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Monica Valluri

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Monica Valluri is an Associate Research Professor at the U-M Department of Astronomy. She studies galactic dynamics, including accurately measuring the masses of supermassive black holes in galaxies; non-linear dynamical processes involved in sculpting galaxies; the properties of “dark matter”; understanding the dynamical structure of the Milky Way Galaxy; predicting the observable properties of dark matter and the detectability of “dark stars”; and inferring the dynamical effects of cluster environments on spiral galaxies from the distributions of their atomic hydrogen gas and stars.

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Lee Hartmann

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Professor Hartmann studies the processes by which cold clouds of gas and dust fragment and then collapse, forming stars and a surrounding rotating disk; and the evolution of disks which ultimately can form planets. Because protostellar clouds form in complex, highly structured and turbulent gas, time-dependent (magneto)hydrodynamics is required to follow fragmentation and collapse to stars and disks.  Similarly, the evolution of planet-forming disks involves both gravitational and magnetic turbulence. Simulations of cloud and disk evolution over the necessary timescales (105 to 106 years) with reasonable resolution demands extensive parallel processing.
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Chris Miller

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Christopher J. Miller is an Assistant Professor of Astronomy and Physics. Professor Miller is a leader in the field of astronomical data mining and computational astrostatistics. He co-founded the INternational Computational Astrostatistics (INCA) group, a collaboration of researchers from the University of Michigan, Carnegie Mellon University, University of Washington, Georgia Tech, the NOAO, and others. From 2008-2010, he led the NOAO Science Data Management group, where he was responsible for using and delivering science quality astronomical data from the US ground-based observatories. He was hired at the University of Michigan under a U-M Presidential initiative for advancing data mining research. His research interests include studies of large-scale structure and cosmology, galaxy clustering, galaxy formation and galaxy evolution.

Astro-informatics is an emerging discipline which matches the large, complex, and time-varying datasets generated by earth and space-based astronomical observatories, to modern unsolved challenges in computer science and statistics. In this example, we compare a semi-blind Fourier deconvolution of an astronomical image (left) to the forward modeling of a physically motivated but smooth galaxy light profile (right). Note that the data are sparse and that the underlying point-spread functions (PSF) are not well known. The technique on the left was developed by Se Un Park (a PhD graduate from EECS) and produces an estimate of the PSF from the data. The method on the right is a traditional astronomical technique. The goal is to obtain the best shape classification of the galaxies in the Universe. With our research, we hope to uncover some of Nature’s astrophysical secrets through the interdisciplinary development and application of computer science algorithms and statistical methods on astronomical datasets.

Astro-informatics is an emerging discipline which matches the large, complex, and time-varying datasets generated by earth and space-based astronomical observatories, to modern unsolved challenges in computer science and statistics. In this example, we compare a semi-blind Fourier deconvolution of an astronomical image (left) to the forward modeling of a physically motivated but smooth galaxy light profile (right). Note that the data are sparse and that the underlying point-spread functions (PSF) are not well known. The technique on the left was developed by Se Un Park (a PhD graduate from EECS) and produces an estimate of the PSF from the data. The method on the right is a traditional astronomical technique. The goal is to obtain the best shape classification of the galaxies in the Universe. With our research, we hope to uncover some of Nature’s astrophysical secrets through the interdisciplinary development and application of computer science algorithms and statistical methods on astronomical datasets.

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Oleg Gnedin

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I am a theoretical astrophysicist studying the origins and structure of galaxies in the universe.  My research focuses on developing more realistic gasdynamics simulations, starting with the initial conditions that are well constrained by observations, and advancing them in time with high spatial resolution using adaptive mesh refinement.  I use machine-learning techniques to compare simulation predictions with observational data.  Such comparison leads to insights about the underlying physics that governs the formation of stars and galaxies.  I have developed a Computational Astrophysics course that teaches practical application of modern  techniques for big-data analysis and model fitting.

Emergence of galaxies and star clusters in cosmological gasdynamics simulations. Left panel shows large-scale cosmic structure (density of dark matter particles), which formed by gravitational instability. In the middle panel we can resolve this structure into disk galaxies with complex morphology (density of molecular/red and atomic/blue gas). These galaxies should create massive star clusters, such as shown in the right panel (real image -- to be reproduced by our future simulations!).

Emergence of galaxies and star clusters in cosmological gasdynamics simulations. Left panel shows large-scale cosmic structure (density of dark matter particles), which formed by gravitational instability. In the middle panel we can resolve this structure into disk galaxies with complex morphology (density of molecular/red and atomic/blue gas). These galaxies should create massive star clusters, such as shown in the right panel (real image — to be reproduced by our future simulations!).

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August (Gus) Evrard

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August (Gus) Evrard is Arthur F. Thurnau Professor in the departments of Physics and Astronomy, and the Michigan Center for Theoretical Physics. He serves as Associate Director for Community Engagement with ARC. Professor Evrard is a computational cosmologist who models the formation and evolution of large-scale cosmic structure. He currently co-leads the Simulation Working Group for the US-led Dark Energy Survey and is a member of the XMM-XXL project and Virgo Consortium based in Europe. His research uses N-body and hydrodynamic methods to study the formation of galaxies and clusters of galaxies. The simulations also produce synthetic expectations for astronomical sky surveys, providing truth tables that are essential for verifying data handling and statistical processing methods applied to survey data to study the nature of dark matter and dark energy. Professor Evrard was named a Fellow of the American Physical Society in 2011 and an ORCID Ambassador in 2013. He is active in instructional technology at Michigan, founding the Academic Reporting Tool service in use since 2006 and Problem Roulette, a cloud-based study service that offers random, topical access to old exam questions for students in introductory physics classes.

Synthetic sky image derived from N-body simulations of a universe dominated by vacuum energy.

Synthetic sky image derived from N-body simulations of a universe dominated by vacuum energy.