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- Institut für Physik und Astronomie (1480) (remove)
Diffusion with stochastic resetting is a paradigm of resetting processes. Standard renewal or master equation approach are typically used to study steady state and other transport properties such as average, mean squared displacement etc.
What remains less explored is the two time point correlation functions whose evaluation is often daunting since it requires the implementation of the exact time dependent probability density functions of the resetting processes which are unknown for most of the problems.
We adopt a different approach that allows us to write a stochastic solution for a single trajectory undergoing resetting.
Moments and the autocorrelation functions between any two times along the trajectory can then be computed directly using the laws of total expectation. Estimation of autocorrelation functions turns out to be pivotal for investigating the ergodic properties of various observables for this canonical model.
In particular, we investigate two observables (i) sample mean which is widely used in economics and (ii) time-averaged-mean-squared-displacement (TAMSD) which is of acute interest in physics.
We find that both diffusion and drift-diffusion processes with resetting are ergodic at the mean level unlike their reset-free counterparts. In contrast, resetting renders ergodicity breaking in the TAMSD while both the stochastic processes are ergodic when resetting is absent. We quantify these behaviors with detailed analytical study and corroborate with extensive numerical simulations.
Our results can be verified in experimental set-ups that can track single particle trajectories and thus have strong implications in understanding the physics of resetting.
With the increasing sensitivity of gravitational-wave detectors, we expect to observe multiple binary neutron-star systems through gravitational waves in the near future. The combined analysis of these gravitational-wave signals offers the possibility to constrain the neutron-star radius and the equation of state of dense nuclear matter with unprecedented accuracy. However, it is crucial to ensure that uncertainties inherent in the gravitational-wave models will not lead to systematic biases when information from multiple detections is combined. To quantify waveform systematics, we perform an extensive simulation campaign of binary neutron-star sources and analyze them with a set of four different waveform models. For our analysis with 38 simulations, we find that statistical uncertainties in the neutron-star radius decrease to 1250 m (2% at 90% credible interval) but that systematic differences between currently employed waveform models can be twice as large. Hence, it will be essential to ensure that systematic biases will not become dominant in inferences of the neutron-star equation of state when capitalizing on future developments.
Numerical studies of the dynamics of gravitational systems, e.g., black hole-neutron star systems, require physical and constraint-satisfying initial data. In this article, we present the newly developed pseudospectral code ELLIPTICA, an infrastructure for construction of initial data for various binary and single gravitational systems of all kinds. The elliptic equations under consideration are solved on a single spatial hypersurface of the spacetime manifold. Using coordinate maps, the hypersurface is covered by patches whose boundaries can adapt to the surface of the compact objects. To solve elliptic equations with arbitrary boundary condition, ELLIPTICA deploys a Schur complement domain decomposition method with a direct solver. In this version, we use cubed sphere coordinate maps and the fields are expanded using Chebyshev polynomials of the first kind. Here, we explain the building blocks of ELLIPTICA and the initial data construction algorithm for a black hole-neutron star binary system. We perform convergence tests and evolve the data to validate our results. Within our framework, the neutron star can reach spin values close to breakup with arbitrary direction, while the black hole can have arbitrary spin with dimensionless spin magnitude ∼0.8.
We perform numerical studies of a thermally driven, overdamped particle in a random quenched force field, known as the Sinai model. We compare the unbounded motion on an infinite 1-dimensional domain to the motion in bounded domains with reflecting boundaries and show that the unbounded motion is at every time close to the equilibrium state of a finite system of growing size. This is due to time scale separation: inside wells of the random potential, there is relatively fast equilibration, while the motion across major potential barriers is ultraslow. Quantities studied by us are the time dependent mean squared displacement, the time dependent mean energy of an ensemble of particles, and the time dependent entropy of the probability distribution. Using a very fast numerical algorithm, we can explore times up top 10(17) steps and thereby also study finite-time crossover phenomena.
Improving permafrost dynamics in land surface models: insights from dual sensitivity experiments
(2024)
The thawing of permafrost and the subsequent release of greenhouse gases constitute one of the most significant and uncertain positive feedback loops in the context of climate change, making predictions regarding changes in permafrost coverage of paramount importance. To address these critical questions, climate scientists have developed Land Surface Models (LSMs) that encompass a multitude of physical soil processes. This thesis is committed to advancing our understanding and refining precise representations of permafrost dynamics within LSMs, with a specific focus on the accurate modeling of heat fluxes, an essential component for simulating permafrost physics.
The first research question overviews fundamental model prerequisites for the representation of permafrost soils within land surface modeling. It includes a first-of-its-kind comparison between LSMs in CMIP6 to reveal their differences and shortcomings in key permafrost physics parameters. Overall, each of these LSMs represents a unique approach to simulating soil processes and their interactions with the climate system. Choosing the most appropriate model for a particular application depends on factors such as the spatial and temporal scale of the simulation, the specific research question, and available computational resources.
The second research question evaluates the performance of the state-of-the-art Community Land Model (CLM5) in simulating Arctic permafrost regions. Our approach overcomes traditional evaluation limitations by individually addressing depth, seasonality, and regional variations, providing a comprehensive assessment of permafrost and soil temperature dynamics. I compare CLM5's results with three extensive datasets: (1) soil temperatures from 295 borehole stations, (2) active layer thickness (ALT) data from the Circumpolar Active Layer Monitoring Network (CALM), and (3) soil temperatures, ALT, and permafrost extent from the ESA Climate Change Initiative (ESA-CCI). The results show that CLM5 aligns well with ESA-CCI and CALM for permafrost extent and ALT but reveals a significant global cold temperature bias, notably over Siberia. These results echo a persistent challenge identified in numerous studies: the existence of a systematic 'cold bias' in soil temperature over permafrost regions. To address this challenge, the following research questions propose dual sensitivity experiments.
The third research question represents the first study to apply a Plant Functional Type (PFT)-based approach to derive soil texture and soil organic matter (SOM), departing from the conventional use of coarse-resolution global data in LSMs. This novel method results in a more uniform distribution of soil organic matter density (OMD) across the domain, characterized by reduced OMD values in most regions. However, changes in soil texture exhibit a more intricate spatial pattern. Comparing the results to observations reveals a significant reduction in the cold bias observed in the control run. This method shows noticeable improvements in permafrost extent, but at the cost of an overestimation in ALT. These findings emphasize the model's high sensitivity to variations in soil texture and SOM content, highlighting the crucial role of soil composition in governing heat transfer processes and shaping the seasonal variation of soil temperatures in permafrost regions.
Expanding upon a site experiment conducted in Trail Valley Creek by \citet{dutch_impact_2022}, the fourth research question extends the application of the snow scheme proposed by \citet{sturm_thermal_1997} to cover the entire Arctic domain. By employing a snow scheme better suited to the snow density profile observed over permafrost regions, this thesis seeks to assess its influence on simulated soil temperatures. Comparing this method to observational datasets reveals a significant reduction in the cold bias that was present in the control run. In most regions, the Sturm run exhibits a substantial decrease in the cold bias. However, there is a distinctive overshoot with a warm bias observed in mountainous areas. The Sturm experiment effectively addressed the overestimation of permafrost extent in the control run, albeit resulting in a substantial reduction in permafrost extent over mountainous areas. ALT results remain relatively consistent compared to the control run. These outcomes align with our initial hypothesis, which anticipated that the reduced snow insulation in the Sturm run would lead to higher winter soil temperatures and a more accurate representation of permafrost physics.
In summary, this thesis demonstrates significant advancements in understanding permafrost dynamics and its integration into LSMs. It has meticulously unraveled the intricacies involved in the interplay between heat transfer, soil properties, and snow dynamics in permafrost regions. These insights offer novel perspectives on model representation and performance.
Non-fullerene acceptors (NFAs) as used in state-of-the-art organic solar cells feature highly crystalline layers that go along with low energetic disorder.
Here, the crucial role of energetic disorder in blends of the donor polymer PM6 with two Y-series NFAs, Y6, and N4 is studied.
By performing temperature-dependent charge transport and recombination studies, a consistent picture of the shape of the density of state distributions for free charges in the two blends is developed, allowing an analytical description of the dependence of the open-circuit voltage V-OC on temperature and illumination intensity.
Disorder is found to influence the value of the V-OC at room temperature, but also its progression with temperature. Here, the PM6:Y6 blend benefits substantially from its narrower state distributions.
The analysis also shows that the energy of the equilibrated free charge population is well below the energy of the NFA singlet excitons for both blends and possibly below the energy of the populated charge transfer manifold, indicating a down-hill driving force for free charge formation.
It is concluded that energetic disorder of charge-separated states has to be considered in the analysis of the photovoltaic properties, even for the more ordered PM6:Y6 blend.
The formation of large polarons has been proposed as reason for the high defect tolerance, low mobility, low charge carrier trapping, and low nonradiative recombination rates of lead halide perovskites. Recently, direct evidence for large-polaron formation has been reported from a 50% effective mass enhancement in angle-resolved photoemission of CsPbBr3 over theory for the orthorhombic structure. We present in-depth band dispersion measurements of CsPbBr3 and GW calculations, which lead to similar effective masses at the valence band maximum of 0.203 1 0.016 m0 in experiment and 0.226 m0 in orthorhombic theory. We argue that the effective mass can be explained solely on the basis of electron-electron correlation and largepolaron formation cannot be concluded from photoemission data.
Only a fast and global transformation towards decarbonization and sustainability can keep the Earth in a civilization-friendly state. As hotspots for (green) innovation and experimentation, cities could play an important role in this transition. They are also known to profit from each other's ideas, with policy and technology innovations spreading to other cities. In this way, cities can be conceptualized as nodes in a globe-spanning learning network. The dynamics of this process are important for society's response to climate change and other challenges, but remain poorly understood on a macroscopic level. In this contribution, we develop an approach to identify whether network-based complex contagion effects are a feature of sustainability policy adoption by cities, based on dose-response contagion and surrogate data models. We apply this methodology to an exemplary data set, comprising empirical data on the spreading of a public transport innovation (Bus Rapid Transit Systems) and a global inter-city connection network based on scheduled flight routes. Although our approach is not able to identify detailed mechanisms, our results point towards a contagious spreading process, and cannot be explained by either the network structure or the increase in global adoption rate alone. Further research on the role of a city's abstract "global neighborhood" regarding its policy and innovation decisions is thus both needed and promising, and may connect with research on social tipping processes. The methodology is generic, and can be used to compare the predictive power for innovation spreading of different kinds of inter-city network connections, e.g. via transport links, trade, or co-membership in political networks.
We investigate the effects of Markovian resetting events on continuous time random walks where the waiting times and the jump lengths are random variables distributed according to power-law probability density functions.
We prove the existence of a nonequilibrium stationary state and finite mean first arrival time.
However, the existence of an optimum reset rate is conditioned to a specific relationship between the exponents of both power-law tails.
We also investigate the search efficiency by finding the optimal random walk which minimizes the mean first arrival time in terms of the reset rate, the distance of the initial position to the target, and the characteristic transport exponents.
Leptonic nonthermal emission from supernova remnants evolving in the circumstellar magnetic field
(2022)
The very-high-energy (VHE; E > 100 GeV) gamma-ray emission observed from a number of supernova remnants (SNRs) indicates particle acceleration to high energies at the shock of the remnants and a potentially significant contribution to Galactic cosmic rays. It is extremely difficult to determine whether protons (through hadronic interactions and subsequent pion decay) or electrons (through inverse Compton scattering on ambient photon fields) are responsible for this emission. For a successful diagnostic, a good understanding of the spatial and energy distribution of the underlying particle population is crucial. Most SNRs are created in core-collapse explosions and expand into the wind bubble of their progenitor stars. This circumstellar medium features a complex spatial distribution of gas and magnetic field which naturally strongly affects the resulting particle population. In this work, we conduct a detailed study of the spectro-spatial evolution of the electrons accelerated at the forward shock of core-collapse SNRs and their nonthermal radiation, using the RATPaC code that is designed for the time- and spatially dependent treatment of particle acceleration at SNR shocks. We focus on the impact of the spatially inhomogeneous magnetic field through the efficiency of diffusion and synchrotron cooling. It is demonstrated that the structure of the circumstellar magnetic field can leave strong signatures in the spectrum and morphology of the resulting nonthermal emission.
Ultrafast X-ray diffraction is used to quantify the transport of energy in laser-excited nanoscale gold-nickel (Au-Ni) bilayers.
Electron transport and efficient electron-phonon coupling in Ni convert the laser-deposited energy in the conduction electrons within a few picoseconds into a strong non-equilibrium between hot Ni and cold Au phonons at the bilayer interface.
Modeling of the subsequent equilibration dynamics within various two-temperature models confirms that for ultrathin Au films, the thermal transport is dominated by phonons instead of conduction electrons because of the weak electron-phonon coupling in Au.
Magnetic reconnection is a multi-faceted process of energy conversion in astrophysical, space and laboratory plasmas that operates at microscopic scales but has macroscopic drivers and consequences.
Solar flares present a key laboratory for its study, leaving imprints of the microscopic physics in radiation spectra and allowing the macroscopic evolution to be imaged, yet a full observational characterization remains elusive.
Here we combine high resolution imaging and spectral observations of a confined solar flare at multiple wavelengths with data-constrained magnetohydrodynamic modeling to study the dynamics of the flare plasma from the current sheet to the plasmoid scale. The analysis suggests that the flare resulted from the interaction of a twisted magnetic flux rope surrounding a filament with nearby magnetic loops whose feet are anchored in chromospheric fibrils. Bright cusp-shaped structures represent the region around a reconnecting separator or quasi-separator (hyperbolic flux tube).
The fast reconnection, which is relevant for other astrophysical environments, revealed plasmoids in the current sheet and separatrices and associated unresolved turbulent motions.
Solar flares provide wide range of observational details about fundamental processes involved. Here, the authors show evidence for magnetic reconnection in a strong confined solar flare displaying all four reconnection flows with plasmoids in the current sheet and the separatrices.
The simultaneous detection of gravitational waves and light from the binary neutron star merger GW170817 led to independent measurements of distance and redshift, providing a direct estimate of the Hubble constant H-0 that does not rely on a cosmic distance ladder, nor assumes a specific cosmological model.
By using gravitational waves as "standard sirens", this approach holds promise to arbitrate the existing tension between the H-0 value inferred from the cosmic microwave background and those obtained from local measurements.
However, the known degeneracy in the gravitational-wave analysis between distance and inclination of the source led to a H-0 value from GW170817 that was not precise enough to resolve the existing tension.
In this review, we summarize recent works exploiting the viewing-angle dependence of the electromagnetic signal, namely the associated short gamma-ray burst and kilonova, to constrain the system inclination and improve on H-0.
We outline the key ingredients of the different methods, summarize the results obtained in the aftermath of GW170817 and discuss the possible systematics introduced by each of these methods.
In the last years, electron density profile functions characterized by a linear dependence on the scale height showed good results when approximating the topside ionosphere. The performance above 800 km, however, is not yet well investigated.
This study investigates the capability of the semi-Epstein functions to represent electron density profiles from the peak height up to 20,000 km. Electron density observations recorded by the Van Allen Probes were used to resolve the scale height dependence in the plasmasphere.
It was found that the linear dependence of the scale height in the topside ionosphere cannot be directly used to extrapolate profiles above 800 km.
We find that the dependence of scale heights on altitude is quadratic in the plasmasphere. A statistical model of the scale heights is therefore proposed. After combining the topside ionosphere and plasmasphere by a unified model, we have obtained good estimations not only in the profile shapes, but also in the Total Electron Content magnitude and distributions when compared to actual measurements from 2013, 2014, 2016 and 2017.
Our investigation shows that Van Allen Probes can be merged to radio-occultation data to properly represent the upper ionosphere and plasmasphere by means of a semi-Epstein function.
A large Rashba effect is essential for future applications in spintronics. Particularly attractive is understanding and controlling nonequilibrium properties of ferroelectric Rashba semiconductors. Here, time- and angle-resolved photoemission is utilized to access the ultrafast dynamics of bulk and surface transient Rashba states after femtosecond optical excitation of GeTe. A complex thermalization pathway is observed, wherein three different timescales can be clearly distinguished: intraband thermalization, interband equilibration, and electronic cooling. These dynamics exhibit an unconventional temperature dependence: while the cooling phase speeds up with increasing sample temperature, the opposite happens for interband thermalization. It is demonstrated how, due to the Rashba effect, an interdependence of these timescales on the relative strength of both electron-electron and electron-phonon interactions is responsible for the counterintuitive temperature dependence, with spin-selection constrained interband electron-electron scatterings found both to dominate dynamics away from the Fermi level, and to weaken with increasing temperature. These findings are supported by theoretical calculations within the Boltzmann approach explicitly showing the opposite behavior of all relevant electron-electron and electron-phonon scattering channels with temperature, thus confirming the microscopic mechanism of the experimental findings. The present results are important for future applications of ferroelectric Rashba semiconductors and their excitations in ultrafast spintronics.
Using over-5-year EMFISIS wave measurements from Van Allen Probes, we present a detailed survey to identify the controlling factors among the geomagnetic indices and solar wind parameters for the 1-min root mean square amplitudes of lower band chorus (LBC) and upper band chorus (UBC).
A set of important features are automatically determined by feature selection techniques, namely, Random Forest and Maximum Relevancy Minimum Redundancy. Our analysis results indicate the AE index with zero-time-delay dominates the intensity evolution of LBC and UBC, consistent with the evidence that chorus waves prefer to occur and amplify during enhanced substorm periods. Regarding solar wind parameters, solar wind speed and IMF B-z are identified as the controlling factors for chorus wave intensity. Using the combination of all these important features, a predictive neural network model of chorus wave intensity is established to reconstruct the temporal variations of chorus wave intensity, for which application of Random Forest produces the overall best performance.
Plain Language Summary
Whistler mode chorus waves are electromagnetic waves observed in the low-density region near the geomagnetic equator outside the plasmapause. The dynamics of Earth's radiation belts are largely influenced by chorus waves owing to their dual contributions to both radiation belt electron acceleration and loss. In this study, we use feature selection techniques to identify the controlling geomagnetic and solar wind factors for magnetospheric chorus waves. Feature selection techniques implement the processes which can select the features most influential to the output.
In this study, the inputs are geomagnetic indices and solar wind parameters and the output is the chorus wave intensity. The results indicate that AE index with zerotime delay dominates the chorus wave intensity. Furthermore, solar wind speed and IMF B-z are identified as the most important solar wind drivers for chorus wave intensity.
On basis of the combination of all these important geomagnetic and solar wind controlling factors, we develop a neural network model of chorus wave intensity, and find that the model with the inputs identified using the Random Forest method produces the overall best performance.
The determination of the spin state of iron-bearing compounds at high pressure and temperature is crucial for our understanding of chemical and physical properties of the deep Earth. Studies on the relationship between the coordination of iron and its electronic spin structure in iron-bearing oxides, silicates, carbonates, iron alloys, and other minerals found in the Earth's mantle and core are scarce because of the technical challenges to simultaneously probe the sample at high pressures and temperatures. We used the unique properties of a pulsed and highly brilliant x-ray free electron laser (XFEL) beam at the High Energy Density (HED) instrument of the European XFEL to x-ray heat and probe samples contained in a diamond anvil cell. We heated and probed with the same x-ray pulse train and simultaneously measured x-ray emission and x-ray diffraction of an FeCO3 sample at a pressure of 51 GPa with up to melting temperatures. We collected spin state sensitive Fe K beta(1,3) fluorescence spectra and detected the sample's structural changes via diffraction, observing the inverse volume collapse across the spin transition. During x-ray heating, the carbonate transforms into orthorhombic Fe4C3O12 and iron oxides. Incipient melting was also observed. This approach to collect information about the electronic state and structural changes from samples contained in a diamond anvil cell at melting temperatures and above will considerably improve our understanding of the structure and dynamics of planetary and exoplanetary interiors.
We present a study of the control of electric field induced strain on the magnetic and electrical transport properties in a magnetoelastically coupled artificial multiferroic Fe3O4/BaTiO3 heterostructure.
In this Fe3O4/BaTiO3 heterostructure, the Fe3O4 thin film is epitaxially grown in the form of bilateral domains, analogous to a-c stripe domains of the underlying BaTiO3(001) substrate.
By in situ electric field dependent magnetization measurements, we demonstrate the extrinsic control of the magnetic anisotropy and the characteristic Verwey metal-insulator transition of the epitaxial Fe3O4 thin film in a wide temperature range between 20-300 K, via strain mediated converse magnetoelectric coupling.
In addition, we observe strain induced modulations in the magnetic and electrical transport properties of the Fe3O4 thin film across the thermally driven intrinsic ferroelectric and structural phase transitions of the BaTiO3 substrate.
In situ electric field dependent Raman measurements reveal that the electric field does not significantly modify the antiphase boundary defects in the Fe3O4 thin film once it is thermodynamically stable after deposition and that the modification of the magnetic properties is mainly caused by strain induced lattice distortions and magnetic anisotropy.
These results provide a framework to realize electrical control of the magnetization in a classical highly correlated transition metal oxide.
The time instant-the first-passage time (FPT)-when a diffusive particle (e.g., a ligand such as oxygen or a signalling protein) for the first time reaches an immobile target located on the surface of a bounded three-dimensional domain (e.g., a hemoglobin molecule or the cellular nucleus) is a decisive characteristic time-scale in diverse biophysical and biochemical processes, as well as in intermediate stages of various inter- and intra-cellular signal transduction pathways. Adam and Delbruck put forth the reduction-of-dimensionality concept, according to which a ligand first binds non-specifically to any point of the surface on which the target is placed and then diffuses along this surface until it locates the target. In this work, we analyse the efficiency of such a scenario and confront it with the efficiency of a direct search process, in which the target is approached directly from the bulk and not aided by surface diffusion. We consider two situations: (i) a single ligand is launched from a fixed or a random position and searches for the target, and (ii) the case of 'amplified' signals when N ligands start either from the same point or from random positions, and the search terminates when the fastest of them arrives to the target. For such settings, we go beyond the conventional analyses, which compare only the mean values of the corresponding FPTs. Instead, we calculate the full probability density function of FPTs for both scenarios and study its integral characteristic-the 'survival' probability of a target up to time t. On this basis, we examine how the efficiencies of both scenarios are controlled by a variety of parameters and single out realistic conditions in which the reduction-of-dimensionality scenario outperforms the direct search.
How related are the ergodic properties of the over- and underdamped Langevin equations driven by fractional Gaussian noise? We here find that for massive particles performing fractional Brownian motion (FBM) inertial effects not only destroy the stylized fact of the equivalence of the ensemble-averaged mean-squared displacement (MSD) to the time-averaged MSD (TAMSD) of overdamped or massless FBM, but also dramatically alter the values of the ergodicity-breaking parameter (EB). Our theoretical results for the behavior of EB for underdamped or massive FBM for varying particle mass m, Hurst exponent H, and trace length T are in excellent agreement with the findings of stochastic computer simulations. The current results can be of interest for the experimental community employing various single-particle-tracking techniques and aiming at assessing the degree of nonergodicity for the recorded time series (studying, e.g., the behavior of EB versus lag time). To infer FBM as a realizable model of anomalous diffusion for a set single-particle-tracking data when massive particles are being tracked, the EBs from the data should be compared to EBs of massive (rather than massless) FBM.