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- Institut für Physik und Astronomie (362) (remove)
Auf der Grundlage von Sonnenphotometermessungen an drei Messstationen (AWIPEV/ Koldewey in Ny-Ålesund (78.923 °N, 11.923 °O) 1995–2008, 35. Nordpol Driftstation – NP-35 (84.3–85.5 °N, 41.7–56.6 °O) März/April 2008, Sodankylä (67.37 °N, 26.65 °O) 2004–2007) wird die Aerosolvariabilität in der europäischen Arktis und deren Ursachen untersucht. Der Schwerpunkt liegt dabei auf der Frage des Zusammenhanges zwischen den an den Stationen gemessenen Aerosolparametern (Aerosol optische Dicke, Angström Koeffizient, usw.) und dem Transport des Aerosols sowohl auf kurzen Zeitskalen (Tagen) als auch auf langen Zeitskalen (Monate, Jahre). Um diesen Zusammenhang herzustellen, werden für die kurzen Zeitskalen mit dem Trajektorienmodell PEP-Tracer 5-Tage Rückwärtstrajektorien in drei Starthöhen (850 hPa, 700 hPa, 500 hPa) für die Uhrzeiten 00, 06, 12 und 18 Uhr berechnet. Mit Hilfe der nicht-hierarchischen Clustermethode k-means werden die berechneten Rückwärtstrajektorien dann zu Gruppen zusammengefasst und bestimmten Quellgebieten und den gemessenen Aerosol optischen Dicken zugeordnet. Die Zuordnung von Aerosol optischer Dicke und Quellregion ergibt keinen eindeutigen Zusammenhang zwischen dem Transport verschmutzter Luftmassen aus Europa oder Russland bzw. Asien und erhöhter Aerosol optischer Dicke. Dennoch ist für einen konkreten Einzelfall (März 2008) ein direkter Zusammenhang von Aerosoltransport und hohen Aerosol optischen Dicken nachweisbar. In diesem Fall gelangte Waldbrandaerosol aus Südwestrussland in die Arktis und konnte sowohl auf der NP-35 als auch in Ny-Ålesund beobachtet werden. In einem weiteren Schritt wird mit Hilfe der EOF-Analyse untersucht, inwieweit großskalige atmosphärische Zirkulationsmuster für die Aerosolvariabilität in der europäischen Arktis verantwortlich sind. Ähnlich wie bei der Trajektorienanalyse ist auch die Verbindung der atmosphärischen Zirkulation zu den Photometermessungen an den Stationen in der Regel nur schwach ausgeprägt. Eine Ausnahme findet sich bei der Betrachtung des Jahresganges des Bodendruckes und der Aerosol optischen Dicke. Hohe Aerosol optische Dicken treten im Frühjahr zum einen dann auf, wenn durch das Islandtief und das sibirische Hochdruckgebiet Luftmassen aus Europa oder Russland/Asien in die Arktis gelangen, und zum anderen, wenn sich ein kräftiges Hochdruckgebiet über Grönland und weiten Teilen der Arktis befindet. Ebenso zeigt sich, dass der Übergang zwischen Frühjahr und Sommer zumindest teilweise bedingt ist durch denWechsel vom stabilen Polarhoch im Winter und Frühjahr zu einer stärker von Tiefdruckgebieten bestimmten arktischen Atmosphäre im Sommer. Die geringere Aerosolkonzentration im Sommer kann zum Teil mit einer Zunahme der nassen Deposition als Aerosolsenke begründet werden. Für Ny-Ålesund wird neben den Transportmustern auch die chemische Zusammensetzung des Aerosols mit Hilfe von Impaktormessungen an der Zeppelinstation auf dem Zeppelinberg (474m ü.NN) nahe Ny-Ålesund abgeleitet. Dabei ist die positive Korrelation der Aerosoloptischen Dicke mit der Konzentration von Sulfationen und Ruß sehr deutlich. Beide Stoffe gelangen zu einem Großteil durch anthropogene Emissionen in die Atmosphäre. Die damit nachweisbar anthropogen geprägte Zusammensetzung des arktischen Aerosols steht im Widerspruch zum nicht eindeutig herstellbaren Zusammenhang mit dem Transport des Aerosols aus Industrieregionen. Dies kann nur durch einen oder mehrere gleichzeitig stattfindende Transformationsprozesse (z. B. Nukleation von Schwefelsäurepartikeln) während des Transportes aus den Quellregionen (Europa, Russland) erklärt werden.
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.
We investigate properties of quantum mechanical systems in the light of quantum information theory. We put an emphasize on systems with infinite-dimensional Hilbert spaces, so-called continuous-variable systems'', which are needed to describe quantum optics beyond the single photon regime and other Bosonic quantum systems. We present methods to obtain a description of such systems from a series of measurements in an efficient manner and demonstrate the performance in realistic situations by means of numerical simulations. We consider both unconditional quantum state tomography, which is applicable to arbitrary systems, and tomography of matrix product states. The latter allows for the tomography of many-body systems because the necessary number of measurements scales merely polynomially with the particle number, compared to an exponential scaling in the generic case. We also present a method to realize such a tomography scheme for a system of ultra-cold atoms in optical lattices. Furthermore, we discuss in detail the possibilities and limitations of using continuous-variable systems for measurement-based quantum computing. We will see that the distinction between Gaussian and non-Gaussian quantum states and measurements plays an crucial role. We also provide an algorithm to solve the large and interesting class of naturally occurring Hamiltonians, namely frustration free ones, efficiently and use this insight to obtain a simple approximation method for slightly frustrated systems. To achieve this goals, we make use of, among various other techniques, the well developed theory of matrix product states, tensor networks, semi-definite programming, and matrix analysis.
In this work, binding interactions between biomolecules were analyzed by a technique that is based on electrically controllable DNA nanolevers. The technique was applied to virus-receptor interactions for the first time. As receptors, primarily peptides on DNA nanostructures and antibodies were utilized. The DNA nanostructures were integrated into the measurement technique and enabled the presentation of the peptides in a controllable geometrical order. The number of peptides could be varied to be compatible to the binding sites of the viral surface proteins.
Influenza A virus served as a model system, on which the general measurability was demonstrated. Variations of the receptor peptide, the surface ligand density, the measurement temperature and the virus subtypes showed the sensitivity and applicability of the technology. Additionally, the immobilization of virus particles enabled the measurement of differences in oligovalent binding of DNA-peptide nanostructures to the viral proteins in their native environment.
When the coronavirus pandemic broke out in 2020, work on binding interactions of a peptide from the hACE2 receptor and the spike protein of the SARS-CoV-2 virus revealed that oligovalent binding can be quantified in the switchSENSE technology. It could also be shown that small changes in the amino acid sequence of the spike protein resulted in complete loss of binding. Interactions of the peptide and inactivated virus material as well as pseudo virus particles could be measured. Additionally, the switchSENSE technology was utilized to rank six antibodies for their binding affinity towards the nucleocapsid protein of SARS-CoV-2 for the development of a rapid antigen test device.
The technique was furthermore employed to show binding of a non-enveloped virus (adenovirus) and a virus-like particle (norovirus-like particle) to antibodies. Apart from binding interactions, the use of DNA origami levers with a length of around 50 nm enabled the switching of virus material. This proved that the technology is also able to size objects with a hydrodynamic diameter larger than 14 nm.
A theoretical work on diffusion and reaction-limited binding interactions revealed that the technique and the chosen parameters enable the determination of binding rate constants in the reaction-limited regime.
Overall, the applicability of the switchSENSE technique to virus-receptor binding interactions could be demonstrated on multiple examples. While there are challenges that remain, the setup enables the determination of affinities between viruses and receptors in their native environment. Especially the possibilities regarding the quantification of oligo- and multivalent binding interactions could be presented.
This work investigates diffusion in nonlinear Hamiltonian systems. The diffusion, more precisely subdiffusion, in such systems is induced by the intrinsic chaotic behavior of trajectories and thus is called chaotic diffusion''. Its properties are studied on the example of one- or two-dimensional lattices of harmonic or nonlinear oscillators with nearest neighbor couplings. The fundamental observation is the spreading of energy for localized initial conditions. Methods of quantifying this spreading behavior are presented, including a new quantity called excitation time. This new quantity allows for a more precise analysis of the spreading than traditional methods. Furthermore, the nonlinear diffusion equation is introduced as a phenomenologic description of the spreading process and a number of predictions on the density dependence of the spreading are drawn from this equation. Two mathematical techniques for analyzing nonlinear Hamiltonian systems are introduced. The first one is based on a scaling analysis of the Hamiltonian equations and the results are related to similar scaling properties of the NDE. From this relation, exact spreading predictions are deduced. Secondly, the microscopic dynamics at the edge of spreading states are thoroughly analyzed, which again suggests a scaling behavior that can be related to the NDE. Such a microscopic treatment of chaotically spreading states in nonlinear Hamiltonian systems has not been done before and the results present a new technique of connecting microscopic dynamics with macroscopic descriptions like the nonlinear diffusion equation. All theoretical results are supported by heavy numerical simulations, partly obtained on one of Europe's fastest supercomputers located in Bologna, Italy. In the end, the highly interesting case of harmonic oscillators with random frequencies and nonlinear coupling is studied, which resembles to some extent the famous Discrete Anderson Nonlinear Schroedinger Equation. For this model, a deviation from the widely believed power-law spreading is observed in numerical experiments. Some ideas on a theoretical explanation for this deviation are presented, but a conclusive theory could not be found due to the complicated phase space structure in this case. Nevertheless, it is hoped that the techniques and results presented in this work will help to eventually understand this controversely discussed case as well.
Changes in extratropical storm track activity and their implications for extreme weather events
(2016)
The Casimir-Polder interaction between a single neutral atom and a nearby surface, arising from the (quantum and thermal) fluctuations of the electromagnetic field, is a cornerstone of cavity quantum electrodynamics (cQED), and theoretically well established. Recently, Bose-Einstein condensates (BECs) of ultracold atoms have been used to test the predictions of cQED. The purpose of the present thesis is to upgrade single-atom cQED with the many-body theory needed to describe trapped atomic BECs. Tools and methods are developed in a second-quantized picture that treats atom and photon fields on the same footing. We formulate a diagrammatic expansion using correlation functions for both the electromagnetic field and the atomic system. The formalism is applied to investigate, for BECs trapped near surfaces, dispersion interactions of the van der Waals-Casimir-Polder type, and the Bosonic stimulation in spontaneous decay of excited atomic states. We also discuss a phononic Casimir effect, which arises from the quantum fluctuations in an interacting BEC.
Noise is ubiquitous in nature and usually results in rich dynamics in stochastic systems such as oscillatory systems, which exist in such various fields as physics, biology and complex networks. The correlation and synchronization of two or many oscillators are widely studied topics in recent years.
In this thesis, we mainly investigate two problems, i.e., the stochastic bursting phenomenon in noisy excitable systems and synchronization in a three-dimensional Kuramoto model with noise. Stochastic bursting here refers to a sequence of coherent spike train, where each spike has random number of followers due to the combined effects of both time delay and noise. Synchronization, as a universal phenomenon in nonlinear dynamical systems, is well illustrated in the Kuramoto model, a prominent model in the description of collective motion.
In the first part of this thesis, an idealized point process, valid if the characteristic timescales in the problem are well separated, is used to describe statistical properties such as the power spectral density and the interspike interval distribution. We show how the main parameters of the point process, the spontaneous excitation rate, and the probability to induce a spike during the delay action can be calculated from the solutions of a stationary and a forced Fokker-Planck equation. We extend it to the delay-coupled case and derive analytically the statistics of the spikes in each neuron, the pairwise correlations between any two neurons, and the spectrum of the total output from the network.
In the second part, we investigate the three-dimensional noisy Kuramoto model, which can be used to describe the synchronization in a swarming model with helical trajectory. In the case without natural frequency, the Kuramoto model can be connected with the Vicsek model, which is widely studied in collective motion and swarming of active matter. We analyze the linear stability of the incoherent state and derive the critical coupling strength above which the incoherent state loses stability. In the limit of no natural frequency, an exact self-consistent equation of the mean field is derived and extended straightforward to any high-dimensional case.
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.
One of the most exciting predictions of Einstein's theory of gravitation that have not yet been proven experimentally by a direct detection are gravitational waves. These are tiny distortions of the spacetime itself, and a world-wide effort to directly measure them for the first time with a network of large-scale laser interferometers is currently ongoing and expected to provide positive results within this decade. One potential source of measurable gravitational waves is the inspiral and merger of two compact objects, such as binary black holes. Successfully finding their signature in the noise-dominated data of the detectors crucially relies on accurate predictions of what we are looking for. In this thesis, we present a detailed study of how the most complete waveform templates can be constructed by combining the results from (A) analytical expansions within the post-Newtonian framework and (B) numerical simulations of the full relativistic dynamics. We analyze various strategies to construct complete hybrid waveforms that consist of a post-Newtonian inspiral part matched to numerical-relativity data. We elaborate on exsisting approaches for nonspinning systems by extending the accessible parameter space and introducing an alternative scheme based in the Fourier domain. Our methods can now be readily applied to multiple spherical-harmonic modes and precessing systems. In addition to that, we analyze in detail the accuracy of hybrid waveforms with the goal to quantify how numerous sources of error in the approximation techniques affect the application of such templates in real gravitational-wave searches. This is of major importance for the future construction of improved models, but also for the correct interpretation of gravitational-wave observations that are made utilizing any complete waveform family. In particular, we comprehensively discuss how long the numerical-relativity contribution to the signal has to be in order to make the resulting hybrids accurate enough, and for currently feasible simulation lengths we assess the physics one can potentially do with template-based searches.