  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.
