Galaxy and Mass Assembly (GAMA): Optimal Tiling of Dense Surveys with a Multi-Object Spectrograph

TL;DR

This paper introduces a greedy algorithm for optimal tiling in dense redshift surveys, improving spatial uniformity and correcting biases, ensuring survey requirements are met despite weather variability.

Contribution

A novel heuristic greedy algorithm for efficient tiling in dense redshift surveys, enhancing uniformity and bias correction in spectroscopic data collection.

Findings

Rapid improvement in spatial uniformity of GAMA survey data

Effective correction of spatial bias introduced by the 2dF spectrograph

Confident predictions that survey requirements will be met despite weather conditions

Abstract

A heuristic greedy algorithm is developed for efficiently tiling spatially dense redshift surveys. In its first application to the Galaxy and Mass Assembly (GAMA) redshift survey we find it rapidly improves the spatial uniformity of our data, and naturally corrects for any spatial bias introduced by the 2dF multi object spectrograph. We make conservative predictions for the final state of the GAMA redshift survey after our final allocation of time, and can be confident that even if worse than typical weather affects our observations, all of our main survey requirements will be met.