Arthur F. Thurnau Professor of Physics
About
My research addresses basic questions about the large-scale structure and evolution of the universe. How did the universe evolve from its very homogeneous state shortly after the Big Bang to the rich and varied structures we see today? How is this evolution affected by the properties and abundance of the universe's main ingredients: dark energy, dark matter, and baryons? My students and I are part of several large astronomical surveys that are attempting to answer these questions. The Dark Energy Survey is an ongoing optical imaging survey of 5000 square degrees of the sky using the Blanco 4-meter telescope at CTIO in Chile. Over the course of 525 nights of observing between 2013 and 2018, we'll survey 1/8 of the sky and observe about 300 million galaxies in five different regions of the visible and near-infrared spectrum (the grizY bands), and discover approximately 4000 Type Ia supernovae. With this data set, we'll be able to measure the expansion history of the universe using the four complementary techniques of weak gravitational lensing, baryon acoustic oscillations, Type Ia supernovae, and--my particular interest--the size and abundance of galaxy clusters. Most of these measurements require knowing the redshift of the object being measured. Ideally, this would be measured via spectroscopy, but this is impractical for a sample of 300 million objects. I am applying machine-learning techniques to estimate galaxy redshifts empirically using only the five observed DES magnitudes, and with my students have developed the publicly-available ArborZ photometric redshift estimation code.Looking to the longer term, I'm part of the BigBOSS project, a proposed 5000-fiber R=5000 spectrograph intended for installation on the 4-meter Mayall telescope at Kitt Peak National Observatory. BigBOSS will measure baryon acoustic oscillation features in galaxies and in hydrogen gas over a wide survey area out to redshifts of 3.5. A large spectroscopic survey like this is the logical next step following a wide-field imaging survey like DES.Professor Gerdes is the recipient of the University of Michigan Provost's Teaching Innovation Prize, a CAREER award from the National Science Foundation, and an Excellence in Education Award from the College of Literature, Science, and the Arts.
