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Low/High Redshift Classification of Emission Line Galaxies in the HETDEX survey

Published online by Cambridge University Press:  01 July 2015

Viviana Acquaviva ,
Eric Gawiser ,
Andrew S. Leung  and
Mario R. Martin
Viviana Acquaviva
Affiliation:
Physics Department, New York City College of Technology, Brooklyn, NY 11201 email: vacquaviva@citytech.cuny.edu
Eric Gawiser
Affiliation:
Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ 08554
Andrew S. Leung
Affiliation:
Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ 08554
Mario R. Martin
Affiliation:
Physics Department, New York City College of Technology, Brooklyn, NY 11201 email: vacquaviva@citytech.cuny.edu
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Abstract

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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.

Keywords

galaxies: distances and redshiftsmethods: statisticalMachine Learning
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 365 - 368
Copyright
Copyright © International Astronomical Union 2015 

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