Non-negative matrix factorization for self-calibration of photometric redshift scatter in weak lensing surveys

TL;DR

This paper introduces a new algorithm based on non-negative matrix factorization to accurately self-calibrate photometric redshift errors in weak lensing surveys, improving the precision of cosmological inferences.

Contribution

It develops a robust, efficient algorithm for self-calibrating photo-z scatter rates using a constrained nonlinear optimization approach with fixed-point iteration.

Findings

Successfully recovers scatter rates at 0.01-1% level

Accurately estimates mean redshifts within 0.001

Performs well on simulated stage IV survey data

Abstract

Photo-z error is one of the major sources of systematics degrading the accuracy of weak lensing cosmological inferences. Zhang et al. (2010) proposed a self-calibration method combining galaxy-galaxy correlations and galaxy-shear correlations between different photo-z bins. Fisher matrix analysis shows that it can determine the rate of photo-z outliers at a level of 0.01-1% merely using photometric data and do not rely on any prior knowledge. In this paper, we develop a new algorithm to implement this method by solving a constrained nonlinear optimization problem arising in the self-calibration process. Based on the techniques of fixed-point iteration and non-negative matrix factorization, the proposed algorithm can efficiently and robustly reconstruct the scattering probabilities between the true-z and photo-z bins. The algorithm has been tested extensively by applying it to mock data from simulated stage IV weak lensing projects. We find that the algorithm provides a successful recovery of the scatter rates at the level of 0.01-1%, and the true mean redshifts of photo-z bins at the level of 0.001, which may satisfy the requirements in future lensing surveys.