PI: James Rhoads, STScI, email@example.com
Address: 3700 San Martin Drive, Baltimore, MD 21218, USA
CoI: Sangeeta Malhotra, JHU
CoI: Arjun Dey, NOAO
CoI: Buell Jannuzi, NOAO
Title: A Census of z > 4 Galaxies
Abstract: Although recent discoveries have yielded galaxies with redshifts up to z\appx 5.7, statistical samples of galaxies at these redshifts are still extremely limited. Recent evidence for reionization near z \appx 6 makes this redshift range the most interesting for studying galaxy formation and evolution. We propose to triple the number of spectroscopically confirmed z \ga 4.3 galaxies with Gemini. The candidates for spectroscopic followup are selected using two criteria: (1) Strong \lya line emission from the Large Area Lyman Alpha (LALA) survey at z=4.5 & 5.7 and (2) broadband colors (Lyman-break galaxies) from the NOAO Deep Wide-Field Survey (NDWFS). These two selection methods are complementary and using both samples will yield the most complete optically selected sample of 4 \la z \la 7 galaxies. Ly- (alpha) selected galaxies typically have low luminosities and high \lya equivalent widths, indicating young stellar populations (\le 10^7 years old) with little or no dust. Lyman-break galaxies are typically more luminous, with weak \lya emission or absorption, and may be more evolved. We will use the spectroscopic sample to refine photometric classification methods, in order to leverage much larger samples from LALA and NDWF surveys and extend redshift determinations to lower luminosities. The sample will be used to study galaxy evolution between z=4.5-6.8. We will have large enough samples to robustly determine luminosity functions, star formation rates, and clustering properties of the sample as a whole and of the Lyman-break and \lya\ populations seperately. We will also study the relationship between the continuum- and line-selected galaxies. In particular, we will determine whether the the lower bolometric luminosities of the line-selected samples imply correspondingly lower halo masses by comparing clustering of the two samples.
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