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Research interests:  Galactic structure, Milky Way dynamics, high resolution hydrodynamic simulations, galactic chemical evolution, star cluster evolution & the scientific application of database technology

Research Projects

  • The Galactic Potential: Jeans Equations Applied to the Milky Way

    As a part of my thesis work with Željko Ivezić, I applied the cylindrically symmetric form of Jeans equations to stars in the Milky Way to infer the underlying mass distribution. Jeans Equations statistically estimate the gravitational potential using observable stellar kinematics. My work focused on quantifying the robustness of this technique using a N-body+SPH simulation from UW's N-body Shop and applying the same methodology to a mock SDSS catalog generated by galfast. The morphology of the resulting 2D SDSS acceleration maps revealed that, in a Newtonian framework, the implied gravitational potential cannot be explained by visible matter alone. My collaborators and I found that within galactocentric distancs of ~20 kpc, the dark matter halo potential is well described as an oblate halo with axis ratio qDM = 0.7+/-0.1; this corresponds to an axis ratio qDM = 0.4+/-0.1 for the dark matter density distribution. The component nature of our acceleration maps allow us to reject several MOND models as an explanation of the observed halo star kinematics. Using this form of Jeans equations, we will be able to map the dark matter halo to much larger distances from the Galactic center with upcoming deep optical surveys, such as LSST.

    A lengthy ApJ paper discussing my methodological approach and major results can be found here. My earlier ApJ Letter paper that highlighted initial results from this work can be found here.​​

  • Big-Data Management: Creating & Analyzing Galactic Merger Trees in the Cloud 

 Members of UW's Database groupUW's N-body Shop, and I recently developed MyMergerTree, a vertical data-analysis service accessible through a web interface and designed to meet the needs of astronomers who work with large-scale cosmological simulations. Cosmological simulations track the evolution of galaxies such as our own over a span of 14 billions years from the Big Bang to the present day. The standard model of galaxy formation involves the hierarchical assembly of galaxies through merging of smaller galaxies. Any given galaxy will have a unique merger history; we would like to know how much these merger histories vary depending on a galaxy’s final mass, proximity to other galaxies, or other factors. MyMergerTree targets this active area of research and has initially been deployed on a 5 TB cosmological volume that contains ~2 billion particles. To facilitate analyses at scale, MyMergerTree uses a new Big Data management and analysis service called UW's N-body ShopUW's Database groupMembers of 

We recently presented our findings at the Association for Computing Machinery's Special Interest Group on Management of Data (SIGMOD) 2014 conference during DanaC: Workshop on Data analytics in the Cloud. The refereed SIGMOD paper that resulted from this work can be found here.

  • Chemical Gradients in Dwarf Spheriodal Galaxies 


I have ongoing work with Alyson Brooks to investigate the origin of chemical trends in dwarf spheroidal galaxies. Our survey includes 21 subhalos from 2 high resolution cosmological simulations of L* galaxies; these subhalos were selected to be gas free at present day and have resolved star formation histories. Our survey sample includes a range of total mass, stellar mass, infall time, and star formation histories; this sample was previously used by Alyson and Adi Zolotov to investigate why luminous satellite galaxies have reduced central masses. My work focuses on characterizing radial trends in [Fe/H] and [O/Fe] as a function of time and environmental conditions to determine if internal or external mechanisms are the primary drivers of trends observed today.

  • Radial Migration and the Milky Way's Thick Disk


For the first part of my thesis work, Željko Ivezić and I collaborated with Rok Roškar, Victor Debattista, and Tom Quinn to compare observations of the Milky Way's disk with N-body+SPH simulations. We determined the spatial, kinematic and metallicity distributions predicted by an isolated Milky Way simulation that naturally included significant radial migration and compared this to the distributions observed by SDSS and GCS. We found the N-body model to be in qualitative agreement with the observational data and plausibly motivates the disk's double-exponential vertical structure and other characteristics as due to internal evolution.

I presented our findings at Stars with Borders: Radial Migration in Spiral Galaxies; the refereed paper that resulted from this work can be found here.

  • Managing Simulations Using Distributed Database Technology 

    Parallel database management systems (DBMSs) and massive-scale data processing systems such as MapReduce hold promise, but are largely untested, to manage the large datasets produced in astronomy. I worked with Jeff Gardner and members of UW's Database group (Yongchul KwonBill HoweMagdalena Balazinska, and Dylan Nunley) to study how well these engines manage several simulations of cosmological volumes. To do this, we developed a use case that comprises five representative queries; we implemented this use case in one distributed DBMS and in the Pig/Hadoop system. We compared the performance of the tools to each other and to hand-written IDL scripts. We found that certain representative analyses are easy to express in each engine's high-level language and both systems provide competitive performance and improved scalability relative to current IDL-based methods.


    I presented our findings at The First Workshop on Interfaces and Abstractions for Scientific Data (IASDS) 2009; the refereed paper that resulted from this work can be found here.

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