@phdthesis{Mitzkus2017,
  author    = {Mitzkus, Martin},
  title     = {Spectroscopic surface brightness fluctuations},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-406327},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ix, 89},
  year      = {2017},
  abstract  = {Galaxies evolve on cosmological timescales and to study this evolution we can either study the stellar populations, tracing the star formation and chemical enrichment, or the dynamics, tracing interactions and mergers of galaxies as well as accretion. In the last decades this field has become one of the most active research areas in modern astrophysics and especially the use of integral field spectrographs furthered our understanding. This work is based on data of NGC 5102 obtained with the panoramic integral field spectrograph MUSE. The data are analysed with two separate and complementary approaches: In the first part, standard methods are used to measure the kinematics and than model the gravitational potential using these exceptionally high-quality data. In the second part I develop the new method of surface brightness fluctuation spectroscopy and quantitatively explore its potential to investigate the bright evolved stellar population. Measuring the kinematics of NGC 5102 I discover that this low-luminosity S0 galaxy hosts two counter rotating discs. The more central stellar component co-rotates with the large amount of HI gas. Investigating the populations I find strong central age and metallicity gradients with a younger and more metal rich central population. The spectral resolution of MUSE does not allow to connect these population gradients with the two counter rotating discs. The kinematic measurements are modelled with Jeans anisotropic models to infer the gravitational potential of NGC 5102. Under the self-consistent mass-follows-light assumption none of the Jeans models is able to reproduce the observed kinematics. To my knowledge this is the strongest evidence evidence for a dark matter dominated system obtained with this approach so far. Including a Navarro, Frenk \& White dark matter halo immediately solves the discrepancies. A very robust result is the logarithmic slope of the total matter density. For this low-mass galaxy I find a value of -1.75 +- 0.04, shallower than an isothermal halo and even shallower than published values for more massive galaxies. This confirms a tentative relation between total mass slope and stellar mass of galaxies. The Surface Brightness Fluctuation (SBF) method is a well established distance measure, but due to its sensitive to bright stars also used to study evolved stars in unresolved stellar populations. The wide-field spectrograph MUSE offers the possibility to apply this technique for the first time to spectroscopic data. In this thesis I develop the spectroscopic SBF technique and measure the first SBF spectrum of any galaxy. I discuss the challenges for measuring SBF spectra that rise due to the complexity of integral field spectrographs compared to imaging instruments. Since decades, stellar population models indicate that SBFs in intermediate-to-old stellar systems are dominated by red giant branch and asymptotic giant branch stars. Especially the later carry significant model uncertainties, making these stars a scientifically interesting target. Comparing the NGC 5102 SBF spectrum with stellar spectra I show for the first time that M-type giants cause the fluctuations. Stellar evolution models suggest that also carbon rich thermally pulsating asymptotic giant branch stars should leave a detectable signal in the SBF spectrum. I cannot detect a significant contribution from these stars in the NGC 5102 SBF spectrum. I have written a stellar population synthesis tool that predicts for the first time SBF spectra. I compute two sets of population models: based on observed and on theoretical stellar spectra. In comparing the two models I find that the models based on observed spectra predict weaker molecular features. The comparison with the NGC 5102 spectrum reveals that these models are in better agreement with the data.},
  language  = {en}
}