2014/11/10 by Viviana Acquaviva, Eric Gawiser, Acquaviva, Viviana +5
Engineering · Physics and Astronomy · #Astronomy and Astrophysical Research #CCD and CMOS Imaging Sensors #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Instrumentation and Methods for Astrophysics (astro-ph.IM) #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.1411.2651
4 pages, accepted for publication in the Proceedings of the IAU Symposium 306: "Statistical Challenges in 21st Century Cosmology" (Lisbon, Portugal, May 2014)
arxiv created 2014/11/10 · openalex publication_date 2014/11/10 · arxiv updated 2014/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We discuss different methods to separate high- from low-redshift galaxies based on a combination of spectroscopic and photometric observations. Our baseline scenario is the Hobby-Eberly Telescope Dark Energy eXperiment (HETDEX) survey, which will observe several hundred thousand Lyman Alpha Emitting (LAE) galaxies at 1.9 < z < 3.5, and for which the main source of contamination is [OII]-emitting galaxies at z < 0.5. Additional information useful for the separation comes from empirical knowledge of LAE and [OII] luminosity functions and equivalent width distributions as a function of redshift. We consider three separation techniques: a simple cut in equivalent width, a Bayesian separation method, and machine learning algorithms, including support vector machines. These methods can be easily applied to other surveys and used on simulated data in the framework of survey planning.