Photometric Redshifts for the Dark Energy Survey and VISTA and Implications for Large Scale Structure

TL;DR

This paper evaluates the accuracy of photometric redshifts for the Dark Energy Survey using neural networks and simulations, demonstrating improvements with near-infrared data and methods to reduce outliers for large-scale structure analysis.

Contribution

It introduces a neural network approach to estimate photometric redshifts, assesses the impact of additional near-infrared data, and explores training set effects for dark energy research.

Findings

Adding VISTA near-infrared data improves photo-z accuracy by over 30%.

Neural network error estimates help identify and remove outliers.

Refined photo-z estimates enable better constraints on large-scale structure.

Abstract

We conduct a detailed analysis of the photometric redshift requirements for the proposed Dark Energy Survey (DES) using two sets of mock galaxy simulations and an artificial neural network code - ANNz. In particular, we examine how optical photometry in the DES grizY bands can be complemented with near infra-red photometry from the planned VISTA Hemisphere Survey (VHS) in the JHK_s bands. We find that the rms scatter on the photometric redshift estimate over 1<z<2 is sigma_z=0.2 from DES alone and sigma_z=0.15 from DES+VISTA, i.e. an improvement of more than 30%. We draw attention to the effects of galaxy formation scenarios such as reddening on the photo-z estimate and using our neural network code, calculate the extinction, A_v for these reddened galaxies. We also look at the impact of using different training sets when calculating photometric redshifts. In particular, we find that using the ongoing DEEP2 and VVDS-Deep spectroscopic surveys to calibrate photometric redshifts for DES, will prove effective. However we need to be aware of uncertainties in the photometric redshift bias that arise when using different training sets as these will translate into errors in the dark energy equation of state parameter, w. Furthermore, we show that the neural network error estimate on the photometric redshift may be used to remove outliers from our samples before any kind of cosmological analysis, in particular for large-scale structure experiments. By removing all galaxies with a neural network photo-z error estimate of greater than 0.1 from our DES+VHS sample, we can constrain the galaxy power spectrum out to a redshift of 2 and reduce the fractional error on this power spectrum by ~15-20% compared to using the entire catalogue.