sample as a qp ensemble. However, several other quantities are computed and stored as "ancillary" data and stored with the ensemble, these are:

• zmode (float): the mode of the marginalized posterior redshift PDF distribution.
• zmean (float): the mean of the marginalized posterior redshift PDF distribution.
• tb (int): the integer index for the "best-fit" SED template at the redshift mode, zmode. Note that the best-fit template can be different at different redshifts as the SED observed colors change with redshift, so you can not assume this single SED for the full marginalized PDF, it should only be used for the "point estimate" redshift zmode.
• todds (float): relating to the comment above on tb, todds is a new quantity not included with the original BPZ implementation, it is the fraction of marginalized posterior probability assigned to tb. So, high values of todds would mean that no other templates fit well, even at other redshifts, while a lower value of todds means that there are alternative fits, either at the same redshift or other redshifts. If you are wanting to compute physical quantities based on tb, a lower value of todds would mean that such fits would be missing degenerate SED solutions, and should not be trusted.