Sample variance in photometric redshift calibration: cosmological biases and survey requirements
Carlos E. Cunha (1,2), Dragan Huterer (2), Michael T. Busha (2,3),, Risa H. Wechsler (2,4) ((1) University of Michigan, (2) KIPAC, Stanford, (3), ITP, University of Zurich, (4) SLAC)

TL;DR
This study investigates how sample variance in spectroscopic redshift samples affects photometric redshift calibration and cosmological parameter estimation, providing guidelines for spectroscopic follow-up to minimize biases.
Contribution
It demonstrates that sample variance impacts photo-z error calibration more than training, and offers a practical observing strategy to control cosmological biases.
Findings
Sample variance significantly affects photo-z error calibration.
Spectroscopic follow-up can be optimized to reduce biases.
Guidelines for observing proposals to ensure minimal cosmological impact.
Abstract
We use N-body/photometric galaxy simulations to examine the impact of sample variance of spectroscopic redshift samples on the accuracy of photometric redshift (photo-z) determination and calibration of photo-z errors. We estimate the biases in the cosmological parameter constraints from weak lensing and derive requirements on the spectroscopic follow-up for three different photo-z algorithms chosen to broadly span the range of algorithms available. We find that sample variance is much more relevant for the photo-z error calibration than for photo-z training, implying that follow-up requirements are similar for different algorithms. We demonstrate that the spectroscopic sample can be used for training of photo-zs and error calibration without incurring additional bias in the cosmological parameters. We provide a guide for observing proposals for the spectroscopic follow-up to ensure…
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