Although photometric redshifts (photo-z's) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and color. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo-z predictions as a function of magnitude and color, and illustrate that the underlying redshift distribution at fixed color can evolve strongly as a function of magnitude. We then test the robustness of the galaxy-galaxy lensing signal in 120 deg2 of HSC-SSP DR1 data to spectroscopic completeness and photo-z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo-z-based weak lensing predictions in future surveys such as LSST and WFIRST.
@article{arxiv.1906.05876,
title = {Galaxy-Galaxy Lensing in HSC: Validation Tests and the Impact of Heterogeneous Spectroscopic Training Sets},
author = {Joshua S. Speagle and Alexie Leauthaud and Song Huang and Christopher P. Bradshaw and Felipe Ardila and Peter L. Capak and Daniel J. Eisenstein and Daniel C. Masters and Rachel Mandelbaum and Surhud More and Melanie Simet and Cristóbal Sifón},
journal= {arXiv preprint arXiv:1906.05876},
year = {2020}
}