# Galaxy-Galaxy Lensing in HSC: Validation Tests and the Impact of Heterogeneous Spectroscopic Training Sets

## TL;DR

This paper examines how biases in spectroscopic training sets affect photometric redshift estimates and galaxy-galaxy lensing measurements, finding that current biases are sub-dominant but highlighting the importance of understanding these effects for future surveys.

## Contribution

It introduces a framework to analyze the impact of spectroscopic incompleteness on photo-z and weak lensing predictions, applicable to upcoming large surveys.

## Key findings

- Spectroscopic biases influence photo-z predictions as a function of magnitude and color.
- Galaxy-galaxy lensing signals are robust against current spectroscopic biases.
- The methodology aids future survey planning for bias mitigation.

## Abstract

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 deg$^2$ 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.