A sparse-sampling strategy for the estimation of large-scale clustering from redshift surveys

Abstract

It is shown that a fractional faint-magnitude limited redshift survey can significantly reduce the uncertainty in the two-point function for a given telescope time investment, in the estimation of large scale clustering. The signal-to-noise ratio for a 1-in-20 bright galaxy sample is roughly twice that provided by a same-cost complete survey, and this performance is the same as for a larger complete survey of about seven times the cost. A similar performance increase is achieved with a wide-field telescope multiple redshift collection from a close to full sky coverage survey. Little performance improvement is seen for smaller multiply collected surveys ideally sampled at a 1-in-10 bright galaxy rate. The optimum sampling fraction for Abell's rich clusters is found to be close to unity, with little sparse sampling performance improvement.