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

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 in order to improve the photometric redshift estimate by a factor of two at $z>1$. 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 $A_v$ 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 $1\sigma$ photo-z scatter 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 $\sim$15-20% compared to using the entire catalogue.