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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Observationally Constrained Metal Signatures of Galaxy Evolution in the Stars and Gas of Cosmological Simulations

Corlies, Lauren Nicole January 2016 (has links)
The halos of galaxies - consisting of gas, stars, and satellite galaxies - are formed and shaped by the most fundamental processes: hierarchical merging and the flow of gas into and out of galaxies. While these processes are hard to disentangle, metals are tied to the gas that fuels star formation and entrained in the wind that the deaths of these stars generate. As such, they can act as important indicators of the star formation, the chemical enrichment, and the outflow histories of galaxies. Thus, this thesis aims to take advantage of such metal signatures in the stars and gas to place observational constraints on current theories of galaxy evolution as implemented in cosmological simulations. The first two chapters consider the metallicities of stars in the stellar halo of the Milky Way and its surviving satellite dwarf galaxies. Chapter 2 pairs an N-body simulation with a semi-analytic model for supernova-driven winds to examine the early environment of a Milky Way-like galaxy. At z=10, progenitors of surviving z=0 satellite galaxies are found to sit preferentially on the outskirts of progenitor halos of the eventual main halo. The consequence of these positions is that main halo progenitors are found to more effectively cross-pollute each other than satellite progenitors. Thus, inhomogeneous cross-pollution as a result of different high-z spatial locations of different progenitors can help to explain observed differences in abundance patterns measured today. Chapter 3 expands this work into the analysis of a cosmological, hydrodynamical simulation of dwarf galaxies in the early universe. We find that simple assumptions for modeling the extent of supernova-driven winds used in Chapter 2 agree well with the simulation whereas the presence of inhomogeneous mixing in the simulation has a large effect on the stellar metallicities. Furthermore, the star-forming halos show both bursty and continuous SFHs, two scenarios proposed by stellar metallicity data. However, the metallicity distribution functions of the simulated halos are both too metal rich and too peaked when compared to the data. This comparison reveals that a complex SFH and a broad metallicity distribution can develop rapidly in the early Universe. The third chapter moves to the present day with a consideration of the circumgalactic medium (CGM) around nearby Milky Way-like galaxies. We compare a cosmological simulation of a Milky Way-like galaxy to recent absorption line data and find that a reduced extragalactic ultraviolet background brings the column density predictions into better agreement with the data. Similarly, when the observationally derived physical properties of the gas are compared to the simulation, we find that the simulation gas is always at temperatures approximately 0.5 dex higher. Thus, similar column densities can be produced from fundamentally different gas. Metal-line emission is then considered as a complementary approach to studying the CGM. From the simulations, we find that the brightest emission is less sensitive to the extragalactic background and that it closely follows the fundamental filamentary structure of the halo. This becomes increasingly true as the galaxy evolves from z = 1 to z = 0 and the majority of the gas transitions to a hotter, more diffuse phase. Finally, resolution is a limiting factor for the conclusions we can draw from emission observations but with moderate resolution and reasonable detection limits, upcoming instrumentation should place constraints on the physical properties of the CGM. Future work advancing the techniques in this thesis remain promising for putting new observational constraints on our theories of galaxy evolution.
2

Decoding Starlight with Big Survey Data, Machine Learning, and Cosmological Simulations

Blancato, Kirsten Nicole January 2020 (has links)
Stars, and collections of stars, encode rich signatures of stellar physics and galaxy evolution. With properties influenced by both their environment and intrinsic nature, stars retain information about astrophysical phenomena that are not otherwise directly observable. In the time-domain, the observed brightness variability of a star can be used to investigate physical processes occurring at the stellar surface and in the stellar interior. On a galactic scale, comparatively fixed properties of stars, including chemical abundances and stellar ages, serve as a multi-dimensional record of the origin of the galaxy. In the Milky Way, together with orbital properties, this informs the details of the subsequent evolution of our Galaxy since its formation. Extending beyond the Local Group, the attributes of unresolved stellar populations allow us to study the diversity of galaxies in the Universe. By examining the properties of stars, and how they vary across a range of spatial and temporal scales, this Dissertation connects the information residing within stars, to global processes in galactic formation and evolution. We develop new approaches to determine stellar properties, including rotation and surface gravity, from the variability that we directly observe. We offer new insight into the chemical enrichment history of the Milky Way, tracing different stellar explosions, that capture billions of years of evolution. We advance knowledge and understanding of how stars and galaxies are linked, by examining differences in the initial stellar mass distributions comprising galaxies, as they form. In building up this knowledge, we highlight current tensions between data and theory. By synthesizing numerical simulations, large observational data sets, and machine learning techniques, this work makes valuable methodological contributions to maximize insights from diverse ensembles of current and future stellar observations.

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