NOAO >   Observing Info >   Approved Programs >   2010B-0554

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Proposal Information for 2010B-0554


PI: Casey Papovich, Texas A&M University, papovich@physics.tamu.edu
Address: Department of Physics and Astronomy, 4242 TAMU, College Station, TX 77843-4242, USA

CoI: Darren DePoy, Texas A&M University
CoI: Mark Dickinson, NOAO
CoI: Keely Finkelstein, Texas A&M University
CoI: Steven Finkelstein, Texas A&M University
CoI: Jennifer Lotz, NOAO
CoI: Ivelina Momcheva, Carnegie Observatories
CoI: A. Muzzin, Yale
CoI: G. Rudnick, Kansas
CoI: K.-V. Tran, Texas A&M University
CoI: P. van Dokkum, Yale
CoI: C. Willmer, Steward Observatory

Title: NEWFIRM Observations of a Massive Conglomeration of Galaxy Cluster Candidates at 1.3 < z < 2.0

Abstract: Cluster galaxies have a remarkably homogeneous color distribution. Galaxy formation models interpret this as evidence that galaxies in higher density environments experience accelerated evolution, but this has yet to be tested at high redshifts when these structures assemble. We propose NEWFIRM observations of a massive conglomeration of galaxy cluster candidates at 1.3<$z$<2.0, selected as the highest concentration of overdensities of galaxies with red IRAC [3.6\micron]- [4.5\micron] colors within the 10 deg^2 SWIRE XMM-LSS field. We will image this conglomeration with three medium-bands, \ja\jb\jc, and broad- band H and K_s, to address the following goals. (1) We will use these data to derive accurate photometric redshifts, (Delta) v \lsim 6000 km s^-1, for galaxies at 1.3<$z$<2.0. We will measure the local galaxy density, and trace this structure in redshift space, determining if it is a candidate supercluster or a projection of structures along the line-of-sight. (2) We will measure the galaxies' stellar population ages by constraining the shape of their 4000 \AA/Balmer breaks. We will establish how these ages correlate with stellar mass and local galaxy density during the redshift period when these galaxies assemble. Comparing these empirically derived age-mass- environment distribution functions to predictions from models will provide insight into galaxy formation.


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