Brian W. O'Shea - Research

cv / bio

My research focuses on theoretical and numerical studies of galaxy formation and evolution, primarily using the Enzo adaptive mesh refinement code and the yt data analysis and visualization package. My research interests are detailed below. Some images from my simulations can be found in the image gallery, and information on recent publications can be found on the news page and in my CV. None of this reserach would be possible without my research group, of course!

If you are a prospective or current undergraduate or graduate student that is interested in working on the subjects discussed below (or a related topic), please contact me! I am interested in working with (and have research projects suitable for) astrophysics, physics, CMSE, computer science, and applied mathematics undergraduate and graduate students.

Structure formation in the high-redshift universe

Overview: The first generations of stars and galaxies are important because they set the stage for all later galaxy formation: these were the first places where metals were created and distributed throughout the Universe, are potential seed sites for the supermassive black holes that are observed at the center of every modern-day massive galaxy, and are the objects that started the process of "reionization," which converted the vast majority of gas in the Universe from a cold, neutral state to a hot, ionized state. This last process started when the first star in the Universe was formed, and ended at approximately z=6, or one billion years after the Big Bang. Observing galaxies in the pre-reionization epoch is a major observational challenge, and the next generation of ground- and space-based telescopes are being constructed with this goal in mind. Making theoretical models of the earliest galaxies is crucial to interpreting these observations, and will yield important insights into galaxy formation and evolution that will be useful for understanding later, larger galaxies such as our own Milky Way.

My group's research focus: Transition from metal-free to metal-enriched star formation; Formation of high-redshift galaxies, including observational predictions for ALMA, JWST and TMT.

Low-redshift galaxies and the intergalactic medium

Overview: Galaxies are composed of vastly more than the stars that are easily seen by optical telescopes. These galaxies are surrounded by halos of gas, which serve as a reservoir for both gas that is flowing into the star-forming regions of the galaxy and gas that is being ejected from the galaxy by feedback from supernovae and the central black hole. In addition, this gas mediates interactions between the central part of the galaxy and its environment. It is crucial to consider the entire galaxy and environs - stars, gaseous halo, and intergalactic medium, and the interplay between them - when attempting to understand the formation of galaxies like our own Milky Way. We have a tremendous amount of observational information about our own galaxy and about neighboring galaxies, which probe the chemical and dynamical behavior of these galaxies. This work is an essential complement to studies of the most distant galaxies in the universe, since it allows us to probe very different aspects of the structure formation process.

My group's research focus: High-fidelity simulations of galaxy formation, including the circumgalactic medium and the intergalactic medium. Chemical evolution, including evolving stellar populations; Improved models for star formation and feedback in cosmological simulations; Using metal-poor galactic halo stars as probes of Milky Way chemical and dynamical evolution; development of statistical tools to compare models with large-scale observational datasets; quantifying the uncertainties associated with modern techniques for studying chemical evolution.

Galaxy clusters

Overview: Galaxy clusters, composed of tens or hundreds of galaxies orbiting within a single common dark matter halo, are the largest gravitationally-bound objects in the Universe, often weighing more than 1014 times the mass of our Sun (or 100 times the mass of the Milky Way). As the largest bound objects, galaxy clusters are useful probes of cosmology and are very interesting astrophysical laboratories in that they are essentially "closed box" systems. 90% of the baryonic matter in galaxy clusters is outside of the galaxies themselves, residing in a hot, diffuse plasma called the "intracluster medium," or ICM, which is extremely bright at X-ray wavelengths but invisible to the naked eye. This plasma is threaded with magnetic fields and relativistic protons and electrons, which are crucial to controlling its behavior. Understanding the ICM and its interactions with the galaxies contained within it is crucial to gaining a complete understanding of galaxy clusters as a whole, the life cycles of these objects, and to assessing their utility as cosmological probes.

My group's research focus: Non-thermal evolution of the intracluster medium, including the effects of magnetic fields and cosmic rays; The effect of feedback from stellar populations and active galactic nuclei, particularly on radio, gamma-ray and x-ray observables; The effects of non-thermal plasma processes in the ICM on galaxy clusters as cosmological probes; Alternative methods of simulating galaxies within clusters.

Extremely large-scale supercomputing

Overview: Many problems in modern astrophysics rely on numerical simulations to make significant theoretical progress. As the questions we ask become more detailed and difficult, so too must the calculations that we undertake in our attempts to answer them. Cosmological structure formation is particularly challenging - in order to be useful for probing the evolution of populations of galaxies, we need to simulate large volumes of the Universe at high spatial and mass resolution and with many different kinds of physics (dark matter dynamics, hydrodynamics, magnetic fields, radiation, cooling, ISM chemistry, star formation and feedback, cosmic rays, and so on). Such calculations are incredibly computationally demanding, and push us to use ever-larger computers. Modern supercomputers are composed of hundreds of thousands or millions of computing cores, often with specialized accelerator hardware, that typically are connected together using networks with complex topologies. The current generation of astrophysical simulation codes needs to be extensively modified and upgraded to take advantage of these machines, and strategies for dealing with the petabytes of resulting data need to be devised.

My group's research focus: Scaling of AMR CFD+gravity codes to 107 computing cores or more, including memory, solver, parallelism, and reliability issues; Large-scale data analysis, including complex and time-ordered datasets; Visualization of petabyte-scale datasets; Extracting statistically useful information from massive, inhomogeneous, time-ordered numerical and observational datasets; Development of algorithms for exascale supercomputers; Development of performance-portable software.

Computational Science Education

Overview: The ability to make models of systems - physical, biological, financial, social, or otherwise - is a critical skill that is widely used in the sciences, engineering, and in business. Similarly, the ability to manipulate and visualize data is critical. However, most students learn how to do these things informally, which often means that they have critical gaps in their understanding of model-making and data manipulation. As part of MSU's Department of Computational Mathematics, Science and Engineering, we are interested in exploring how students learn to use computers to make models, to work with data, and to think in an algorithmic ways (i.e., "think like a computer").

My group's research focus: learning to think about and make models; student identity relating to computational science; active learning in computational science; curricular reform.

The research done by my research group is supported in part by the National Science Foundation, the National Aeronautics and Space Administration, the U.S. Department of Energy, and Michigan State University.