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

Authors


E-mail: mbanerji@star.ucl.ac.uk

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-infrared photometry from the planned VISTA Hemisphere Survey (VHS) in the JHKs bands. We find that the rms scatter on the photometric redshift estimate over 1 < z < 2 is σz= 0.2 from DES alone and σz= 0.15 from DES + VISTA, i.e. an improvement of more than 30 per cent. 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, Av 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 per cent compared to using the entire catalogue.

Output tables of spectroscopic redshift versus photometric redshift used to produce the results in this paper can be found at http://www.star.ucl.ac.uk/~mbanerji/DESdata.

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