A New Approach to the Optimal Target Selection Problem

Abstract

Optimally selecting a subset of targets from a larger catalog is a common problem in astronomy and cosmology. A specific example is the selection of targets from an imaging survey for multi-object spectrographic follow-up. We present a new heuristic algorithm, HYBRID, for this purpose and undertake detailed studies of its performance. HYBRID combines elements of the simulated annealing, MCMC and particle-swarm methods and is particularly successful in cases where the survey landscape has multiple curvature or clustering scales. HYBRID consistently outperforms the other methods, especially in high-dimensionality spaces with many extrema. This means many fewer simulations must be run to reach a given performance confidence level and implies very significant advantages in solving complex or computationally expensive optimisation problems.

10 pages, 14 figures, Extended version accepted to Astron. Astrophys

References in corpus (6)

• The 2dF Galaxy Redshift Survey: Spectra and redshifts
• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
• Spectroscopic Target Selection for the Sloan Digital Sky Survey: The Luminous Red Galaxy Sample
• Universal fitting formulae for baryon oscillation surveys
• Eyes Wide Open - Optimising Cosmological Surveys in a Crowded Market
• Particle Swarm Optimization: An efficient method for tracing periodic orbits in 3D galactic potentials

Cited by in corpus (6)

• EMERGE - An empirical model for the formation of galaxies since $z\sim 10$
• Empirical constraints on the formation of early-type galaxies
• Cosmological parameter estimation via iterative emulation of likelihoods
• Density mapping with weak lensing and phase information
• Self-consistent redshift estimation using correlation functions without a spectroscopic reference sample
• Intricate dynamics of a deterministic walk confined in a strip