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

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.

12 pages, 6 figures. Accepted for publication in ApJ. Updated to match the published version

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• Effect of redshift bin mismatch on cross correlation between DESI Legacy Imaging Survey and Planck CMB lensing potential
• Redshift Distributions of Fast Radio Bursts Inferred Using Clustering in Dispersion Measure Space
• Optimizing Redshift Distribution Inference through Joint Self-Calibration and Clustering-Redshift Synergy
• Reconstructing redshift distributions with photometric galaxy clustering
• Calibrating systematic errors in the distance determination with the luminosity-distance space large scale structure of dark sirens and its potential applications