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Subject of this work is the investigation of universal scaling laws which are observed in coupled chaotic systems. Progress is made by replacing the chaotic fluctuations in the perturbation dynamics by stochastic processes. First, a continuous-time stochastic model for weakly coupled chaotic systems is introduced to study the scaling of the Lyapunov exponents with the coupling strength (coupling sensitivity of chaos). By means of the the Fokker-Planck equation scaling relations are derived, which are confirmed by results of numerical simulations. Next, the new effect of avoided crossing of Lyapunov exponents of weakly coupled disordered chaotic systems is described, which is qualitatively similar to the energy level repulsion in quantum systems. Using the scaling relations obtained for the coupling sensitivity of chaos, an asymptotic expression for the distribution function of small spacings between Lyapunov exponents is derived and compared with results of numerical simulations. Finally, the synchronization transition in strongly coupled spatially extended chaotic systems is shown to resemble a continuous phase transition, with the coupling strength and the synchronization error as control and order parameter, respectively. Using results of numerical simulations and theoretical considerations in terms of a multiplicative noise partial differential equation, the universality classes of the observed two types of transition are determined (Kardar-Parisi-Zhang equation with saturating term, directed percolation).
In the present work, we study wave phenomena in strongly nonlinear lattices. Such lattices are characterized by the absence of classical linear waves. We demonstrate that compactons – strongly localized solitary waves with tails decaying faster than exponential – exist and that they play a major role in the dynamics of the system under consideration. We investigate compactons in different physical setups. One part deals with lattices of dispersively coupled limit cycle oscillators which find various applications in natural sciences such as Josephson junction arrays or coupled Ginzburg-Landau equations. Another part deals with Hamiltonian lattices. Here, a prominent example in which compactons can be found is the granular chain. In the third part, we study systems which are related to the discrete nonlinear Schrödinger equation describing, for example, coupled optical wave-guides or the dynamics of Bose-Einstein condensates in optical lattices. Our investigations are based on a numerical method to solve the traveling wave equation. This results in a quasi-exact solution (up to numerical errors) which is the compacton. Another ansatz which is employed throughout this work is the quasi-continuous approximation where the lattice is described by a continuous medium. Here, compactons are found analytically, but they are defined on a truly compact support. Remarkably, both ways give similar qualitative and quantitative results. Additionally, we study the dynamical properties of compactons by means of numerical simulation of the lattice equations. Especially, we concentrate on their emergence from physically realizable initial conditions as well as on their stability due to collisions. We show that the collisions are not exactly elastic but that a small part of the energy remains at the location of the collision. In finite lattices, this remaining part will then trigger a multiple scattering process resulting in a chaotic state.
Relativistic pair beams produced in the cosmic voids by TeV gamma rays from blazars are expected to produce a detectable GeV-scale cascade emission missing in the observations. The suppression of this secondary cascade implies either the deflection of the pair beam by intergalactic magnetic fields (IGMFs) or an energy loss of the beam due to the electrostatic beam-plasma instability. IGMF of femto-Gauss strength is sufficient to significantly deflect the pair beams reducing the flux of secondary cascade below the observational limits. A similar flux reduction may result in the absence of the IGMF from the beam energy loss by the instability before the inverse Compton cooling. This dissertation consists of two studies about the instability role in the evolution of blazar-induced beams.
Firstly, we investigated the effect of sub-fG level IGMF on the beam energy loss by the instability. Considering IGMF with correlation lengths smaller than a few kpc, we found that such fields increase the transverse momentum of the pair beam particles, dramatically reducing the linear growth rate of the electrostatic instability and hence the energy-loss rate of the pair beam. Our results show that the IGMF eliminates beam plasma instability as an effective energy-loss agent at a field strength three orders of magnitude below that needed to suppress the secondary cascade emission by magnetic deflection. For intermediate-strength IGMF, we do not know a viable process to explain the observed absence of GeV-scale cascade emission and hence can be excluded.
Secondly, we probed how the beam-plasma instability feeds back on the beam, using a realistic two-dimensional beam distribution. We found that the instability broadens the beam opening angles significantly without any significant energy loss, thus confirming a recent feedback study on a simplified one-dimensional beam distribution. However, narrowing diffusion feedback of the beam particles with Lorentz factors less than 1e6 might become relevant even though initially it is negligible. Finally, when considering the continuous creation of TeV pairs, we found that the beam distribution and the wave spectrum reach a new quasi-steady state, in which the scattering of beam particles persists and the beam opening angle may increase by a factor of hundreds. This new intrinsic scattering of the cascade can result in time delays of around ten years, thus potentially mimicking the IGMF deflection. Understanding the implications on the GeV cascade emission requires accounting for inverse Compton cooling and simulating the beam-plasma system at different points in the IGM.
Since their discovery in 1610 by Galileo Galilei, Saturn's rings continue to fascinate both experts and amateurs. Countless numbers of icy grains in almost Keplerian orbits reveal a wealth of structures such as ringlets, voids and gaps, wakes and waves, and many more. Grains are found to increase in size with increasing radial distance to Saturn. Recently discovered "propeller" structures in the Cassini spacecraft data, provide evidence for the existence of embedded moonlets. In the wake of these findings, the discussion resumes about origin and evolution of planetary rings, and growth processes in tidal environments. In this thesis, a contact model for binary adhesive, viscoelastic collisions is developed that accounts for agglomeration as well as restitution. Collisional outcomes are crucially determined by the impact speed and masses of the collision partners and yield a maximal impact velocity at which agglomeration still occurs. Based on the latter, a self-consistent kinetic concept is proposed. The model considers all possible collisional outcomes as there are coagulation, restitution, and fragmentation. Emphasizing the evolution of the mass spectrum and furthermore concentrating on coagulation alone, a coagulation equation, including a restricted sticking probability is derived. The otherwise phenomenological Smoluchowski equation is reproduced from basic principles and denotes a limit case to the derived coagulation equation. Qualitative and quantitative analysis of the relevance of adhesion to force-free granular gases and to those under the influence of Keplerian shear is investigated. Capture probability, agglomerate stability, and the mass spectrum evolution are investigated in the context of adhesive interactions. A size dependent radial limit distance from the central planet is obtained refining the Roche criterion. Furthermore, capture probability in the presence of adhesion is generally different compared to the case of pure gravitational capture. In contrast to a Smoluchowski-type evolution of the mass spectrum, numerical simulations of the obtained coagulation equation revealed, that a transition from smaller grains to larger bodies cannot occur via a collisional cascade alone. For parameters used in this study, effective growth ceases at an average size of centimeters.
The theory of atomic Boson-Fermion mixtures in the dilute limit beyond mean-field is considered in this thesis. Extending the formalism of quantum field theory we derived expressions for the quasi-particle excitation spectra, the ground state energy, and related quantities for a homogenous system to first order in the dilute gas parameter. In the framework of density functional theory we could carry over the previous results to inhomogeneous systems. We then determined to density distributions for various parameter values and identified three different phase regions: (i) a stable mixed regime, (ii) a phase separated regime, and (iii) a collapsed regime. We found a significant contribution of exchange-correlation effects in the latter case. Next, we determined the shift of the Bose-Einstein condensation temperature caused by Boson-Fermion interactions in a harmonic trap due to redistribution of the density profiles. We then considered Boson-Fermion mixtures in optical lattices. We calculated the criterion for stability against phase separation, identified the Mott-insulating and superfluid regimes both, analytically within a mean-field calculation, and numerically by virtue of a Gutzwiller Ansatz. We also found new frustrated ground states in the limit of very strong lattices. ----Anmerkung: Der Autor ist Träger des durch die Physikalische Gesellschaft zu Berlin vergebenen Carl-Ramsauer-Preises 2004 für die jeweils beste Dissertation der vier Universitäten Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin und Universität Potsdam.
Planets outside our solar system, so-called "exoplanets", can be detected with different methods, and currently more than 5000 exoplanets have been confirmed, according to NASA Exoplanet Archive. One major highlight of the studies on exoplanets in the past twenty years is the characterization of their atmospheres usingtransmission spectroscopy as the exoplanet transits. However, this characterization is a challenging process and sometimes there are reported discrepancies in the literature regarding the atmosphere of the same exoplanet. One potential reason for the observed atmospheric inconsistencies is called impact parameter degeneracy, and it is highly driven by the limb darkening effect of the host star. A brief introductionto those topics in presented in chapter 1, while the motivation and objectives of thiswork are described in chapter 2.The first goal is to clarify the origin of the transmission spectrum, which is anindicator of an exoplanet’s atmosphere; whether it is real or influenced by the impactparameter degeneracy. A second goal is to determine whether photometry from space using the Transiting Exoplanet Survey Satellite (TESS), could improve on the major parameters, which are responsible for the aforementioned degeneracy, of known exoplanetary systems. Three individual projects were conducted in order toaddress those goals. The three manuscripts are presented, in short, in the manuscriptoverview in chapter 3.More specifically, in chapter 4, the first manuscript is presented, which is an ex-tended investigation on the impact parameter degeneracy and its application onsynthetic transmission spectra. Evidently, the limb darkening of the host star isan important driver for this effect. It keeps the degeneracy persisting through different groups of exoplanets, based on the uncertainty of their impact parameter and on the type of their host star. The second goal, was addressed in the second and third manuscripts (chapter 5 and chapter 6 respectively). Using observationsfrom the TESS mission, two samples of exoplanets were studied; 10 transiting inflated hot-Jupiters and 43 transiting grazing systems. Potentially, the refinement or confirmation of their major system parameters’ measurements can assist in solving current or future discrepancies regarding their atmospheric characterization.In chapter 7 the conclusions of this work are discussed, while in chapter 8 itis proposed how TESS’s measurements can be able to discern between erroneousinterpretations of transmission spectra, especially on systems where the impact parameter degeneracy is likely not applicable.
The topic of synchronization forms a link between nonlinear dynamics and neuroscience. On the one hand, neurobiological research has shown that the synchronization of neuronal activity is an essential aspect of the working principle of the brain. On the other hand, recent advances in the physical theory have led to the discovery of the phenomenon of phase synchronization. A method of data analysis that is motivated by this finding - phase synchronization analysis - has already been successfully applied to empirical data. The present doctoral thesis ties up to these converging lines of research. Its subject are methodical contributions to the further development of phase synchronization analysis, as well as its application to event-related potentials, a form of EEG data that is especially important in the cognitive sciences. The methodical contributions of this work consist firstly in a number of specialized statistical tests for a difference in the synchronization strength in two different states of a system of two oscillators. Secondly, in regard of the many-channel character of EEG data an approach to multivariate phase synchronization analysis is presented. For the empirical investigation of neuronal synchronization a classic experiment on language processing was replicated, comparing the effect of a semantic violation in a sentence context with that of the manipulation of physical stimulus properties (font color). Here phase synchronization analysis detects a decrease of global synchronization for the semantic violation as well as an increase for the physical manipulation. In the latter case, by means of the multivariate analysis the global synchronization effect can be traced back to an interaction of symmetrically located brain areas.<BR> The findings presented show that the method of phase synchronization analysis motivated by physics is able to provide a relevant contribution to the investigation of event-related potentials in the cognitive sciences.
Galaxies are among the most complex systems that can currently be modelled with a computer. A realistic simulation must take into account cosmology and gravitation as well as effects of plasma, nuclear, and particle physics that occur on very different time, length, and energy scales. The Milky Way is the ideal test bench for such simulations, because we can observe millions of its individual stars whose kinematics and chemical composition are records of the evolution of our Galaxy. Thanks to the advent of multi-object spectroscopic surveys, we can systematically study stellar populations in a much larger volume of the Milky Way. While the wealth of new data will certainly revolutionise our picture of the formation and evolution of our Galaxy and galaxies in general, the big-data era of Galactic astronomy also confronts us with new observational, theoretical, and computational challenges.
This thesis aims at finding new observational constraints to test Milky-Way models, primarily based on infra-red spectroscopy from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and asteroseismic data from the CoRoT mission. We compare our findings with chemical-evolution models and more sophisticated chemodynamical simulations. In particular we use the new powerful technique of combining asteroseismic and spectroscopic observations that allows us to test the time dimension of such models for the first time. With CoRoT and APOGEE (CoRoGEE) we can infer much more precise ages for distant field red-giant stars, opening up a new window for Galactic archaeology.
Another important aspect of this work is the forward-simulation approach that we pursued when interpreting these complex datasets and comparing them to chemodynamical models.
The first part of the thesis contains the first chemodynamical study conducted with the APOGEE survey. Our sample comprises more than 20,000 red-giant stars located within 6 kpc from the Sun, and thus greatly enlarges the Galactic volume covered with high-resolution spectroscopic observations. Because APOGEE is much less affected by interstellar dust extinction, the sample covers the disc regions very close to the Galactic plane that are typically avoided by optical surveys. This allows us to investigate the chemo-kinematic properties of the Milky Way's thin disc outside the solar vicinity. We measure, for the first time with high-resolution data, the radial metallicity gradient of the disc as a function of distance from the Galactic plane, demonstrating that the gradient flattens and even changes its sign for mid-plane distances greater than 1 kpc.
Furthermore, we detect a gap between the high- and low-[$\alpha$/Fe] sequences in the chemical-abundance diagram (associated with the thin and thick disc) that unlike in previous surveys can hardly be explained by selection effects. Using 6D kinematic information, we also present chemical-abundance diagrams cleaned from stars on kinematically hot orbits. The data allow us to confirm without doubt that the scale length of the (chemically-defined) thick disc is significantly shorter than that of the thin disc.
In the second part, we present our results of the first combination of asteroseismic and spectroscopic data in the context of Galactic Archaeology. We analyse APOGEE follow-up observations of 606 solar-like oscillating red giants in two CoRoT fields close to the Galactic plane. These stars cover a large radial range of the Galactic disc (4.5 kpc $\lesssim R_{\rm Gal}\lesssim15$ kpc) and a large age baseline (0.5 Gyr $\lesssim \tau\lesssim$ 13 Gyr), allowing us to study the age- and radius-dependence of the [$\alpha$/Fe] vs. [Fe/H] distributions. We find that the age distribution of the high-[$\alpha$/Fe] sequence appears to be broader than expected from a monolithically-formed old thick disc that stopped to form stars 10 Gyr ago. In particular, we discover a significant population of apparently young, [$\alpha$/Fe]-rich stars in the CoRoGEE data whose existence cannot be explained by standard chemical-evolution models. These peculiar stars are much more abundant in the inner CoRoT field LRc01 than in the outer-disc field LRc01, suggesting that at least part of this population has a chemical-evolution rather than a stellar-evolution origin, possibly due to a peculiar chemical-enrichment history of the inner disc. We also find that strong radial migration is needed to explain the abundance of super-metal-rich stars in the outer disc.
Finally, we use the CoRoGEE sample to study the time evolution of the radial metallicity gradient in the thin disc, an observable that has been the subject of observational and theoretical debate for more than 20 years. By dividing the CoRoGEE dataset into six age bins, performing a careful statistical analysis of the radial [Fe/H], [O/H], and [Mg/Fe] distributions, and accounting for the biases introduced by the observation strategy, we obtain reliable gradient measurements. The slope of the radial [Fe/H] gradient of the young red-giant population ($-0.058\pm0.008$ [stat.] $\pm0.003$ [syst.] dex/kpc) is consistent with recent Cepheid data. For the age range of $1-4$ Gyr, the gradient steepens slightly ($-0.066\pm0.007\pm0.002$ dex/kpc), before flattening again to reach a value of $\sim-0.03$ dex/kpc for stars with ages between 6 and 10 Gyr. This age dependence of the [Fe/H] gradient can be explained by a nearly constant negative [Fe/H] gradient of $\sim-0.07$ dex/kpc in the interstellar medium over the past 10 Gyr, together with stellar heating and migration. Radial migration also offers a new explanation for the puzzling observation that intermediate-age open clusters in the solar vicinity (unlike field stars) tend to have higher metallicities than their younger counterparts. We suggest that non-migrating clusters are more likely to be kinematically disrupted, which creates a bias towards high-metallicity migrators from the inner disc and may even steepen the intermediate-age cluster abundance gradient.
Gravitational-wave (GW) astrophysics is a field in full blossom. Since the landmark detection of GWs from a binary black hole on September 14th 2015, fifty-two compact-object binaries have been reported by the LIGO-Virgo collaboration. Such events carry astrophysical and cosmological information ranging from an understanding of how black holes and neutron stars are formed, what neutron stars are composed of, how the Universe expands, and allow testing general relativity in the highly-dynamical strong-field regime. It is the goal of GW astrophysics to extract such information as accurately as possible. Yet, this is only possible if the tools and technology used to detect and analyze GWs are advanced enough. A key aspect of GW searches are waveform models, which encapsulate our best predictions for the gravitational radiation under a certain set of parameters, and that need to be cross-correlated with data to extract GW signals. Waveforms must be very accurate to avoid missing important physics in the data, which might be the key to answer the fundamental questions of GW astrophysics. The continuous improvements of the current LIGO-Virgo detectors, the development of next-generation ground-based detectors such as the Einstein Telescope or the Cosmic Explorer, as well as the development of the Laser Interferometer Space Antenna (LISA), demand accurate waveform models. While available models are enough to capture the low spins, comparable-mass binaries routinely detected in LIGO-Virgo searches, those for sources from both current and next-generation ground-based and spaceborne detectors must be accurate enough to detect binaries with large spins and asymmetry in the masses. Moreover, the thousands of sources that we expect to detect with future detectors demand accurate waveforms to mitigate biases in the estimation of signals’ parameters due to the presence of a foreground of many sources that overlap in the frequency band. This is recognized as one of the biggest challenges for the analysis of future-detectors’ data, since biases might hinder the extraction of important astrophysical and cosmological information from future detectors’ data. In the first part of this thesis, we discuss how to improve waveform models for binaries with high spins and asymmetry in the masses. In the second, we present the first generic metrics that have been proposed to predict biases in the presence of a foreground of many overlapping signals in GW data.
For the first task, we will focus on several classes of analytical techniques. Current models for LIGO and Virgo studies are based on the post-Newtonian (PN, weak-field, small velocities) approximation that is most natural for the bound orbits that are routinely detected in GW searches. However, two other approximations have risen in prominence, the post-Minkowskian (PM, weak- field only) approximation natural for unbound (scattering) orbits and the small-mass-ratio (SMR) approximation typical of binaries in which the mass of one body is much bigger than the other. These are most appropriate to binaries with high asymmetry in the masses that challenge current waveform models. Moreover, they allow one to “cover” regions of the parameter space of coalescing binaries, thereby improving the interpolation (and faithfulness) of waveform models. The analytical approximations to the relativistic two-body problem can synergically be included within the effective-one-body (EOB) formalism, in which the two-body information from each approximation can be recast into an effective problem of a mass orbiting a deformed Schwarzschild (or Kerr) black hole. The hope is that the resultant models can cover both the low-spin comparable-mass binaries that are routinely detected, and the ones that challenge current models. The first part of this thesis is dedicated to a study about how to best incorporate information from the PN, PM, SMR and EOB approaches in a synergistic way. We also discuss how accurate the resulting waveforms are, as compared against numerical-relativity (NR) simulations. We begin by comparing PM models, whether alone or recast in the EOB framework, against PN models and NR simulations. We will show that PM information has the potential to improve currently-employed models for LIGO and Virgo, especially if recast within the EOB formalism. This is very important, as the PM approximation comes with a host of new computational techniques from particle physics to exploit. Then, we show how a combination of PM and SMR approximations can be employed to access previously-unknown PN orders, deriving the third subleading PN dynamics for spin-orbit and (aligned) spin1-spin2 couplings. Such new results can then be included in the EOB models currently used in GW searches and parameter estimation studies, thereby improving them when the binaries have high spins. Finally, we build an EOB model for quasi-circular nonspinning binaries based on the SMR approximation (rather than the PN one as usually done). We show how this is done in detail without incurring in the divergences that had affected previous attempts, and compare the resultant model against NR simulations. We find that the SMR approximation is an excellent approximation for all (quasi-circular nonspinning) binaries, including both the equal-mass binaries that are routinely detected in GW searches and the ones with highly asymmetric masses. In particular, the SMR-based models compare much better than the PN models, suggesting that SMR-informed EOB models might be the key to model binaries in the future. In the second task of this thesis, we work within the linear-signal ap- proximation and describe generic metrics to predict inference biases on the parameters of a GW source of interest in the presence of confusion noise from unfitted foregrounds and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simple (yet realistic) LISA sources, and demonstrate its validity against Monte-Carlo simulations. The metrics we describe pave the way for more realistic studies to quantify the biases with future ground-based and spaceborne detectors.
During a dark night, it is possible to observe thousands of stars by eye. All these stars are located within the Milky Way, our home. Not all stars are the same, they can have different sizes, masses, temperatures and ages. Heavy stars do not live long (in astronomical terms), only a few million years, but stars less massive than the Sun can get more than ten billion years old. Such small stars that formed in the beginning of the Universe still shine today. These ancient stars are very helpful to learn more about the early Universe, the First Stars and the history of the Milky Way. But how do you recognise an ancient star? Using their chemical fingerprints! In the beginning of the Universe, there were only two chemical elements: hydrogen and helium (and a tiny bit of lithium). All the heavier elements like carbon, calcium and iron were only made later within stars and their explosions. The amount of chemical elements in the Universe increases with the number of stars that are born, evolve and explode. Stars that form later are born with more heavy elements, or a greater metallicity. In the field of astronomy that is called “Galactic Archaeology”, stars of various metallicities are used to study the history of the Milky Way. In this doctoral thesis, the focus is on metal-poor stars because these are expected to be the oldest and can therefore tell us a lot about the early history of our Galaxy.
Until today, we still have not discovered a metal-free star. The most metal-poor stars, however, give us important insights in the lives and deaths of the First Stars. Many of the oldest, most metal-poor stars have an unexpectedly large amount of carbon, compared to for example iron. These carbon-enhanced metal-poor (CEMP) stars tell us something about the very first stars in the Universe: they somehow produced a lot of carbon. If we look at the precise chemical fingerprints of the CEMP stars, we can learn a lot more. But our interpretation depends on the assumption that the chemical fingerprint of a star does not change during its life. In this thesis, new data is presented that shows that this assumption may be too simple: many extremely metal-poor CEMP stars are members of binary systems. Interactions between two stars in a binary system can pollute the surface of the stars. Likely not all of the CEMP stars in binary systems were actually polluted, but we should be very careful in our interpretations of the fingerprints of these stars.
The CEMP stars and other metal-poor stars are also important for our understanding of the early history of the Milky Way. Most researchers who study metal-poor stars look for these stars in the halo of the Milky Way: a huge diffuse Galactic component containing about 1% of the stars in our Galaxy. However, models predict that the oldest metal-poor stars are located in the center of the Milky Way, in the bulge. The metal-poor inner Galaxy is unfortunately difficult to study due to large amounts of dust between us and the center and an overwhelming majority of metal-rich stars. This thesis presents results from the successful Pristine Inner Galaxy Survey (PIGS), a new survey looking for (and finding) the oldest stars in the bulge of the Milky Way. PIGS is using images with a specific color that is sensitive to the metallicity of stars, and can therefore efficiently select the metal-poor stars among millions of other, more metal-rich stars. The interesting candidates are followed up with spectroscopy, which is then analysed using two independent methods. With this strategy, PIGS has discovered the largest sample of metal-poor stars in the inner Galaxy to date. A new result from the PIGS data is that the metal-poor stars rotate more slowly around the Galactic center compared to the more metal-rich stars, and they show larger randomness in their motions as well. Another important contribution from PIGS is the discovery of tens of CEMP stars in the inner Galaxy, where previously only two such stars were known.
The new results from this thesis help us to understand the First Stars and the early history of the Milky Way. Ongoing and future large surveys will provide us with a lot of additional data in the coming years. It is an exciting time for the field of Galactic Archaeology.
Future magnetic recording industry needs a high-density data storage technology. However, switching the magnetization of small bits requires high magnetic fields that cause excessive heat dissipation. Therefore, controlling magnetism without applying external magnetic field is an important research topic for potential applications in data storage devices with low power consumption. Among the different approaches being investigated, two of them stand out, namely i) all-optical helicity dependent switching (AO-HDS) and ii) ferroelectric control of magnetism. This thesis aims to contribute towards a better understanding of the physical processes behinds these effects as well as reporting new and exciting possibility for the optical and/or electric control of magnetic properties. Hence, the thesis contains two differentiated chapters of results; the first devoted to AO-HDS on TbFe alloys and the second to the electric field control of magnetism in an archetypal Fe/BaTiO3 system.
In the first part, the scalability of the AO-HDS to small laser spot-sizes of few microns in the ferrimagnetic TbFe alloy is investigated by spatially resolving the magnetic contrast with photo-emission electron microscopy (PEEM) and X-ray magnetic circular dichroism (XMCD). The results show that the AO-HDS is a local effect within the laser spot size that occurs in the ring-shaped region in the vicinity of thermal demagnetization. Within the ring region, the helicity dependent switching occurs via thermally activated domain wall motion. Further, the thesis reports on a novel effect of thickness dependent inversion of the switching orientation. It addresses some of the important questions like the role of laser heating and the microscopic mechanism driving AO-HDS.
The second part of the thesis focuses on the electric field control of magnetism in an artificial multiferroic heterostructure. The sample consists of an Fe wedge with thickness varying between 0:5 nm and 3 nm, deposited on top of a ferroelectric and ferroelastic BaTiO3 [001]-oriented single crystal substrate. Here, the magnetic contrast is imaged via PEEM and XMCD as a function of out-of-plane voltage. The results show the evidence of the electric field control of superparamagnetism mediated by a ferroelastic modification of the magnetic anisotropy. The changes in the magnetoelastic anisotropy drive the transition from the superparamagnetic to superferromagnetic state at localized sample positions.
Polymeric semiconductors are strong contenders for replacing traditional inorganic semiconductors in electronic applications requiring low power, low cost and flexibility, such as biosensors, flexible solar cells and electronic displays. Molecular doping has the potential to enable this revolution by improving the conductivity and charge transport properties of this class of materials. Despite decades of research in this field, gaps in our understanding of the nature of dopant–polymer interactions has resulted in limited commercialization of this technology. This work aims at providing a deeper insight into the underlying mechanisms of molecular p-doping of semiconducting polymers in the solution and solid-state, and thereby bring the scientific community closer to realizing the dream of making organic semiconductors commonplace in the electronics industry. The role of 1) dopant size/shape, 2) polymer chain aggregation and 3) charge delocalization on the doping mechanism and efficiency is addressed using optical (UV-Vis-NIR) and electron paramagnetic resonance (EPR) spectroscopies. By conducting a comprehensive study of the nature and concentration of the doping-induced species in solutions of the polymer poly(3-hexylthiophene) (P3HT) with 3 different dopants, we identify the unique optical signatures of the delocalized polaron, localized polaron and charge-transfer complex, and report their extinction coefficient values. Furthermore, with X-ray diffraction, atomic force microscopy and electrical conductivity measurements, we study the impact of processing technique and doping mechanism on the morphology and thereby, charge transport through the doped films.
This work demonstrates that the doping mechanism and type of doping-induced species formed are strongly influenced by the polymer backbone arrangement rather than dopant shape/size. The ability of the polymer chain to aggregate is found to be crucial for efficient charge transfer (ionization) and polaron delocalization. At the same time, our results suggest that the high ionization efficiency of a dopant–polymer system in solution may subsequently hinder efficient charge transport in the solid-state due to the reduction in the fraction of tie chains, which enable charges to move efficiently between aggregated domains in the films. This study demonstrates the complex multifaceted nature of polymer doping while providing important hints for the future design of dopant-host systems and film fabrication techniques.
In this dissertation we introduce a concept of light driven active and passive manipulation of colloids trapped at solid/liquid interface. The motion is induced due to generation of light driven diffusioosmotic flow (LDDO) upon irradiation with light of appropriate wavelength. The origin of the flow is due to osmotic pressure gradient resulting from a concentration gradient at the solid/liquid interface of the photosensitive surfactant present in colloidal dispersion. The photosensitive surfactant consists of a cationic head group and a hydrophobic tail in which azobenzene group is integrated in. The azobenzene is known to undergo reversible photo-isomerization from a stable trans to a meta stable cis state under irradiation with UV light. Exposure to light of larger wavelength results in back photo-isomerization from cis to trans state. The two isomers have different molecular properties, for instance, trans isomer has a rod like structure and low polarity (0 dipole moment), whereas cis one is bent and has a dipole moment of ~3 Debye. Being integrated in the hydrophobic tail of the surfactant molecule, the azobenzene state determines the hydrophobicity of the whole molecule: in the trans state the surfactant is more hydrophobic than in the cis-state. In this way many properties of the surfactant such as the CMC, solubility and the interaction potential with a solid surface can be altered by light. When the solution containing such a surfactant is irradiated with focused light, a concentration gradient of different isomers is formed near the boundary of the irradiated area near the solid surface resulting in osmotic pressure gradient. The generated diffusioosmotic (DO) flow carries the particles passively along.
The local-LDDO flow can be generated around and by each particle when mesoporous silica colloids are dispersed in the surfactant solution. This is because porous particles act as a sink/source which absorbs azobenzene molecule in trans state and expels it when it is in the cis state. The DO flows generated at each particle interact resulting in aggregation or separation depending upon the initial state of surfactant molecules. The kinetic of aggregation and separation can be controlled and manipulated by altering the parameters such as the wavelength and intensity of the applied light, as well as surfactant and particle concentration. Using two wavelengths simultaneously allows for dynamic gathering and separation creating fascinating patterns such as 2D disk of well separated particles or establishing collective complex behaviour of particle ensemble as described in this thesis.
The mechanism of l-LDDO is also used to generate self-propelled motion. This is possible when half of the porous particle is covered by metal layer, basically blocking the pores on one side. The LDDO flow generated on uncapped side pushes the particle forward resulting in a super diffusive motion. The system of porous particle and azobenzene containing surfactant molecule can be utilized for various application such as drug delivery, cargo transportation, self-assembling, micro motors/ machines or micro patterning.
The Earth's inner magnetosphere is a very dynamic system, mostly driven by the external solar wind forcing exerted upon the magnetic field of our planet. Disturbances in the solar wind, such as coronal mass ejections and co-rotating interaction regions, cause geomagnetic storms, which lead to prominent changes in charged particle populations of the inner magnetosphere - the plasmasphere, ring current, and radiation belts. Satellites operating in the regions of elevated energetic and relativistic electron fluxes can be damaged by deep dielectric or surface charging during severe space weather events. Predicting the dynamics of the charged particles and mitigating their effects on the infrastructure is of particular importance, due to our increasing reliance on space technologies.
The dynamics of particles in the plasmasphere, ring current, and radiation belts are strongly coupled by means of collisions and collisionless interactions with electromagnetic fields induced by the motion of charged particles. Multidimensional numerical models simplify the treatment of transport, acceleration, and loss processes of these particles, and allow us to predict how the near-Earth space environment responds to solar storms. The models inevitably rely on a number of simplifications and assumptions that affect model accuracy and complicate the interpretation of the results. In this dissertation, we quantify the processes that control electron dynamics in the inner magnetosphere, paying particular attention to the uncertainties of the employed numerical codes and tools.
We use a set of convenient analytical solutions for advection and diffusion equations to test the accuracy and stability of the four-dimensional Versatile Electron Radiation Belt (VERB-4D) code. We show that numerical schemes implemented in the code converge to the analytical solutions and that the VERB-4D code demonstrates stable behavior independent of the assumed time step. The order of the numerical scheme for the convection equation is demonstrated to affect results of ring current and radiation belt simulations, and it is crucially important to use high-order numerical schemes to decrease numerical errors in the model.
Using the thoroughly tested VERB-4D code, we model the dynamics of the ring current electrons during the 17 March 2013 storm. The discrepancies between the model and observations above 4.5 Earth's radii can be explained by uncertainties in the outer boundary conditions. Simulation results indicate that the electrons were transported from the geostationary orbit towards the Earth by the global-scale electric and magnetic fields.
We investigate how simulation results depend on the input models and parameters. The model is shown to be particularly sensitive to the global electric field and electron lifetimes below 4.5 Earth's radii. The effects of radial diffusion and subauroral polarization streams are also quantified.
We developed a data-assimilative code that blends together a convection model of energetic electron transport and loss and Van Allen Probes satellite data by means of the Kalman filter. We show that the Kalman filter can correct model uncertainties in the convection electric field, electron lifetimes, and boundary conditions. It is also demonstrated how the innovation vector - the difference between observations and model prediction - can be used to identify physical processes missing in the model of energetic electron dynamics.
We computed radial profiles of phase space density of ultrarelativistic electrons, using Van Allen Probes measurements. We analyze the shape of the profiles during geomagnetically quiet and disturbed times and show that the formation of new local minimums in the radial profiles coincides with the ground observations of electromagnetic ion-cyclotron (EMIC) waves. This correlation indicates that EMIC waves are responsible for the loss of ultrarelativistic electrons from the heart of the outer radiation belt into the Earth's atmosphere.
In the current paradigm of cosmology, the formation of large-scale structures is mainly driven by non-radiating dark matter, making up the dominant part of the matter budget of the Universe. Cosmological observations however, rely on the detection of luminous galaxies, which are biased tracers of the underlying dark matter. In this thesis I present cosmological reconstructions of both, the dark matter density field that forms the cosmic web, and cosmic velocities, for which both aspects of my work are delved into, the theoretical formalism and the results of its applications to cosmological simulations and also to a galaxy redshift survey.The foundation of our method is relying on a statistical approach, in which a given galaxy catalogue is interpreted as a biased realization of the underlying dark matter density field. The inference is computationally performed on a mesh grid by sampling from a probability density function, which describes the joint posterior distribution of matter density and the three dimensional velocity field. The statistical background of our method is described in Chapter ”Implementation of argo”, where the introduction in sampling methods is given, paying special attention to Markov Chain Monte-Carlo techniques. In Chapter ”Phase-Space Reconstructions with N-body Simulations”, I introduce and implement a novel biasing scheme to relate the galaxy number density to the underlying dark matter, which I decompose into a deterministic part, described by a non-linear and scale-dependent analytic expression, and a stochastic part, by presenting a negative binomial (NB) likelihood function that models deviations from Poissonity. Both bias components had already been studied theoretically, but were so far never tested in a reconstruction algorithm. I test these new contributions againstN-body simulations to quantify improvements and show that, compared to state-of-the-art methods, the stochastic bias is inevitable at wave numbers of k≥0.15h Mpc^−1 in the power spectrum in order to obtain unbiased results from the reconstructions. In the second part of Chapter ”Phase-Space Reconstructions with N-body Simulations” I describe and validate our approach to infer the three dimensional cosmic velocity field jointly with the dark matter density. I use linear perturbation theory for the large-scale bulk flows and a dispersion term to model virialized galaxy motions, showing that our method is accurately recovering the real-space positions of the redshift-space distorted galaxies. I analyze the results with the isotropic and also the two-dimensional power spectrum.Finally, in Chapter ”Phase-space Reconstructions with Galaxy Redshift Surveys”, I show how I combine all findings and results and apply the method to the CMASS (for Constant (stellar) Mass) galaxy catalogue of the Baryon Oscillation Spectroscopic Survey (BOSS). I describe how our method is accounting for the observational selection effects inside our reconstruction algorithm. Also, I demonstrate that the renormalization of the prior distribution function is mandatory to account for higher order contributions in the structure formation model, and finally a redshift-dependent bias factor is theoretically motivated and implemented into our method. The various refinements yield unbiased results of the dark matter until scales of k≤0.2 h Mpc^−1in the power spectrum and isotropize the galaxy catalogue down to distances of r∼20h^−1 Mpc in the correlation function. We further test the results of our cosmic velocity field reconstruction by comparing them to a synthetic mock galaxy catalogue, finding a strong correlation between the mock and the reconstructed velocities. The applications of both, the density field without redshift-space distortions, and the velocity reconstructions, are very broad and can be used for improved analyses of the baryonic acoustic oscillations, environmental studies of the cosmic web, the kinematic Sunyaev-Zel’dovic or integrated Sachs-Wolfe effect.
The Arctic is the hot spot of the ongoing, global climate change. Over the last decades, near-surface temperatures in the Arctic have been rising almost four times faster than on global average. This amplified warming of the Arctic and the associated rapid changes of its environment are largely influenced by interactions between individual components of the Arctic climate system. On daily to weekly time scales, storms can have major impacts on the Arctic sea-ice cover and are thus an important part of these interactions within the Arctic climate. The sea-ice impacts of storms are related to high wind speeds, which enhance the drift and deformation of sea ice, as well as to changes in the surface energy budget in association with air mass advection, which impact the seasonal sea-ice growth and melt.
The occurrence of storms in the Arctic is typically associated with the passage of transient cyclones. Even though the above described mechanisms how storms/cyclones impact the Arctic sea ice are in principal known, there is a lack of statistical quantification of these effects. In accordance with that, the overarching objective of this thesis is to statistically quantify cyclone impacts on sea-ice concentration (SIC) in the Atlantic Arctic Ocean over the last four decades. In order to further advance the understanding of the related mechanisms, an additional objective is to separate dynamic and thermodynamic cyclone impacts on sea ice and assess their relative importance. Finally, this thesis aims to quantify recent changes in cyclone impacts on SIC. These research objectives are tackled utilizing various data sets, including atmospheric and oceanic reanalysis data as well as a coupled model simulation and a cyclone tracking algorithm.
Results from this thesis demonstrate that cyclones are significantly impacting SIC in the Atlantic Arctic Ocean from autumn to spring, while there are mostly no significant impacts in summer. The strength and the sign (SIC decreasing or SIC increasing) of the cyclone impacts strongly depends on the considered daily time scale and the region of the Atlantic Arctic Ocean. Specifically, an initial decrease in SIC (day -3 to day 0 relative to the cyclone) is found in the Greenland, Barents and Kara Seas, while SIC increases following cyclones (day 0 to day 5 relative to the cyclone) are mostly limited to the Barents and Kara Seas.
For the cold season, this results in a pronounced regional difference between overall (day -3 to day 5 relative to the cyclone) SIC-decreasing cyclone impacts in the Greenland Sea and overall SIC-increasing cyclone impacts in the Barents and Kara Seas. A cyclone case study based on a coupled model simulation indicates that both dynamic and thermodynamic mechanisms contribute to cyclone impacts on sea ice in winter. A typical pattern consisting of an initial dominance of dynamic sea-ice changes followed by enhanced thermodynamic ice growth after the cyclone passage was found. This enhanced ice growth after the cyclone passage most likely also explains the (statistical) overall SIC-increasing effects of cyclones in the Barents and Kara Seas in the cold season.
Significant changes in cyclone impacts on SIC over the last four decades have emerged throughout the year. These recent changes are strongly varying from region to region and month to month. The strongest trends in cyclone impacts on SIC are found in autumn in the Barents and Kara Seas. Here, the magnitude of destructive cyclone impacts on SIC has approximately doubled over the last four decades. The SIC-increasing effects following the cyclone passage have particularly weakened in the Barents Sea in autumn. As a consequence, previously existing overall SIC-increasing cyclone impacts in this region in autumn have recently disappeared. Generally, results from this thesis show that changes in the state of the sea-ice cover (decrease in mean sea-ice concentration and thickness) and near-surface air temperature are most important for changed cyclone impacts on SIC, while changes in cyclone properties (i.e. intensity) do not play a significant role.
Finding millisecond binary pulsars in 47 tucanae by applying the hough transformation to radio data
(2005)
Corvino, Corvino and Schoen, Chruściel and Delay have shown the existence of a large class of asymptotically flat vacuum initial data for Einstein's field equations which are static or stationary in a neighborhood of space-like infinity, yet quite general in the interior. The proof relies on some abstract, non-constructive arguments which makes it difficult to calculate such data numerically by using similar arguments. A quasilinear elliptic system of equations is presented of which we expect that it can be used to construct vacuum initial data which are asymptotically flat, time-reflection symmetric, and asymptotic to static data up to a prescribed order at space-like infinity. A perturbation argument is used to show the existence of solutions. It is valid when the order at which the solutions approach staticity is restricted to a certain range. Difficulties appear when trying to improve this result to show the existence of solutions that are asymptotically static at higher order. The problems arise from the lack of surjectivity of a certain operator. Some tensor decompositions in asymptotically flat manifolds exhibit some of the difficulties encountered above. The Helmholtz decomposition, which plays a role in the preparation of initial data for the Maxwell equations, is discussed as a model problem. A method to circumvent the difficulties that arise when fast decay rates are required is discussed. This is done in a way that opens the possibility to perform numerical computations. The insights from the analysis of the Helmholtz decomposition are applied to the York decomposition, which is related to that part of the quasilinear system which gives rise to the difficulties. For this decomposition analogous results are obtained. It turns out, however, that in this case the presence of symmetries of the underlying metric leads to certain complications. The question, whether the results obtained so far can be used again to show by a perturbation argument the existence of vacuum initial data which approach static solutions at infinity at any given order, thus remains open. The answer requires further analysis and perhaps new methods.
We study buckling instabilities of filaments in biological systems. Filaments in a cell are the building blocks of the cytoskeleton. They are responsible for the mechanical stability of cells and play an important role in intracellular transport by molecular motors, which transport cargo such as organelles along cytoskeletal filaments. Filaments of the cytoskeleton are semiflexible polymers, i.e., their bending energy is comparable to the thermal energy such that they can be viewed as elastic rods on the nanometer scale, which exhibit pronounced thermal fluctuations. Like macroscopic elastic rods, filaments can undergo a mechanical buckling instability under a compressive load. In the first part of the thesis, we study how this buckling instability is affected by the pronounced thermal fluctuations of the filaments. In cells, compressive loads on filaments can be generated by molecular motors. This happens, for example, during cell division in the mitotic spindle. In the second part of the thesis, we investigate how the stochastic nature of such motor-generated forces influences the buckling behavior of filaments. In chapter 2 we review briefly the buckling instability problem of rods on the macroscopic scale and introduce an analytical model for buckling of filaments or elastic rods in two spatial dimensions in the presence of thermal fluctuations. We present an analytical treatment of the buckling instability in the presence of thermal fluctuations based on a renormalization-like procedure in terms of the non-linear sigma model where we integrate out short-wavelength fluctuations in order to obtain an effective theory for the mode of the longest wavelength governing the buckling instability. We calculate the resulting shift of the critical force by fluctuation effects and find that, in two spatial dimensions, thermal fluctuations increase this force. Furthermore, in the buckled state, thermal fluctuations lead to an increase in the mean projected length of the filament in the force direction. As a function of the contour length, the mean projected length exhibits a cusp at the buckling instability, which becomes rounded by thermal fluctuations. Our main result is the observation that a buckled filament is stretched by thermal fluctuations, i.e., its mean projected length in the direction of the applied force increases by thermal fluctuations. Our analytical results are confirmed by Monte Carlo simulations for buckling of semiflexible filaments in two spatial dimensions. We also perform Monte Carlo simulations in higher spatial dimensions and show that the increase in projected length by thermal fluctuations is less pronounced than in two dimensions and strongly depends on the choice of the boundary conditions. In the second part of this work, we present a model for buckling of semiflexible filaments under the action of molecular motors. We investigate a system in which a group of motors moves along a clamped filament carrying a second filament as a cargo. The cargo-filament is pushed against the wall and eventually buckles. The force-generating motors can stochastically unbind and rebind to the filament during the buckling process. We formulate a stochastic model of this system and calculate the mean first passage time for the unbinding of all linking motors which corresponds to the transition back to the unbuckled state of the cargo filament in a mean-field model. Our results show that for sufficiently short microtubules the movement of kinesin-I-motors is affected by the load force generated by the cargo filament. Our predictions could be tested in future experiments.
The H.E.S.S. array is a third generation Imaging Atmospheric Cherenkov Telescope (IACT) array. It is located in the Khomas Highland in Namibia, and measures very high energy (VHE) gamma-rays. In Phase I, the array started data taking in 2004 with its four identical 13 m telescopes. Since then, H.E.S.S. has emerged as the most successful IACT experiment to date. Among the almost 150 sources of VHE gamma-ray radiation found so far, even the oldest detection, the Crab Nebula, keeps surprising the scientific community with unexplained phenomena such as the recently discovered very energetic flares of high energy gamma-ray radiation. During its most recent flare, which was detected by the Fermi satellite in March 2013, the Crab Nebula was simultaneously observed with the H.E.S.S. array for six nights. The results of the observations will be discussed in detail during the course of this work. During the nights of the flare, the new 24 m × 32 m H.E.S.S. II telescope was still being commissioned, but participated in the data taking for one night. To be able to reconstruct and analyze the data of the H.E.S.S. Phase II array, the algorithms and software used by the H.E.S.S. Phase I array had to be adapted. The most prominent advanced shower reconstruction technique developed by de Naurois and Rolland, the template-based model analysis, compares real shower images taken by the Cherenkov telescope cameras with shower templates obtained using a semi-analytical model. To find the best fitting image, and, therefore, the relevant parameters that describe the air shower best, a pixel-wise log-likelihood fit is done. The adaptation of this advanced shower reconstruction technique to the heterogeneous H.E.S.S. Phase II array for stereo events (i.e. air showers seen by at least two telescopes of any kind), its performance using MonteCarlo simulations as well as its application to real data will be described.
Over the past decades, there has been a growing interest in ‘extreme events’ owing to the increasing threats that climate-related extremes such as floods, heatwaves, droughts, etc., pose to society. While extreme events have diverse definitions across various disciplines, ranging from earth science to neuroscience, they are characterized mainly as dynamic occurrences within a limited time frame that impedes the normal functioning of a system. Although extreme events are rare in occurrence, it has been found in various hydro-meteorological and physiological time series (e.g., river flows, temperatures, heartbeat intervals) that they may exhibit recurrent behavior, i.e., do not end the lifetime of the system. The aim of this thesis to develop some
sophisticated methods to study various properties of extreme events.
One of the main challenges in analyzing such extreme event-like time series is that they have large temporal gaps due to the paucity of the number of observations of extreme events. As a result, existing time series analysis tools are usually not helpful to decode the underlying
information. I use the edit distance (ED) method to analyze extreme event-like time series in their unaltered form. ED is a specific distance metric, mainly designed to measure the similarity/dissimilarity between point process-like data. I combine ED with recurrence plot techniques to identify the recurrence property of flood events in the Mississippi River in the United States. I also use recurrence quantification analysis to show the deterministic properties
and serial dependency in flood events.
After that, I use this non-linear similarity measure (ED) to compute the pairwise dependency in extreme precipitation event series. I incorporate the similarity measure within the framework of complex network theory to study the collective behavior of climate extremes. Under this architecture, the nodes are defined by the spatial grid points of the given spatio-temporal climate dataset. Each node is associated with a time series corresponding to the temporal evolution
of the climate observation at that grid point. Finally, the network links are functions of the pairwise statistical interdependence between the nodes. Various network measures, such as degree, betweenness centrality, clustering coefficient, etc., can be used to quantify the network’s topology. We apply the methodology mentioned above to study the spatio-temporal coherence pattern of extreme rainfall events in the United States and the Ganga River basin, which reveals its relation to various climate processes and the orography of the region.
The identification of precursors associated with the occurrence of extreme events in the near future is extremely important to prepare the masses for an upcoming disaster and mitigate the potential risks associated with such events. Under this motivation, I propose an in-data prediction recipe for predicting the data structures that typically occur prior to extreme events using the Echo state network, a type of Recurrent Neural Network which is a part of the reservoir
computing framework. However, unlike previous works that identify precursory structures in the same variable in which extreme events are manifested (active variable), I try to predict these structures by using data from another dynamic variable (passive variable) which does not show large excursions from the nominal condition but carries imprints of these extreme events. Furthermore, my results demonstrate that the quality of prediction depends on the magnitude
of events, i.e., the higher the magnitude of the extreme, the better is its predictability skill. I show quantitatively that this is because the input signals collectively form a more coherent pattern for an extreme event of higher magnitude, which enhances the efficiency of the machine to predict the forthcoming extreme events.
Classical semiconductor physics has been continuously improving electronic components such as diodes, light-emitting diodes, solar cells and transistors based on highly purified inorganic crystals over the past decades. Organic semiconductors, notably polymeric, are a comparatively young field of research, the first light-emitting diode based on conjugated polymers having been demonstrated in 1990. Polymeric semiconductors are of tremendous interest for high-volume, low-cost manufacturing ("printed electronics"). Due to their rather simple device structure mostly comprising only one or two functional layers, polymeric diodes are much more difficult to optimize compared to small-molecular organic devices. Usually, functions such as charge injection and transport are handled by the same material which thus needs to be highly optimized. The present work contributes to expanding the knowledge on the physical mechanisms determining device performance by analyzing the role of charge injection and transport on device efficiency for blue and white-emitting devices, based on commercially relevant spiro-linked polyfluorene derivatives. It is shown that such polymers can act as very efficient electron conductors and that interface effects such as charge trapping play the key role in determining the overall device efficiency. This work contributes to the knowledge of how charges drift through the polymer layer to finally find neutral emissive trap states and thus allows a quantitative prediction of the emission color of multichromophoric systems, compatible with the observed color shifts upon driving voltage and temperature variation as well as with electrical conditioning effects. In a more methodically oriented part, it is demonstrated that the transient device emission observed upon terminating the driving voltage can be used to monitor the decay of geminately-bound species as well as to determine trapped charge densities. This enables direct comparisons with numerical simulations based on the known properties of charge injection, transport and recombination. The method of charge extraction under linear increasing voltages (CELIV) is investigated in some detail, correcting for errors in the published approach and highlighting the role of non-idealized conditions typically present in experiments. An improved method is suggested to determine the field dependence of charge mobility in a more accurate way. Finally, it is shown that the neglect of charge recombination has led to a misunderstanding of experimental results in terms of a time-dependent mobility relaxation.
Actin-based directional motility is important for embryonic development, wound healing, immune responses, and development of tissues. Actin and myosin are essential players in this process that can be subdivided into protrusion, adhesion, and traction. Protrusion is the forward movement of the membrane at the leading edge of the cell. Adhesion is required to enable movement along a substrate, and traction finally leads to the forward movement of the entire cell body, including its organelles. While actin polymerization is the main driving force in cell protrusions, myosin motors lead to the contraction of the cell body. The goal of this work was to study the regulatory mechanisms of the motile machinery by selecting a representative key player for each stage of the signaling process: the regulation of Arp2/3 activity by WASP (actin system), the role of cGMP in myosin II assembly (myosin system), and the influence of phosphoinositide signaling (upstream receptor pathway). The model organism chosen for this work was the social ameba Dictyostelium discoideum, due to the well-established knowledge of its cytoskeletal machinery, the easy handling, and the high motility of its vegetative and starvation developed cells. First, I focused on the dynamics of the actin cytoskeleton by modulating the activity of one of its key players, the Arp2/3 complex. This was achieved using the carbazole derivative Wiskostatin, an inhibitor of the Arp2/3 activator WASP. Cells treated with Wiskostatin adopted a round shape, with no of few pseudopodia. With the help of a microfluidic cell squeezer device, I could show that Wiskostatin treated cells display a reduced mechanical stability, comparable to cells treated with the actin disrupting agent Latrunculin A. Furthermore, the WASP inhibited cells adhere stronger to a surface and show a reduced motility and chemotactic performance. However, the overall F-actin content in the cells was not changed. Confocal microscopy and TIRF microscopy imaging showed that the cells maintained an intact actin cortex. Localized dynamic patches of increased actin polymerization were observed that, however, did not lead to membrane deformation. This indicated that the mechanisms of actin-driven force generation were impaired in Wiskostatin treated cells. It is concluded that in these cells, an altered architecture of the cortical network leads to a reduced overall stiffness of the cell, which is insufficient to support the force generation required for membrane deformation and pseudopod formation. Second, the role of cGMP in myosin II dynamics was investigated. Cyclic GMP is known to regulate the association of myosin II with the cytoskeleton. In Dictyostelium, intracellular cGMP levels increase when cells are exposed to chemoattractants, but also in response to osmotic stress. To study the influence of cyclic GMP on actin and myosin II dynamics, I used the laser-induced photoactivation of a DMACM-caged-Br-cGMP to locally release cGMP inside the cell. My results show that cGMP directly activates the myosin II machinery, but is also able to induce an actin response independently of cAMP receptor activation and signaling. The actin response was observed in both vegetative and developed cells. Possible explanations include cGMP-induced actin polymerization through VASP (vasodilator-stimulated phosphoprotein) or through binding of cGMP to cyclic nucleotide-dependent kinases. Finally, I investigated the role of phosphoinositide signaling using the Polyphosphoinositide-Binding Peptide (PBP10) that binds preferentially to PIP2. Phosphoinositides can recruit actin-binding proteins to defined subcellular sites and alter their activity. Neutrophils, as well as developed Dictyostelium cells produce PIP3 in the plasma membrane at their leading edge in response to an external chemotactic gradient. Although not essential for chemotaxis, phosphoinositides are proposed to act as an internal compass in the cell. When treated with the peptide PBP10, cells became round, with fewer or no pseudopods. PH-CRAC translocation to the membrane still occurs, even at low cAMP stimuli, but cell motility (random and directional) was reduced. My data revealed that the decrease in the pool of available PIP2 in the cell is sufficient to impair cell motility, but enough PIP2 remains so that PIP3 is formed in response to chemoattractant stimuli. My data thus highlights how sensitive cell motility and morphology are to changes in the phosphoinositide signaling. In summary, I have analyzed representative regulatory mechanisms that govern key parts of the motile machinery and characterized their impact on cellular properties including mechanical stability, adhesion and chemotaxis.
Seasonal forecasts are of great interest in many areas. Knowing the amount of precipitation for the upcoming season in regions of water scarcity would facilitate a better water management. If farmers knew the weather conditions of the upcoming summer at sowing time, they could select those cereal species that are best adapted to these conditions. This would allow farmers to improve the harvest and potentially even reduce the amount of pesticides used. However, the undoubted advantages of seasonal forecasts are often opposed by their high degree of uncertainty. The great challenge of generating seasonal forecasts with lead times of several months mainly originates from the chaotic nature of the earth system. In a chaotic system, even tiny differences in the initial conditions can lead to strong deviations in the system’s state in the long run.
In this dissertation we propose an emergent machine learning approach for seasonal forecasting, called the AnlgModel. The AnlgModel combines the analogue method with myopic feature selection and bootstrapping. To benchmark the abilities of the AnlgModel we apply it to seasonal cyclone activity forecasts in the North Atlantic and Northwest Pacific. The AnlgModel demonstrates competitive hindcast skills with two operational forecasts and even outperforms these for long lead times.
In the second chapter we comprehend the forecasting strategy of the Anlg-Model. We thereby analyse the analogue selection process for the 2017 North Atlantic and the 2018 Northwest Pacific seasonal cyclone activity. The analysis shows that those climate indices which are known to influence the seasonal cyclone activity, such as the Niño 3.4 SST, are correctly represented among the selected analogues. Furthermore the selected analogues reflect large-scale climate patterns that were identified by expert reports as being determinative for these particular seasons.
In the third chapter we analyse the features that are used by the AnlgModel for its predictions. We therefore inspect the feature relevance (FR). The FR patterns learned by the AnlgModel show a high congruence with the predictor regions used by the operational forecasts. However, the AnlgModel also discovered new features, such as the SST anomaly in the Gulf of Guinea during November. This SST pattern exhibits a remarkably high predictive potential for the upcoming Atlantic hurricane activity.
In the final chapter we investigate potential mechanisms, that link two of these regions with high feature relevance to the Atlantic hurricane activity. We mainly focus on ocean surface transport. The ocean surface flow paths are calculated using Lagrangian particle analysis. We demonstrate that the FR patterns in the region of the Canary islands do not correspond with ocean surface transport. It is instead likely that these FR patterns fingerprint a wind transport of latent heat. The second region to be studied is situated in the Gulf of Guinea. Our analysis shows that the FR patterns seen there do fingerprint ocean surface transport. However, our simulations also show that at least one other mechanism is involved in linking the Gulf of Guinea SST anomaly in November to the hurricane activity of the upcoming season.
In this work the AnlgModel does not only demonstrate its outstanding forecast skills but also shows its capabilities as research tool for detecting oceanic and atmospheric mechanisms.
The work done during the PhD studies has been focused on measurements of distribution functions of rotating galaxies using integral field spectroscopy observations.
Throughout the main body of research presented here we have been using CALIFA (Calar Alto Legacy Integral Field Area) survey stellar velocity fields to obtain robust measurements of circular velocities for rotating galaxies of all morphological types. A crucial part of the work was enabled by well-defined CALIFA sample selection criteria: it enabled reconstructing sample-independent distributions of galaxy properties.
In Chapter 2, we measure the distribution in absolute magnitude - circular velocity space for a well-defined sample of 199 rotating CALIFA galaxies using their stellar kinematics. Our aim in this analysis is to avoid subjective selection criteria and to take volume and large-scale structure factors into account. Using stellar velocity fields instead of gas emission line kinematics allows including rapidly rotating early type galaxies. Our initial sample contains 277 galaxies with available stellar velocity fields and growth curve r-band photometry. After rejecting 51 velocity fields that could not be modelled due to the low number of bins, foreground contamination or significant interaction we perform Markov Chain Monte Carlo (MCMC) modelling of the velocity fields, obtaining the rotation curve and kinematic parameters and their realistic uncertainties. We perform an extinction correction and calculate the circular velocity v_circ accounting for pressure support a given galaxy has. The resulting galaxy distribution on the M_r - v_circ plane is then modelled as a mixture of two distinct populations, allowing robust and reproducible rejection of outliers, a significant fraction of which are slow rotators. The selection effects are understood well enough that the incompleteness of the sample can be corrected and the 199 galaxies can be weighted by volume and large-scale structure factors enabling us to fit a volume-corrected Tully-Fisher relation (TFR). More importantly, we also provide the volume-corrected distribution of galaxies in the M_r - v_circ plane, which can be compared with cosmological simulations. The joint distribution of the luminosity and circular velocity space densities, representative over the range of -20 > M_r > -22 mag, can place more stringent constraints on the galaxy formation and evolution scenarios than linear TFR fit parameters or the luminosity function alone.
In Chapter 3, we measure one of the marginal distributions of the M_r - v_circ distribution: the circular velocity function of rotating galaxies. The velocity function is a fundamental observable statistic of the galaxy population, being of a similar importance as the luminosity function, but much more difficult to measure. We present the first directly measured circular velocity function that is representative between 60 < v_circ < 320 km s^-1 for galaxies of all morphological types at a given rotation velocity. For the low mass galaxy population 60 < v_circ < 170 km s^-1, we use the HIPASS velocity function. For the massive galaxy population 170 < v_circ < 320 km s^-1, we use stellar circular velocities from CALIFA. The CALIFA velocity function includes homogeneous velocity measurements of both late and early-type rotation-supported galaxies. It has the crucial advantage of not missing gas-poor massive ellipticals that HI surveys are blind to. We show that both velocity functions can be combined in a seamless manner, as their ranges of validity overlap. The resulting observed velocity function is compared to velocity functions derived from cosmological simulations of the z = 0 galaxy population. We find that dark matter-only simulations show a strong mismatch with the observed VF. Hydrodynamic Illustris simulations fare better, but still do not fully reproduce observations.
In Chapter 4, we present some other work done during the PhD studies, namely, a method that improves the precision of specific angular measurements by combining simultaneous Markov Chain Monte Carlo modelling of ionised gas 2D velocity fields and HI linewidths. To test the method we use a sample of 25 galaxies from the Sydney-AAO Multi-object Integral field (SAMI) survey that had matching ALFALFA HI linewidths. Such a method allows constraining the rotation curve both in the inner regions of a galaxy and in its outskirts, leading to increased precision of specific angular momentum measurements. It could be used to further constrain the observed relation between galaxy mass, specific angular momentum and morphology (Obreschkow & Glazebrook 2014).
Mathematical and computational methods are presented in the appendices.
We do magnetohydrodynamic (MHD) simulations of local box models of turbulent Interstellar Medium (ISM) and analyse the process of amplification and saturation of mean magnetic fields with methods of mean field dynamo theory. It is shown that the process of saturation of mean fields can be partially described by the prolonged diffusion time scales in presence of the dynamically significant magnetic fields. However, the outward wind also plays an essential role in the saturation in higher SN rate case. Algebraic expressions for the back reaction of the magnetic field onto the turbulent transport coefficients are derived, which allow a complete description of the nonlinear dynamo. We also present the effects of dynamically significant mean fields on the ISM configuration and pressure distribution. We further add the cosmic ray component in the simulations and investigate the kinematic growth of mean fields with a dynamo perspective.
Cargo transport by molecular motors is ubiquitous in all eukaryotic cells and is typically driven cooperatively by several molecular motors, which may belong to one or several motor species like kinesin, dynein or myosin. These motor proteins transport cargos such as RNAs, protein complexes or organelles along filaments, from which they unbind after a finite run length. Understanding how these motors interact and how their movements are coordinated and regulated is a central and challenging problem in studies of intracellular transport. In this thesis, we describe a general theoretical framework for the analysis of such transport processes, which enables us to explain the behavior of intracellular cargos based on the transport properties of individual motors and their interactions. Motivated by recent in vitro experiments, we address two different modes of transport: unidirectional transport by two identical motors and cooperative transport by actively walking and passively diffusing motors. The case of cargo transport by two identical motors involves an elastic coupling between the motors that can reduce the motors’ velocity and/or the binding time to the filament. We show that this elastic coupling leads, in general, to four distinct transport regimes. In addition to a weak coupling regime, kinesin and dynein motors are found to exhibit a strong coupling and an enhanced unbinding regime, whereas myosin motors are predicted to attain a reduced velocity regime. All of these regimes, which we derive both by analytical calculations and by general time scale arguments, can be explored experimentally by varying the elastic coupling strength. In addition, using the time scale arguments, we explain why previous studies came to different conclusions about the effect and relevance of motor-motor interference. In this way, our theory provides a general and unifying framework for understanding the dynamical behavior of two elastically coupled molecular motors. The second mode of transport studied in this thesis is cargo transport by actively pulling and passively diffusing motors. Although these passive motors do not participate in active transport, they strongly enhance the overall cargo run length. When an active motor unbinds, the cargo is still tethered to the filament by the passive motors, giving the unbound motor the chance to rebind and continue its active walk. We develop a stochastic description for such cooperative behavior and explicitly derive the enhanced run length for a cargo transported by one actively pulling and one passively diffusing motor. We generalize our description to the case of several pulling and diffusing motors and find an exponential increase of the run length with the number of involved motors.
In the present work synchronization phenomena in complex dynamical systems exhibiting multiple time scales have been analyzed. Multiple time scales can be active in different manners. Three different systems have been analyzed with different methods from data analysis. The first system studied is a large heterogenous network of bursting neurons, that is a system with two predominant time scales, the fast firing of action potentials (spikes) and the burst of repetitive spikes followed by a quiescent phase. This system has been integrated numerically and analyzed with methods based on recurrence in phase space. An interesting result are the different transitions to synchrony found in the two distinct time scales. Moreover, an anomalous synchronization effect can be observed in the fast time scale, i.e. there is range of the coupling strength where desynchronization occurs. The second system analyzed, numerically as well as experimentally, is a pair of coupled CO₂ lasers in a chaotic bursting regime. This system is interesting due to its similarity with epidemic models. We explain the bursts by different time scales generated from unstable periodic orbits embedded in the chaotic attractor and perform a synchronization analysis of these different orbits utilizing the continuous wavelet transform. We find a diverse route to synchrony of these different observed time scales. The last system studied is a small network motif of limit cycle oscillators. Precisely, we have studied a hub motif, which serves as elementary building block for scale-free networks, a type of network found in many real world applications. These hubs are of special importance for communication and information transfer in complex networks. Here, a detailed study on the mechanism of synchronization in oscillatory networks with a broad frequency distribution has been carried out. In particular, we find a remote synchronization of nodes in the network which are not directly coupled. We also explain the responsible mechanism and its limitations and constraints. Further we derive an analytic expression for it and show that information transmission in pure phase oscillators, such as the Kuramoto type, is limited. In addition to the numerical and analytic analysis an experiment consisting of electrical circuits has been designed. The obtained results confirm the former findings.
In the living cell, the organization of the complex internal structure relies to a large extent on molecular motors. Molecular motors are proteins that are able to convert chemical energy from the hydrolysis of adenosine triphosphate (ATP) into mechanical work. Being about 10 to 100 nanometers in size, the molecules act on a length scale, for which thermal collisions have a considerable impact onto their motion. In this way, they constitute paradigmatic examples of thermodynamic machines out of equilibrium. This study develops a theoretical description for the energy conversion by the molecular motor myosin V, using many different aspects of theoretical physics. Myosin V has been studied extensively in both bulk and single molecule experiments. Its stepping velocity has been characterized as a function of external control parameters such as nucleotide concentration and applied forces. In addition, numerous kinetic rates involved in the enzymatic reaction of the molecule have been determined. For forces that exceed the stall force of the motor, myosin V exhibits a 'ratcheting' behaviour: For loads in the direction of forward stepping, the velocity depends on the concentration of ATP, while for backward loads there is no such influence. Based on the chemical states of the motor, we construct a general network theory that incorporates experimental observations about the stepping behaviour of myosin V. The motor's motion is captured through the network description supplemented by a Markov process to describe the motor dynamics. This approach has the advantage of directly addressing the chemical kinetics of the molecule, and treating the mechanical and chemical processes on equal grounds. We utilize constraints arising from nonequilibrium thermodynamics to determine motor parameters and demonstrate that the motor behaviour is governed by several chemomechanical motor cycles. In addition, we investigate the functional dependence of stepping rates on force by deducing the motor's response to external loads via an appropriate Fokker-Planck equation. For substall forces, the dominant pathway of the motor network is profoundly different from the one for superstall forces, which leads to a stepping behaviour that is in agreement with the experimental observations. The extension of our analysis to Markov processes with absorbing boundaries allows for the calculation of the motor's dwell time distributions. These reveal aspects of the coordination of the motor's heads and contain direct information about the backsteps of the motor. Our theory provides a unified description for the myosin V motor as studied in single motor experiments.
Adherent cells constantly collect information about the mechanical properties of their extracellular environment by actively pulling on it through cell-matrix contacts, which act as mechanosensors. In recent years, the sophisticated use of elastic substrates has shown that cells respond very sensitively to changes in effective stiffness in their environment, which results in a reorganization of the cytoskeleton in response to mechanical input. We develop a theoretical model to predict cellular self-organization in soft materials on a coarse grained level. Although cell organization in principle results from complex regulatory events inside the cell, the typical response to mechanical input seems to be a simple preference for large effective stiffness, possibly because force is more efficiently generated in a stiffer environment. The term effective stiffness comprises effects of both rigidity and prestrain in the environment. This observation can be turned into an optimization principle in elasticity theory. By specifying the cellular probing force pattern and by modeling the environment as a linear elastic medium, one can predict preferred cell orientation and position. Various examples for cell organization, which are of large practical interest, are considered theoretically: cells in external strain fields and cells close to boundaries or interfaces for different sample geometries and boundary conditions. For this purpose the elastic equations are solved exactly for an infinite space, an elastic half space and the elastic sphere. The predictions of the model are in excellent agreement with experiments for fibroblast cells, both on elastic substrates and in hydrogels. Mechanically active cells like fibroblasts could also interact elastically with each other. We calculate the optimal structures on elastic substrates as a function of material properties, cell density and the geometry of cell positioning, respectively, that allows each cell to maximize the effective stiffness in its environment due to the traction of all the other cells. Finally, we apply Monte Carlo simulations to study the effect of noise on cellular structure formation. The model not only contributes to a better understanding of many physiological situations. In the future it could also be used for biomedical applications to optimize protocols for artificial tissues with respect to sample geometry, boundary condition, material properties or cell density.
Semi-empirical sea-level models (SEMs) exploit physically motivated empirical relationships between global sea level and certain drivers, in the following global mean temperature. This model class evolved as a supplement to process-based models (Rahmstorf (2007)) which were unable to fully represent all relevant processes. They thus failed to capture past sea-level change (Rahmstorf et al. (2012)) and were thought likely to underestimate future sea-level rise. Semi-empirical models were found to be a fast and useful tool for exploring the uncertainties in future sea-level rise, consistently giving significantly higher projections than process-based models.
In the following different aspects of semi-empirical sea-level modelling have been studied. Models were first validated using various data sets of global sea level and temperature. SEMs were then used on the glacier contribution to sea level, and to infer past global temperature from sea-level data via inverse modelling. Periods studied encompass the instrumental period, covered by tide gauges (starting 1700 CE (Common Era) in Amsterdam) and satellites (first launched in 1992 CE), the era from 1000 BCE (before CE) to present, and the full length of the Holocene (using proxy data). Accordingly different data, model formulations and implementations have been used. It could be shown in Bittermann et al. (2013) that SEMs correctly predict 20th century sea-level when calibrated with data until 1900 CE. SEMs also turned out to give better predictions than the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report (AR4, IPCC (2007)) models, for the period from 1961–2003 CE.
With the first multi-proxy reconstruction of global sea-level as input, estimate of the human-induced component of modern sea-level change and projections of future sea-level rise were calculated (Kopp et al. (2016)). It turned out with 90% confidence that more than 40 % of the observed 20th century sea-level rise is indeed anthropogenic. With the new semi-empirical and IPCC (2013) 5th assessment report (AR5) projections the gap between SEM and process-based model projections closes, giving higher credibility to both. Combining all scenarios, from strong mitigation to business as usual, a global sea-level rise of 28–131 cm relative to 2000 CE, is projected with 90% confidence. The decision for a low carbon pathway could halve the expected global sea-level rise by 2100 CE.
Present day temperature and thus sea level are driven by the globally acting greenhouse-gas forcing. Unlike that, the Milankovich forcing, acting on Holocene timescales, results mainly in a northern-hemisphere temperature change. Therefore a semi-empirical model can be driven with northernhemisphere temperatures, which makes it possible to model the main subcomponent of sea-level change over this period. It showed that an additional positive constant rate of the order of the estimated Antarctic sea-level contribution is then required to explain the sea-level evolution over the Holocene. Thus the global sea level, following the climatic optimum, can be interpreted as the sum of a temperature induced sea-level drop and a positive long-term contribution, likely an ongoing response to deglaciation coming from Antarctica.
The present thesis was born and evolved within the RAdial Velocity Experiment (RAVE) with the goal of measuring chemical abundances from the RAVE spectra and exploit them to investigate the chemical gradients along the plane of the Galaxy to provide constraints on possible Galactic formation scenarios. RAVE is a large spectroscopic survey which aims to observe spectroscopically ~10^6 stars by the end of 2012 and measures their radial velocities, atmospheric parameters and chemical abundances. The project makes use of the UK Schmidt telescope at Australian Astronomical Observatory (AAO) in Siding Spring, Australia, equipped with the multiobject spectrograph 6dF. To date, RAVE collected and measured more than 450,000 spectra. The precision of the chemical abundance estimations depends on the reliability of the atomic and atmosphere parameters adopted (in particular the oscillator strengths of the absorption lines and the effective temperature, gravity, and metallicity of the stars measured). Therefore we first identified 604 absorption lines in the RAVE wavelength range and refined their oscillator strengths with an inverse spectral analysis. Then, we improved the RAVE stellar parameters by modifying the RAVE pipeline and the spectral library the pipeline rely on. The modifications removed some systematic errors in stellar parameters discovered during this work. To obtain chemical abundances, we developed two different processing pipelines. Both of them perform chemical abundances measurements by assuming stellar atmospheres in Local Thermodynamic Equilibrium (LTE). The first one determines elements abundances from equivalent widths of absorption lines. Since this pipeline showed poor sensibility on abundances relative to iron, it has been superseded. The second one exploits the chi^2 minimization technique between observed and model spectra. Thanks to its precision, it has been adopted for the creation of the RAVE chemical catalogue. This pipeline provides abundances with uncertains of about ~0.2dex for spectra with signal-to-noise ratio S/N>40 and ~0.3dex for spectra with 20>S/N>40. For this work, the pipeline measured chemical abundances up to 7 elements for 217,358 RAVE stars. With these data we investigated the chemical gradients along the Galactic radius of the Milky Way. We found that stars with low vertical velocities |W| (which stay close to the Galactic plane) show an iron abundance gradient in agreement with previous works (~-0.07$ dex kpc^-1) whereas stars with larger |W| which are able to reach larger heights above the Galactic plane, show progressively flatter gradients. The gradients of the other elements follow the same trend. This suggests that an efficient radial mixing acts in the Galaxy or that the thick disk formed from homogeneous interstellar matter. In particular, we found hundreds of stars which can be kinetically classified as thick disk stars exhibiting a chemical composition typical of the thin disk. A few stars of this kind have already been detected by other authors, and their origin is still not clear. One possibility is that they are thin disk stars kinematically heated, and then underwent an efficient radial mixing process which blurred (and so flattened) the gradient. Alternatively they may be a transition population" which represents an evolutionary bridge between thin and thick disk. Our analysis shows that the two explanations are not mutually exclusive. Future follow-up high resolution spectroscopic observations will clarify their role in the Galactic disk evolution.
The problem under consideration in the thesis is a two level atom in a photonic crystal and a pumping laser. The photonic crystal provides an environment for the atom, that modifies the decay of the exited state, especially if the atom frequency is close to the band gap. The population inversion is investigated als well as the emission spectrum. The dynamics is analysed in the context of open quantum systems. Due to the multiple reflections in the photonic crystal, the system has a finite memory that inhibits the Markovian approximation. In the Heisenberg picture the equations of motion for the system variables form a infinite hierarchy of integro-differential equations. To get a closed system, approximations like a weak coupling approximation are needed. The thesis starts with a simple photonic crystal that is amenable to analytic calculations: a one-dimensional photonic crystal, that consists of alternating layers. The Bloch modes inside and the vacuum modes outside a finite crystal are linked with a transformation matrix that is interpreted as a transfer matrix. Formulas for the band structure, the reflection from a semi-infinite crystal, and the local density of states in absorbing crystals are found; defect modes and negative refraction are discussed. The quantum optics section of the work starts with the discussion of three problems, that are related to the full resonance fluorescence problem: a pure dephasing model, the driven atom and resonance fluorescence in free space. In the lowest order of the system-environment coupling, the one-time expectation values for the full problem are calculated analytically and the stationary states are discussed for certain cases. For the calculation of the two time correlation functions and spectra, the additional problem of correlations between the two times appears. In the Markovian case, the quantum regression theorem is valid. In the general case, the fluctuation dissipation theorem can be used instead. The two-time correlation functions are calculated by the two different methods. Within the chosen approximations, both methods deliver the same result. Several plots show the dependence of the spectrum on the parameters. Some examples for squeezing spectra are shown with different approximations. A projection operator method is used to establish two kinds of Markovian expansion with and without time convolution. The lowest order is identical with the lowest order of system environment coupling, but higher orders give different results.