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We conduct a detailed analysis of the photometric redshift requirements forthe proposed Dark Energy Survey (DES) using two sets of mock galaxy simulationsand an artificial neural network code - ANNz. In particular, we examine howoptical photometry in the DES grizY bands can be complemented with nearinfra-red photometry from the planned VISTA Hemisphere Survey (VHS) in theJHK_s bands. We find that the rms scatter on the photometric redshift estimateover 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 galaxyformation scenarios such as reddening on the photo-z estimate and using ourneural network code, calculate the extinction, A_v for these reddened galaxies.We also look at the impact of using different training sets when calculatingphotometric redshifts. In particular, we find that using the ongoing DEEP2 andVVDS-Deep spectroscopic surveys to calibrate photometric redshifts for DES,will prove effective. However we need to be aware of uncertainties in thephotometric redshift bias that arise when using different training sets asthese will translate into errors in the dark energy equation of stateparameter, w. Furthermore, we show that the neural network error estimate onthe photometric redshift may be used to remove outliers from our samples beforeany kind of cosmological analysis, in particular for large-scale structureexperiments. By removing all galaxies with a neural network photo-z errorestimate of greater than 0.1 from our DES+VHS sample, we can constrain thegalaxy power spectrum out to a redshift of 2 and reduce the fractional error onthis power spectrum by ~15-20% compared to using the entire catalogue.

Photometric redshifts for the Dark Energy Survey and VISTA and implications for large‐scale structure