Although exotic objects like supermassive black holes (SMBHs) and dark matter halos do not emit or interact with light, we can still detect them across the vastness of space. By observing the gravitational dance of objects we can see, astronomers are able to infer the mass of the invisible objects they orbit. This has led to the discovery that nearly every massive galaxy hosts a SMBH at its center, and has confirmed that every galaxy is embedded in an extended halo of dark matter. However, the practice of inferring mass from the motions of bright kinematics tracers has many complications, not the least of which is that we seldom observe more than the line-of-sight component of the instantaneous velocity of a star. Consequently, astronomers must build dynamical models of the galaxies they wish to study. These models often rely on overly restrictive assumptions, or are crippled by degeneracies amongst their parameters and lack predictive power.
In this thesis, I introduce a significant advancement into the field of dynamical modeling. My modeling technique is based on the powerful principle of orbit superposition, also known as Schwarzschild Modeling. This technique is robust to many of the degeneracies
associated with dynamical modeling, and has enjoyed much success in measuring the SMBHs and dark matter halos of large elliptical or bulge-dominated galaxies. I use it in Chapter 2 to accurately measure the SMBH in the Sombrero Galaxy (NGC 4594) and to constrain its dark matter halo. Unfortunately, when measuring dark matter halos with Schwarzschild Modeling, the modeler is required to adopt a parameterization for the dark matter density profile. Often this is precisely the quantity one wishes to measure. To avoid this reliance on a priori parameterizations, I introduce a technique that calculates the profile non-parametrically. Armed with this powerful new technique, I set out to measure the distribution of dark matter in the halos of some of the smallest galaxies in the Universe.
These dwarf spheroidal galaxies (dSphs) orbit the Milky Way as satellites, and their dark matter content has been studied extensively. However, the models used to probe their halos have been simplistic and required overly restrictive assumptions. As a result,
robust conclusions about their dark matter content have remained elusive. Into this controversial and active area of study, I bring Non-Parametric Schwarzschild Modeling. The results I find offer the most robust and detailed measurements of the dark matter profiles in the dSphs to date.
I begin my study with the first application of standard Schwarzschild Modeling to any dSph galaxy by using it in Chapter 3 on Fornax. This chapter details the process of re-tooling Schwarzschild Modeling for the purpose of measuring these small galaxies. In Chapter 4, I introduce the fully non-parametric technique, and apply it to Draco as proof of concept. Chapter 5 presents the main results of this thesis. Here I apply Non-Parametric Schwarzschild Modeling to Draco, Carina, Fornax, Sculptor, and Sextans. By relaxing the assumption of a parameterization for the dark matter profile, I find a variety of profile types in these five galaxies---some of which are consistent with past observations, others consistent with predictions from simulations, and still others were completely unanticipated. Finally, in Chapter 6 I describe the modeling of these galaxies in more detail. I demonstrate the accuracy of Non-Parametric Schwarzschild Modeling by recovering a known dark matter profile from artificial simulated data. I also expound upon the modeling results by presenting the detailed orbit structure of the five dSphs. Lastly, I compare my results to hydrodynamical simulations to explore the link between dark matter profile type and the baryon content of the dSphs. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/24763 |
| Date | 23 June 2014 |
| Creators | Jardel, John Raymond |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0018 seconds