Measuring the redshift evolution of clustering: the Hubble Deep Field South

We present an analysis of the evolution of galaxy clustering in the redshift interval 0 less than or equal to z less than or equal to 4.5 in the Hubble Deep Field South (HDF-South). The HST optical data are combined with infrared ISAAC/VLT observations, and photometric redshifts are used for all the galaxies brighter than I-AB less than or equal to 27.5. The clustering signal is obtained in different redshift bins using two different approaches: a standard one, which uses the best redshift estimate of each object, and a second one, which takes into account the redshift probability function of each object. This second method makes it possible to improve the information in the redshift intervals where contamination from objects with insecure redshifts is important. With both methods, we find that the clustering strength up to z similar or equal to 3.5 in the HDF-South is consistent with the previous results in the HDF-North. Whereas at redshift lower than z similar to 1 the HDF galaxy population is un/anti-biased (b less than or equal to 1) with respect to the underlying dark matter, at high redshift the bias increases up to b(z similar to 3) similar or equal to 2-3, depending on the cosmological model. These results support previous claims that, at high redshift, galaxies are preferentially located in massive haloes, as predicted by the biased galaxy formation scenario. In order to quantify the impact of cosmic errors on our analyses, we have used analytical expressions from Bernstein. Once the behaviour of higher-order moments is assumed, our results show that errors in the clustering measurements in the HDF surveys are indeed dominated by pure shot-noise in most regimes, as assumed in our analysis. We also show that future observations with instruments like the Advanced Camera on HST will improve the signal-to-noise ratio by at least a factor of 2, as a consequence, more detailed analyses of the errors will be required. In fact, pure shot-noise will give a smaller contribution with respect to other sources of errors, such as finite volume effects or non-Poissonian discreteness effects.