Optimising Spectroscopic and Photometric Galaxy Surveys: Efficient Target Selection and Survey Strategy

摘要

The next generation of spectroscopic surveys will have a wealth ofphotometric data available for use in target selection. Selecting the besttargets is likely to be one of the most important hurdles in making thesespectroscopic campaigns as successful as possible. Our ability to measure darkenergy depends strongly on the types of targets that we are able to select witha given photometric data set. We show in this paper that we will be able tosuccessfully select the targets needed for the next generation of spectroscopicsurveys. We also investigate the details of this selection, includingoptimisation of instrument design and survey strategy in order to measure darkenergy. We use color-color selection as well as neural networks to select thebest possible emission line galaxies and luminous red galaxies for acosmological survey. Using the Fisher matrix formalism we forecast theefficiency of each target selection scenario. We show how the dark energyfigures of merit change in each target selection regime as a function of targettype, survey time, survey density and other survey parameters. We outline theoptimal target selection scenarios and survey strategy choices which will beavailable to the next generation of spectroscopic surveys.