Spitzer Space Telescope - General Observer Proposal #80025 Stellar Distributions in Dark Matter Halos: Looking Over the Edge Principal Investigator: Liese van Zee Institution: Indiana University Technical Contact: Daniel Dale, University of Wyoming Co-Investigators: Daniel A. Dale, University of Wyoming Kate L. Barnes, Indiana University Shawn Staudaher, University of Wyoming Daniela Calzetti, University of Massachusetts Julianne J. Dalcanton, University of Washington James S. Bullock, University of California, Irvine Rupali Chandar, University of Toledo Science Category: nearby galaxies (z<0.05, v_sys<15,000 km/s) Observing Modes: IRAC Post-Cryo Mapping Hours Approved: 1005.3 Abstract: We propose to obtain deep observations with IRAC bands 1 and 2 to trace the faint extended stellar component of nearby galaxies. Little is known about the full extent of the stellar distribution in normal galaxies; deep IR observations of the area around galaxies will allow us to trace the stellar distribution to unprecedented levels. Our sample will include galaxies with a range of morphology, inclination angle, luminosity, and environment in order to explore fully the diverse range of galaxy properties and to enhance the legacy value of this data set. These observations will enable a wide variety of projects, including investigation of thick disks and halo formation, identification of old and young star clusters, and identification of stars well beyond the bright stellar disk. The proposed observations will provide not only a census, but also the first quantitative measurements of the physical properties of low surface brightness features identified around nearby galaxies (e.g., stellar mass surface density, distribution, and fraction of total stellar mass). With sensitivity to substructures featuring stellar mass surface densities of only a few x 0.01 M_sun/pc^2, this project will provide the first look at the stellar edge for a large sample of galaxies and will be instrumental in providing observational constraints for galaxy formation models.