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

TL;DR

This paper demonstrates how to optimize target selection and survey strategies for future spectroscopic galaxy surveys using photometric data, neural networks, and Fisher matrix forecasts to enhance dark energy measurements.

Contribution

It introduces methods for selecting optimal galaxy targets using color-color cuts and neural networks, and forecasts their impact on dark energy constraints in spectroscopic surveys.

Findings

Neural networks improve target selection efficiency.

Optimized survey strategies enhance dark energy figure of merit.

Forecasts show significant gains with tailored target selection.

Abstract

The next generation of spectroscopic surveys will have a wealth of photometric data available for use in target selection. Selecting the best targets is likely to be one of the most important hurdles in making these spectroscopic campaigns as successful as possible. Our ability to measure dark energy depends strongly on the types of targets that we are able to select with a given photometric data set. We show in this paper that we will be able to successfully select the targets needed for the next generation of spectroscopic surveys. We also investigate the details of this selection, including optimisation of instrument design and survey strategy in order to measure dark energy. We use color-color selection as well as neural networks to select the best possible emission line galaxies and luminous red galaxies for a cosmological survey. Using the Fisher matrix formalism we forecast the efficiency of each target selection scenario. We show how the dark energy figures of merit change in each target selection regime as a function of target type, survey time, survey density and other survey parameters. We outline the optimal target selection scenarios and survey strategy choices which will be available to the next generation of spectroscopic surveys.