Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements

TL;DR

This study uses simulations to assess how spectroscopic redshift errors and incompleteness affect cosmological measurements from weak lensing, highlighting the importance of accurate redshifts and effective error estimation methods.

Contribution

It demonstrates that neural network-based modeling can mitigate spectroscopic incompleteness effects, but redshift inaccuracies significantly bias cosmological results, emphasizing the need for precise redshift calibration.

Findings

Spectroscopic incompleteness does not significantly bias cosmology.

Incorrect redshifts cause severe biases exceeding 1%.

Photo-z error estimators can reduce cosmological biases.

Abstract

We use N-body-spectro-photometric simulations to investigate the impact of incompleteness and incorrect redshifts in spectroscopic surveys to photometric redshift training and calibration and the resulting effects on cosmological parameter estimation from weak lensing shear-shear correlations. The photometry of the simulations is modeled after the upcoming Dark Energy Survey and the spectroscopy is based on a low/intermediate resolution spectrograph with wavelength coverage of 5500{\AA} < {\lambda} < 9500{\AA}. The principal systematic errors that such a spectroscopic follow-up encounters are incompleteness (inability to obtain spectroscopic redshifts for certain galaxies) and wrong redshifts. Encouragingly, we find that a neural network-based approach can effectively describe the spectroscopic incompleteness in terms of the galaxies' colors, so that the spectroscopic selection can be applied to the photometric sample. Hence, we find that spectroscopic incompleteness yields no appreciable biases to cosmology, although the statistical constraints degrade somewhat because the photometric survey has to be culled to match the spectroscopic selection. Unfortunately, wrong redshifts have a more severe impact: the cosmological biases are intolerable if more than a percent of the spectroscopic redshifts are incorrect. Moreover, we find that incorrect redshifts can also substantially degrade the accuracy of training set based photo-z estimators. The main problem is the difficulty of obtaining redshifts, either spectroscopically or photometrically, for objects at z > 1.3. We discuss several approaches for reducing the cosmological biases, in particular finding that photo-z error estimators can reduce biases appreciably.