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Kernpunkt dieser Arbeit ist die Untersuchung der Eigenschaften des Vakuums und des Grundzustandes von Kernmaterie anhand eines effektiven Modells. Das Lineare Sigma-Modell mit globaler chiraler U(2)R ×U(2)L-Symmetrie wurde mit (Axial-)Vektormesonen sowie dem chiralen Partner des Nukleons, der mit der Resonanz N(1535) identifiziert wird, erweitert. Die Einführung des chiralen Partners in der Spiegel-Zuordnung ermöglicht die Untersuchung zweier verschiedener Erzeugungsprozesse der Baryonenmasse: durch spontane Symmetriebrechung sowie durch einen chiral invarianten Massenterm, parametrisiert durch m0. Die Parameter des Modells werden durch experimentelle Werte der Zerfallsbreiten von N∗ → Nπ und a1 → πγ und der axialen Kopplungskonstante des Nukleons gN A , sowie durch Lattice-Berechnungen von gN∗ A fixiert. Im Rahmen dieses Modells ergibt sich für den Massenparameter m0 ∼ 500 MeV, was darauf hin deutet, dass ein beträchtlicher Anteil der Baryonenmasse nicht durch das chirale Kondensat erzeugt wird. Das Modell wird anhand des Zerfalls N∗ → Nη sowie s-Wellen-πN-Streulängen a(±) 0 validiert und zeigt gute Übereinstimmung mit dem Experiment. In Kernmaterie wird m0 durch Kondensate anderer skalarer Felder ausgedrückt, z. B. dem Tetraquark-Kondensat. Der Einfluß dieses Kondensates auf dichte Materie wird untersucht. Die Nukleonenmassen hängen stark von den Kondensaten ab und verschwinden, so wie auch die Kondensate selbst, wenn die chirale Symmetrie wieder hergestellt ist.
The HADES (High Acceptance DiElectron Spectrometer) is an experimental
apparatus installed at the heavy-ion synchrotron SIS-18 at GSI, Darmstadt.
The main physics motivation of the HADES experiment is the measurement
of e+e− pairs in the invariant-mass range up to 1 GeV/c2 in heavy-ion collisions
as well as in pion and proton-induced reactions.
The HADES physics program is focused on in-medium properties of the light
vector mesons ρ(770), ω(783) and φ(1020), which decay with a small branching
ratio into dileptons. Dileptons are penetrating probes which allow to study
the in-medium properties of hadrons. However, in heavy-ion collisions, the
measurement of such lepton pairs is difficult because they are rare and have a
very large combinatorial background.
Recently, HADES has been upgraded with new detectors and new electronics
in order to handle higher intensity beams and reactions with heavy nuclei up
to Au.
HADES will continue for a few more years its rich physics program at its
current place at SIS-18 and then move to the upcoming international Facility
for Antiproton and Ion Research (FAIR) accelerator complex. In this context
the physics results presented in this work are important prerequisites for the investigation
of in-medium vector meson properties in p + A and A+A collisions.
This work consists of five chapters. The first chapter introduces the physics
motivation and a review of recent physics results. In the second chapter, the
HADES spectrometer is described and its sub-detectors are presented. Chapter
three deals with the issue of lepton identification and the reconstruction of
the dielectron spectra in p + p collisions is presented. Here, two reactions
are characterized: inclusive and exclusive dilepton production reactions. From
the spectra obtained, the corresponding cross sections are presented with the
respective statistical and systematical errors. A comparison with theoretical
models is included as well. Conclusions are given in chapter four.
The final part of this work is dedicated to the HADES upgrade, whose goal
is among others the achievement of a reliable and fast data acquisition of the
Multiwire Drift Chambers (MDCs). Chapter five presents my contribution to
this successful project during the three years of my stay at GSI.
In nature, society and technology many disordered systems exist, that show emergent behaviour, where the interactions of numerous microscopic agents result in macroscopic, systemic properties, that may not be present on the microscopic scale. Examples include phase transitions in magnetism and percolation, for example in porous unordered media, biological, and social systems. Also technological systems that are explicitly designed to function without central control instances, like their prime example the Internet, or virtual networks, like the World Wide Web, which is defined by the hyperlinks from one web page to another, exhibit emergent properties. The study of the common network characteristics found in previously seemingly unrelated fields of science and the urge to explain their emergence, form a scientific field in its own right, the science of complex networks. In this field, methodologies from physics, leading to simplification and generalization by abstraction, help to shift the focus from the implementation's details on the microscopic level to the macroscopic, coarse grained system level. By describing the macroscopic properties that emerge from microscopic interactions, statistical physics, in particular stochastic and computational methods, has proven to be a valuable tool in the investigation of such systems. The mathematical framework for the description of networks is graph theory, in hindsight founded by Euler in 1736 and an active area of research since then. In recent years, applied graph theory flourished through the advent of large scale data sets, made accessible by the use of computers. A paradigm for microscopic interactions among entities that locally optimize their behaviour to increase their own benefit is game theory, the mathematical framework of decision finding. With first applications in economics e.g. Neumann (1944), game theory is an approved field of mathematics. However, game theoretic behaviour is also found in natural systems, e.g. populations of the bacterium Escherichia coli, as described by Kerr (2002). In the present work, a combination of graph theory and game theory is used to model the interactions of selfish agents that form networks. Following brief introductions to graph theory and game theory, the present work approaches the interplay of local self-organizing rules with network properties and topology from three perspectives. To investigate the dynamics of topology reshaping, coupling of the so called iterated prisoners' dilemma (IPD) to the network structure is proposed and studied in Chapter 4. In dependence of a free parameter in the payoff matrix, the reorganization dynamics result in various emergent network structures. The resulting topologies exhibit an increase in performance, measured by a variance of closeness, of a factor 1.2 to 1.9, depending in the chosen free parameter. Presented in Chapter 5, the second approach puts the focus on a static network structure and studies the cooperativity of the system, measured by the fixation probability. Heterogeneous strategies to distribute incentives for cooperation among the players are proposed. These strategies allow to enhance the cooperative behaviour, while requiring fewer total investments. Putting the emphasis on communication networks in Chapters 6 and 7, the third approach investigates the use of routing metrics to increase the performance of data packet transport networks. Algorithms for the iterative determination of such metrics are demonstrated and investigated. The most successful of these algorithms, the hybrid metric, is able to increase the throughput capacity of a network by a factor of 7. During the investigation of the iterative weight assignments a simple, static weight assignment, the so called logKiKj metric, is found. In contrast to the algorithmic metrics, it results in vanishing computational costs, yet it is able to increase the performance by a factor of 5.
The aim of this work is to develop an effective equation of state for QCD, having the correct asymptotic degrees of freedom, to be used as input for dynamical studies of heavy ion collisions. We present an approach for modeling an EoS that respects the symmetries underlying QCD, and includes the correct asymptotic degrees of freedom, i.e. quarks and gluons at high temperature and hadrons in the low-temperature limit. We achieve this by including quarks degrees of freedom and the thermal contribution of the Polyakov loop in a hadronic chiral sigma-omega model. The hadronic part of the model is a nonlinear realization of an sigma-omega model. As the fundamental symmetries of QCD should also be present in its hadronic states such an approach is widely used to describe hadron properties below and around Tc. The quarks are introduced as thermal quasi particles, coupling to the Polyakov loop, while the dynamics of the Polyakov loop are controlled by a potential term which is fitted to reproduce pure gauge lattice data. In this model the sigma field serves a the order parameter for chiral restoration and the Polyakov loop as order parameter for deconfinement. The hadrons are suppressed at high densities by excluded volume corrections. As a next step, we introduce our new HQ model equation of state in a microscopic+macroscopic hybrid approach to heavy ion collisions. This hybrid approach is based on the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) transport approach with an intermediate hydrodynamical evolution for the hot and dense stage of the collision. The present implementation allows to compare pure microscopic transport calculations with hydrodynamic calculations using exactly the same initial conditions and freeze-out procedure. The effects of the change in the underlying dynamics - ideal fluid dynamics vs. non-equilibrium transport theory - are explored. The final pion and proton multiplicities are lower in the hybrid model calculation due to the isentropic hydrodynamic expansion while the yields for strange particles are enhanced due to the local equilibrium in the hydrodynamic evolution. The elliptic and directed flow are shown to be not sensitive to changes in the EoS while the smaller mean free path in the hydrodynamic evolution reflects directly in higher flow results which are consistent with the experimental data. This finding indicates qualitatively that physical mechanisms like viscosity and other non equilibrium effects play an essentially more important role than the EoS when bulk observables like flow are investigated. In the last chapter, results for the thermal production of MEMOs in nucleus-nucleus collisions from a combined micro+macro approach are presented. Multiplicities, rapidity and transverse momentum spectra are predicted for Pb+Pb interaction at different beam energies. The presented excitation functions for various MEMO multiplicities show a clear maximum at the upper FAIR energy regime making this facility the ideal place to study the production of these exotic forms of multistrange objects.
This dissertation connects two independent fields of theoretical neuroscience: on the one hand, the self-organization of topographic connectivity patterns, and on the other hand, invariant object recognition, that is the recognition of objects independently of their various possible retinal representations (for example due to translations or scalings). The topographic representation is used in the presented approach, as a coordinate system, which then allows for the implementation of invariance transformations. Hence this study shows, that it is possible that the brain self-organizes before birth, so that it is able to invariantly recognize objects immediately after birth. Besides the core hypothesis that links prenatal work with object recognition, advancements in both fields themselves are also presented. In the beginning of the thesis, a novel analytically solvable probabilistic generative model for topographic maps is introduced. And at the end of the thesis, a model that integrates classical feature-based ideas with the normalization-based approach is presented. This bilinear model makes use of sparseness as well as slowness to implement "optimal" topographic representations. It is therefore a good candidate for hierarchical processing in the brain and for future research.
Statistical physics of power flows on networks with a high share of fluctuating renewable generation
(2010)
Renewable energy sources will play an important role in future generation of electrical energy. This is due to the fact that fossil fuel reserves are limited and because of the waste caused by conventional electricity generation. The most important sources of renewable energy, wind and solar irradiation, exhibit strong temporal fluctuations. This poses new problems for the security of supply. Further, the power flows become a stochastic character so that new methods are required to predict flows within an electrical grid. The main focus of this work is the description of power flows in a electrical transmission network with a high share of renewable generation of electrical energy. To define an appropriate model, it is important to understand the general set-up of a stable system with fluctuating generation. Therefore, generation time series of solar and wind power are compared to load time series for whole Europe and the required balancing or storage capacities analyzed. With these insights, a simple model is proposed to study the power flows. An approximation to the full power flow equations is used and evaluated with Monte-Carlo simulations. Further, approximations to the distributions of power flows along the links are analytically derived. Finally, the results are compared to the power flows calculated from the generation and load data.
Quasi-zweidimensionale organischen Ladungstransfersalze weisen gewisse Analogien zu den Hochtemperatur-Kupratsupraleitern (HTSL) auf. Zu nennen ist einerseits der ähnliche schichtartige Aufbau, wobei sich leitfähige und isolierende Ebenen abwechseln. Zum anderen liegt der antiferromagnetische Grundzustand in direkter Nachbarschaft zur Supraleitung und bei höheren Temperaturen wird ebenfalls die Entstehung einer Pseudo-Energielücke diskutiert. Im Gegensatz zu den HTSL können die elektronischen Eigenschaften der organischen Ladungstransfersalze jedoch leicht durch äußere Parameter wie hydrostatischen bzw. chemischen Druck - die Verwendung verschiedener Anionen X läßt sich in einem verallgemeinerten Phasendiagramm ebenfalls auf die Achse W/U abbilden, siehe Abschn. 4.2 - oder moderate Temperaturen beeinflußt werden. In den quasi-zweidimensionalen K-(BEDT-TTF)2X-Salzen ist bspw. ein moderater Druck p ~ 250 bar ausreichend, um das antiferromagnetisch-isolierende System (X=Cu[N(CN)2]Cl) auf die metallische Seite des Phasendiagramms zu verschieben, wobei dann im Grundzustand Supraleitung auftritt (Tc ~ 12,8 K). Eine Dotierung wie bei den HTSL und die damit einhergehende unerwünschte Unordnung ist nicht notwendig um einen Isolator-Metall-übergang zu induzieren. Demnach sind die experimentellen Anforderungen im Vergleich zu anderen stark korrelierten Elektronensystemen auf relativ einfache Weise zu realisieren. Auch das macht die organischen Ladungstransfersalze zu idealen Modellsystemen, um fundamentale Konzepte der theoretischen Festkörperphysik zu studieren, wovon einige bislang lediglich von akademischem Interesse waren. Erstmalig wird in dieser Arbeit die Fluktuationsspektroskopie als experimentelle Methode angewendet, um die Dynamik des TT-Elektronensystems in den quasi-zweidimensionalen organischen Ladungstransfersalzen K-(BEDT-TTF)2X bei niedrigen Frequenzen zu studieren. Ziel ist es, Informationen über die Temperatur-, Druck- und Magnetfeld-Abhängigkeit der spektralen Leistungsdichte des Widerstandsrauschens und damit über die Dynamik der Ladungsfluktuationen zu gewinnen. Insbesondere in der Nähe korrelationsgetriebener Ordnungsphänomene spielt die Dynamik der Ladungsträger eine entscheidende Rolle. Auch die Kopplung des elektronischen Systems an bestimmte strukturelle Anregungen hat Einfluß auf das Widerstandsrauschen. Zu Beginn wird eine kurze Einführung in die Signalanalyse gegeben und daran anschließend werden verschiedene Arten des Rauschens in Festkörpern dargestellt (Kap. 1). Einige der für diese Arbeit relevanten Ordnungsphänomene werden in Kap. 2 in knapper Form eingeführt, wobei auf die dynamischen Eigenschaften in der Nähe eines Glasübergangs etwas ausführlicher eingegangen wird. Nach der Vorstellung der eingesetzten Meßmethoden, des Versuchsaufbaus und der Probenkontaktierung (Kap. 3) werden die experimentellen Ergebnisse an den K-(BEDT-TTF)2X-Salzen in Kap. 4 ausführlich diskutiert.
In this work we study compact stars, i.e. neutron stars, as cosmic laboratories for the nuclear matter. With a mass of around 1 - 3 solar masses and a radius of around 10km, compact stars are very dense and, besides nucleons, can contain exotic matter such as hyperons or quark matter. The KaoS collaboration studied nuclear matter for densities up to 2-3 times saturation density by analysing kaon multiplicities from Au+Au and C+C collisions. The results show that nuclear matter in the corresponding density region is very compressible, with a compressibility of <200MeV. For such soft nuclear equations of state the maximum masses of neutron stars are ca. 1.8 - 1.9 solar masses, whereas the central densities are higher than 5 times nuclear saturation density and therefore point towards a possible phase transition to quark matter. If quark matter would be present in the interior of neutron stars, so-called hybrid stars, it could be produced already during their birth in supernova explosions. To study this we implement a quark matter phase transition in a hadronic equation of state which is used in supernova simulations. Supernova simulations of low and intermediate mass progenitors and two different bag constants show a collapse of the proto neutron star due to the softening of the equations of state in the quark-hadron mixed phase. The stiffening of the equation of state for pure quark matter halts the collapse and leads to the production of a second shock wave. The second shock wave is energetic enough to lead to an explosion of the star and produces a neutrino burst when passing the neutrinospheres. Furthermore, first studies of the longtime cooling of hybrid stars show, that colour superconductivity can significantly influence the cooling behaviour of hybrid stars, if all quarks form Cooper Pairs. For the so-called CSL phase (colour-spin locking) with pairing energies of several MeV, the cooling of the quark phase is suppressed and the hybrid star appears as a pure hadronic star.
Vibronic (vibrational-electronic) transition is one of the fundamental processes in molecular physics. Indeed, vibronic transition is essential both in radiative and nonradiative photophysical or photochemical properties of molecules such as absorption, emission, Raman scattering, circular dichroism, electron transfer, internal conversion, etc. A detailed understanding of these transitions in varying systems, especially for (large) biomolecules, is thus of particular interest. Describing vibronic transitions in polyatomic systems with hundreds of atoms is, however, a difficult task due to the large number of coupled degrees of freedom. Even within the relatively crude harmonic approximation, such as for Born-Oppenheimer harmonic potential energy surfaces, the brute-force evaluation of Franck-Condon intensity profiles in a time-independent sum-over-states approach is prohibitive for complex systems owing to the vast number of multi-dimensional Franck-Condon integrals. The main goal of this thesis is to describe a variety of molecular vibronic transitions, with special focus on the development of approaches that are applicable to extended molecular systems. We use various representations of Fermi’s golden rule in frequency, time and phase spaces via coherent states to reduce the computational complexity. Although each representation has benefits and shortcomings in its evaluation, they complement each other. Peak assignment of a spectrum can be made directly after calculation in the frequency domain but this sum-over-states route is usually slow. In contrast, computation is considerably faster in the time domain with Fourier transformation but the peak assignment is not directly available. The representation in phase space does not immediately provide physically-meaningful quantities but it can link frequency and time domains. This has been applied to, herein, for example (non-Condon) absorption spectra of benzene and electron transfer of bacteriochlorophyll in the photosynthetic reaction center at finite temperature. This work is a significant step in the treatment of vibronic structure, allowing for the accurate and efficient treatment of complex systems, and provides a new analysis tool for molecular science.
This thesis investigates the development of early cognition in infancy using neural network models. Fundamental events in visual perception such as caused motion, occlusion, object permanence, tracking of moving objects behind occluders, object unity perception and sequence learning are modeled in a unifying computational framework while staying close to experimental data in developmental psychology of infancy. In the first project, the development of causality and occlusion perception in infancy is modeled using a simple, three-layered, recurrent network trained with error backpropagation to predict future inputs (Elman network). The model unifies two infant studies on causality and occlusion perception. Subsequently, in the second project, the established framework is extended to a larger prediction network that models the development of object unity, object permanence and occlusion perception in infancy. It is shown that these different phenomena can be unified into a single theoretical framework thereby explaining experimental data from 14 infant studies. The framework shows that these developmental phenomena can be explained by accurately representing and predicting statistical regularities in the visual environment. The models assume (1) different neuronal populations processing different motion directions of visual stimuli in the visual cortex of the newborn infant which are supported by neuroscientific evidence and (2) available learning algorithms that are guided by the goal of predicting future events. Specifically, the models demonstrate that no innate force notions, motion analysis modules, common motion detectors, specific perceptual rules or abilities to "reason" about entities which have been widely postulated in the developmental literature are necessary for the explanation of the discussed phenomena. Since the prediction of future events turned out to be fruitful for theoretical explanation of various developmental phenomena and a guideline for learning in infancy, the third model addresses the development of visual expectations themselves. A self-organising, fully recurrent neural network model that forms internal representations of input sequences and maps them onto eye movements is proposed. The reinforcement learning architecture (RLA) of the model learns to perform anticipatory eye movements as observed in a range of infant studies. The model suggests that the goal of maximizing the looking time at interesting stimuli guides infants' looking behavior thereby explaining the occurrence and development of anticipatory eye movements and reaction times. In contrast to classical neural network modelling approaches in the developmental literature, the model uses local learning rules and contains several biologically plausible elements like excitatory and inhibitory spiking neurons, spike-timing dependent plasticity (STDP), intrinsic plasticity (IP) and synaptic scaling. It is also novel from the technical point of view as it uses a dynamic recurrent reservoir shaped by various plasticity mechanisms and combines it with reinforcement learning. The model accounts for twelve experimental studies and predicts among others anticipatory behavior for arbitrary sequences and facilitated reacquisition of already learned sequences. All models emphasize the development of the perception of the discussed phenomena thereby addressing the questions of how and why this developmental change takes place - questions that are difficult to be assessed experimentally. Despite the diversity of the discussed phenomena all three projects rely on the same principle: the prediction of future events. This principle suggests that cognitive development in infancy may largely be guided by building internal models and representations of the visual environment and using those models to predict its future development.