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Browsing by study line "Astrophysical Sciences"

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  • The Impact of AGN Feedback on Galaxy Formation in Gas-Rich Mergers at z∼1-2 
    Mattero, Max (2024)
    This thesis studies gas-rich galaxy mergers at redshifts of z ∼ 1-2 using numerical simulations, with a particular focus on the effect of feedback from active galactic nuclei (AGNs). In total, 16 galaxy mergers at redshifts z = 1 and z = 2 were modeled using the simulation codes KETJU and GADGET-3. The simulations were performed on the supercomputer Mahti located at the Finnish IT Centre for Science (CSC). AGN feedback can be described as the radiative and mechanical energy released through accretion, which act to heat and disperse the remaining gaseous material surrounding the central supermassive black hole (SMBH). The feedback mechanisms include, for example, photoionization heating due to high-energy photons and winds and jets driven by the AGN. Numerically, AGN feedback was implemented using two models in this thesis: thermal and kinetic AGN feedback, in which the gas particles are either heated or ‘kicked’, respectively. In addition to AGN feedback, the simulations included metal-dependent gas cooling, stochastic star formation, and stellar feedback. The simulated progenitor galaxies were gas-rich spirals consistent with observed galaxies at redshifts z = 1 and z = 2. The virial masses of the progenitors were set to correspond to typical massive galaxies at their redshifts using the Press-Schechter mass function, while the initial masses for the central SMBHs were set using observed MBH-M⋆ and MBH-σ⋆ relations. The gas fractions and metal abundances of the progenitors were calibrated using observational data at their respective redshifts. The KETJU and GADGET-3 simulations produced very similar results for the overall evolution of a given merger configuration. Consistent with earlier studies, the kinetic feedback was observed to be significantly more effective at removing gas from the galaxies than the thermal feedback. The combined effect of AGN and stellar feedback was observed to strongly suppress star formation, with the star formation of one merger being almost completely shut down. The thermal and kinetic feedback models caused noticeable differences in the orbital evolution of the SMBH binaries. Merger timescales were significantly longer for the SMBHs in the KETJU simulations with kinetic feedback. In general, the merger timescales increased with decreasing initial eccentricity for the SMBH binary. The merger remnants were compared to observed MBH-σ⋆, R1/2-M⋆, fgas-M⋆, and mass-metallicity relations. Overall, the remnants were reasonably consistent with the observed relations. Hence, we can conclude that AGN feedback plays a crucial role in galaxy evolution and that both the thermal and kinetic feedback models are able to produce realistic high-redshift galaxies.
  • Time Series Analysis of Solar Wind Magnetic Field Fluctuations Using Information Theory and Complex Networks 
    Koikkalainen, Venla (2023)
    The aim of this study is to inspect fluctuations in the solar wind magnetic field for four different types of solar wind time series. The events considered are fast and slow solar wind, along with magnetic clouds and sheath regions, which are found in coronal mass ejections (CMEs). Time series measurements of these processes are analysed using methods from Information Theory and Complex Network Analysis. The techniques that are used here are the Fisher-Shannon information plane, the Jensen-Shannon complexity-entropy plane, and Horizontal Visibility Graph Analysis. Statistical and information theory measures as well as network analysis have recently been applied to studying time series in an attempt to determine their internal structure. There is promising research into these methods quantifying data as either chaotic, stochastic, or periodic. Knowing whether a process has e.g. a deterministic origin could shed light on the creation of said process. Applying these methods to solar wind, more information could be found about its formation at the Sun. In general, the solar wind data analysed in this thesis was found to be stochastic, which agrees with previous studies. In addition, when analysing magnetic field magnitude B, magnetic clouds appear to have more internal structure in the time series signal than the other types of solar wind data tested. The results obtained here are promising in terms of finding differences in structure within solar wind, and could be investigated further with the use of more solar wind data.

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