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Institute
A task-based parallel elliptic solver for numerical relativity with discontinuous Galerkin methods
(2022)
Elliptic partial differential equations are ubiquitous in physics. In numerical relativity---the study of computational solutions to the Einstein field equations of general relativity---elliptic equations govern the initial data that seed every simulation of merging black holes and neutron stars. In the quest to produce detailed numerical simulations of these most cataclysmic astrophysical events in our Universe, numerical relativists resort to the vast computing power offered by current and future supercomputers. To leverage these computational resources, numerical codes for the time evolution of general-relativistic initial value problems are being developed with a renewed focus on parallelization and computational efficiency. Their capability to solve elliptic problems for accurate initial data must keep pace with the increasing detail of the simulations, but elliptic problems are traditionally hard to parallelize effectively.
In this thesis, I develop new numerical methods to solve elliptic partial differential equations on computing clusters, with a focus on initial data for orbiting black holes and neutron stars. I develop a discontinuous Galerkin scheme for a wide range of elliptic equations, and a stack of task-based parallel algorithms for their iterative solution. The resulting multigrid-Schwarz preconditioned Newton-Krylov elliptic solver proves capable of parallelizing over 200 million degrees of freedom to at least a few thousand cores, and already solves initial data for a black hole binary about ten times faster than the numerical relativity code SpEC. I also demonstrate the applicability of the new elliptic solver across physical disciplines, simulating the thermal noise in thin mirror coatings of interferometric gravitational-wave detectors to unprecedented accuracy. The elliptic solver is implemented in the new open-source SpECTRE numerical relativity code, and set up to support simulations of astrophysical scenarios for the emerging era of gravitational-wave and multimessenger astronomy.
Proteine sind an praktisch allen Prozessen in lebenden Zellen maßgeblich beteiligt. Auch in der Biotechnologie werden Proteine in vielfältiger Weise eingesetzt.
Ein Protein besteht aus einer Kette von Aminosäuren. Häufig lagern sich mehrere dieser Ketten zu größeren Strukturen und Funktionseinheiten, sogenannten Proteinkomplexen,
zusammen. Kürzlich wurde gezeigt, dass eine Proteinkomplexbildung bereits während der Biosynthese der Proteine (co-translational) stattfinden kann
und nicht stets erst danach (post-translational) erfolgt. Da Fehlassemblierungen von Proteinen zu Funktionsverlusten und adversen Effekten führen, ist eine präzise und verlässliche Proteinkomplexbildung sowohl für zelluläre Prozesse als auch für biotechnologische Anwendungen essenziell. Mit experimentellen Methoden lassen sich zwar u.a. die Stöchiometrie und die Struktur von Proteinkomplexen bestimmen,
jedoch bisher nicht die Dynamik der Komplexbildung auf unterschiedlichen Zeitskalen. Daher sind grundlegende Mechanismen der Proteinkomplexbildung noch nicht vollständig verstanden. Die hier vorgestellte, auf experimentellen Erkenntnissen aufbauende, computergestützte Modellierung der Proteinkomplexbildung erlaubt eine umfassende Analyse des Einflusses physikalisch-chemischer Parameter
auf den Assemblierungsprozess. Die Modelle bilden möglichst realistisch die experimentellen Systeme der Kooperationspartner (Bar-Ziv, Weizmann-Institut, Israel; Bukau und Kramer, Universität Heidelberg) ab, um damit die Assemblierung von Proteinkomplexen einerseits in einem quasi-zweidimensionalen synthetischen Expressionssystem (in vitro) und andererseits im Bakterium Escherichia coli (in vivo) untersuchen zu können. Mit Hilfe eines vereinfachten Expressionssystems, in dem die Proteine nur an die Chip-Oberfläche, aber nicht aneinander binden können, wird das theoretische Modell parametrisiert. In diesem vereinfachten in-vitro-System durchläuft die Effizienz der Komplexbildung drei Regime – ein bindedominiertes Regime, ein Mischregime und ein produktionsdominiertes Regime. Ihr Maximum erreicht die Effizienz dabei kurz nach dem Übergang vom bindedominierten ins Mischregime und fällt anschließend monoton ab. Sowohl im nicht-vereinfachten in-vitro- als auch im in-vivo-System koexistieren je zwei konkurrierende Assemblierungspfade: Im in-vitro-System erfolgt die Komplexbildung entweder spontan in wässriger Lösung (Lösungsassemblierung) oder aber in einer definierten Schrittfolge an der Chip-Oberfläche (Oberflächenassemblierung); Im in-vivo-System konkurrieren hingegen die co- und die post-translationale Komplexbildung. Es zeigt sich, dass die Dominanz der Assemblierungspfade im in-vitro-System zeitabhängig ist und u.a. durch die Limitierung und Stärke der Bindestellen auf der Chip-Oberfläche beeinflusst werden kann. Im in-vivo-System hat der räumliche Abstand zwischen den Syntheseorten der beiden Proteinkomponenten nur dann einen Einfluss auf die Komplexbildung, wenn die Untereinheiten schnell degradieren. In diesem Fall dominiert die co-translationale Assemblierung auch auf kurzen Zeitskalen deutlich, wohingegen es bei stabilen Untereinheiten zu einem Wechsel von der Dominanz der post- hin zu einer geringen Dominanz der co-translationalen Assemblierung kommt. Mit den in-silico-Modellen lässt sich neben der Dynamik u.a. auch die Lokalisierung der Komplexbildung und -bindung darstellen, was einen Vergleich der theoretischen Vorhersagen mit experimentellen Daten und somit eine Validierung der Modelle ermöglicht. Der hier präsentierte in-silico Ansatz ergänzt die experimentellen Methoden, und erlaubt so, deren Ergebnisse zu interpretieren und neue Erkenntnisse davon abzuleiten.
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.
The complex hierarchical structure of bone undergoes a lifelong remodeling process, where it adapts to mechanical needs. Hereby, bone resorption by osteoclasts and bone formation by osteoblasts have to be balanced to sustain a healthy and stable organ. Osteocytes orchestrate this interplay by sensing mechanical strains and translating them into biochemical signals. The osteocytes are located in lacunae and are connected to one another and other bone cells via cell processes through small channels, the canaliculi. Lacunae and canaliculi form a network (LCN) of extracellular spaces that is able to transport ions and enables cell-to-cell communication. Osteocytes might also contribute to mineral homeostasis by direct interactions with the surrounding matrix. If the LCN is acting as a transport system, this should be reflected in the mineralization pattern. The central hypothesis of this thesis is that osteocytes are actively changing their material environment. Characterization methods of material science are used to achieve the aim of detecting traces of this interaction between osteocytes and the extracellular matrix. First, healthy murine bones were characterized. The properties analyzed were then compared with three murine model systems: 1) a loading model, where a bone of the mouse was loaded during its life time; 2) a healing model, where a bone of the mouse was cut to induce a healing response; and 3) a disease model, where the Fbn1 gene is dysfunctional causing defects in the formation of the extracellular tissue.
The measurement strategy included routines that make it possible to analyze the organization of the LCN and the material components (i.e., the organic collagen matrix and the mineral particles) in the same bone volumes and compare the spatial distribution of different data sets. The three-dimensional network architecture of the LCN is visualized by confocal laser scanning microscopy (CLSM) after rhodamine staining and is then subsequently quantified. The calcium content is determined via quantitative backscattered electron imaging (qBEI), while small- and wide-angle X-ray scattering (SAXS and WAXS) are employed to determine the thickness and length of local mineral particles.
First, tibiae cortices of healthy mice were characterized to investigate how changes in LCN architecture can be attributed to interactions of osteocytes with the surrounding bone matrix. The tibial mid-shaft cross-sections showed two main regions, consisting of a band with unordered LCN surrounded by a region with ordered LCN. The unordered region is a remnant of early bone formation and exhibited short and thin mineral particles. The surrounding, more aligned bone showed ordered and dense LCN as well as thicker and longer mineral particles. The calcium content was unchanged between the two regions.
In the mouse loading model, the left tibia underwent two weeks of mechanical stimulation, which results in increased bone formation and decreased resorption in skeletally mature mice. Here the specific research question addressed was how do bone material characteristics change at (re)modeling sites? The new bone formed in response to mechanical stimulation showed similar properties in terms of the mineral particles, like the ordered calcium region but lower calcium content compared to the right, non-loaded control bone of the same mice. There was a clear, recognizable border between mature and newly formed bone. Nevertheless, some canaliculi went through this border connecting the LCN of mature and newly formed bone.
Additionally, the question should be answered whether the LCN topology and the bone matrix material properties adapt to loading. Although, mechanically stimulated bones did not show differences in calcium content compared to controls, different correlations were found between the local LCN density and the local Ca content depending on whether the bone was loaded or not. These results suggest that the LCN may serve as a mineral reservoir.
For the healing model, the femurs of mice underwent an osteotomy, stabilized with an external fixator and were allowed to heal for 21 days. Thus, the spatial variations in the LCN topology with mineral properties within different tissue types and their interfaces, namely calcified cartilage, bony callus and cortex, could be simultaneously visualized and compared in this model. All tissue types showed structural differences across multiple length scales. Calcium content increased and became more homogeneous from calcified cartilage to bony callus to lamellar cortical bone. The degree of LCN organization increased as well, while the lacunae became smaller, as did the lacunar density between these different tissue types that make up the callus. In the calcified cartilage, the mineral particles were short and thin. The newly formed callus exhibited thicker mineral particles, which still had a low degree of orientation. While most of the callus had a woven-like structure, it also served as a scaffold for more lamellar tissue at the edges. The lamelar bone callus showed thinner mineral particles, but a higher degree of alignment in both, mineral particles and the LCN. The cortex showed the highest values for mineral length, thickness and degree of orientation. At the same time, the lacunae number density was 34% lower and the lacunar volume 40% smaller compared to bony callus. The transition zone between cortical and callus regions showed a continuous convergence of bone mineral properties and lacunae shape. Although only a few canaliculi connected callus and the cortical region, this indicates that communication between osteocytes of both tissues should be possible. The presented correlations between LCN architecture and mineral properties across tissue types may suggest that osteocytes have an active role in mineralization processes of healing.
A mouse model for the disease marfan syndrome, which includes a genetic defect in the fibrillin-1 gene, was investigated. In humans, Marfan syndrome is characterized by a range of clinical symptoms such as long bone overgrowth, loose joints, reduced bone mineral density, compromised bone microarchitecture, and increased fracture rates. Thus, fibrillin-1 seems to play a role in the skeletal homeostasis. Therefore, the present work studied how marfan syndrome alters LCN architecture and the surrounding bone matrix. The mice with marfan syndrome showed longer tibiae than their healthy littermates from an age of seven weeks onwards. In contrast, the cortical development appeared retarded, which was observed across all measured characteristics, i. e. lower endocortical bone formation, looser and less organized lacuno-canalicular network, less collagen orientation, thinner and shorter mineral particles.
In each of the three model systems, this study found that changes in the LCN architecture spatially correlated with bone matrix material parameters. While not knowing the exact mechanism, these results provide indications that osteocytes can actively manipulate a mineral reservoir located around the canaliculi to make a quickly accessible contribution to mineral homeostasis. However, this interaction is most likely not one-sided, but could be understood as an interplay between osteocytes and extra-cellular matrix, since the bone matrix contains biochemical signaling molecules (e.g. non-collagenous proteins) that can change osteocyte behavior. Bone (re)modeling can therefore not only be understood as a method for removing defects or adapting to external mechanical stimuli, but also for increasing the efficiency of possible osteocyte-mineral interactions during bone homeostasis. With these findings, it seems reasonable to consider osteocytes as a target for drug development related to bone diseases that cause changes in bone composition and mechanical properties. It will most likely require the combined effort of materials scientists, cell biologists, and molecular biologists to gain a deeper understanding of how bone cells respond to their material environment.
Stellar interferometry is the only method in observational astronomy for obtaining the highest resolution images of astronomical targets. This method is based on combining light from two or more separate telescopes to obtain the complex visibility that contains information about the brightness distribution of an astronomical source. The applications of stellar interferometry have made significant contributions in the exciting research areas of astronomy and astrophysics, including the precise measurement of stellar diameters, imaging of stellar surfaces, observations of circumstellar disks around young stellar objects, predictions of Einstein's General relativity at the galactic center, and the direct search for exoplanets to name a few. One important related technique is aperture masking interferometry, pioneered in the 1960s, which uses a mask with holes at the re-imaged pupil of the telescope, where the light from the holes is combined using the principle of stellar interferometry. While this can increase the resolution, it comes with a disadvantage. Due to the finite size of the holes, the majority of the starlight (typically > 80 %) is lost at the mask, thus limiting the signal-to-noise ratio (SNR) of the output images. This restriction of aperture masking only to the bright targets can be avoided using pupil remapping interferometry - a technique combining aperture masking interferometry and advances in photonic technologies using single-mode fibers. Due to the inherent spatial filtering properties, the single-mode fibers can be placed at the focal plane of the re-imaged pupil, allowing the utilization of the whole pupil of the telescope to produce a high-dynamic range along with high-resolution images. Thus, pupil remapping interferometry is one of the most promising application areas in the emerging field of astrophotonics.
At the heart of an interferometric facility, a beam combiner exists whose primary function is to combine light to obtain high-contrast fringes. A beam combiner can be as simple as a beam splitter or an anamorphic lens to combine light from 2 apertures (or telescopes) or as complex as a cascade of beam splitters and lenses to combine light for > 2 apertures. However, with the field of astrophotonics, interferometric facilities across the globe are increasingly employing some form of photonics technologies by using single-mode fibers or integrated optics (IO) chips as an efficient way to combine light from several apertures. The state-of-the-art instrument - GRAVITY at the very large telescope interferometer (VLTI) facility uses an IO-based beam combiner device reaching visibilities accuracy of better than < 0.25 %, which is roughly 50× as precise as a few decades back.
Therefore, in the context of IO-based components for applications in stellar interferometry, this Thesis describes the work towards the development of a 3-dimensional (3-D) IO device - a monolithic astrophotonics component containing both the pupil remappers and a discrete beam combiner (DBC). In this work, the pupil remappers are 3-D single-mode waveguides in a glass substrate collecting light from the re-imaged pupil of the telescope and feeding the light to a DBC, where the combination takes place. The DBC is a lattice of 3-D single-mode waveguides, which interact through evanescent coupling. By observing the output power of single-mode waveguides of the DBC, the visibilities are retrieved by using a calibrated transfer matrix ({U}) of the device.
The feasibility of the DBC in retrieving the visibilities theoretically and experimentally had already been studied in the literature but was only limited to laboratory tests with monochromatic light sources. Thus, a part of this work extends these studies by investigating the response of a 4-input DBC to a broad-band light source. Hence, the objectives of this Thesis are the following: 1) Design an IO device for broad-band light operation such that accurate and precise visibilities could be retrieved experimentally at astronomical H-band (1.5-1.65 μm), and 2) Validation of the DBC as a possible beam combination scheme for future interferometric facilities through on-sky testing at the William Herschel Telescope (WHT).
This work consisted of designing three different 3-D IO devices. One of the popular methods for fabricating 3-D photonic components in a glass substrate is ultra-fast laser inscription (ULI). Thus, manufacturing of the designed devices was outsourced to Politecnico di Milano as part of an iterative fabrication process using their state-of-the-art ULI facility. The devices were then characterized using a 2-beam Michelson interferometric setup obtaining both the monochromatic and polychromatic visibilities. The retrieved visibilities for all devices were in good agreement as predicted by the simulation results of a DBC, which confirms both the repeatability of the ULI process and the stability of the Michelson setup, thus fulfilling the first objective.
The best-performing device was then selected for the pupil-remapping of the WHT using a different optical setup consisting of a deformable mirror and a microlens array. The device successfully collected stellar photons from Vega and Altair. The visibilities were retrieved using a previously calibrated {U} but showed significant deviations from the expected results. Based on the analysis of comparable simulations, it was found that such deviations were primarily caused by the limited SNR of the stellar observations, thus constituting a first step towards the fulfillment of the second objective.
Extending synchrotron X-ray refraction techniques to the quantitative analysis of metallic materials
(2022)
In this work, two X-ray refraction based imaging methods, namely, synchrotron X-ray refraction radiography (SXRR) and synchrotron X-ray refraction computed tomography (SXRCT), are applied to analyze quantitatively cracks and porosity in metallic materials. SXRR and SXRCT make use of the refraction of X-rays at inner surfaces of the material, e.g., the surfaces of cracks and pores, for image contrast. Both methods are, therefore, sensitive to smaller defects than their absorption based counterparts X-ray radiography and computed tomography. They can detect defects of nanometric size. So far the methods have been applied to the analysis of ceramic materials and fiber reinforced plastics. The analysis of metallic materials requires higher photon energies to achieve sufficient X-ray transmission due to their higher density. This causes smaller refraction angles and, thus, lower image contrast because the refraction index depends on the photon energy. Here, for the first time, a conclusive study is presented exploring the possibility to apply SXRR and SXRCT to metallic materials. It is shown that both methods can be optimized to overcome the reduced contrast due to smaller refraction angles. Hence, the only remaining limitation is the achievable X-ray transmission which is common to all X-ray imaging methods. Further, a model for the quantitative analysis of the inner surfaces is presented and verified.
For this purpose four case studies are conducted each posing a specific challenge to the imaging task. Case study A investigates cracks in a coupon taken from an aluminum weld seam. This case study primarily serves to verify the model for quantitative analysis and prove the sensitivity to sub-resolution features. In case study B, the damage evolution in an aluminum-based particle reinforced metal-matrix composite is analyzed. Here, the accuracy and repeatability of subsequent SXRR measurements is investigated showing that measurement errors of less than 3 % can be achieved. Further, case study B marks the fist application of SXRR in combination with in-situ tensile loading. Case study C is out of the highly topical field of additive manufacturing. Here, porosity in additively manufactured Ti-Al6-V4 is analyzed with a special interest in the pore morphology. A classification scheme based on SXRR measurements is devised which allows to distinguish binding defects from keyhole pores even if the defects cannot be spatially resolved. In case study D, SXRCT is applied to the analysis of hydrogen assisted cracking in steel. Due to the high X-ray attenuation of steel a comparatively high photonenergy of 50 keV is required here. This causes increased noise and lower contrast in the data compared to the other case studies. However, despite the lower data quality a quantitative analysis of the occurance of cracks in dependence of hydrogen content and applied mechanical load is possible.
Weather extremes pose a persistent threat to society on multiple layers. Besides an average of ~37,000 deaths per year, climate-related disasters cause destroyed properties and impaired economic activities, eroding people's livelihoods and prosperity. While global temperature rises – caused by anthropogenic greenhouse gas emissions – the direct impacts of climatic extreme events increase and will further intensify without proper adaptation measures. Additionally, weather extremes do not only have local direct effects. Resulting economic repercussions can propagate either upstream or downstream along trade chains causing indirect effects. One approach to analyze these indirect effects within the complex global supply network is the agent-based model Acclimate. Using and extending this loss-propagation model, I focus in this thesis on three aspects of the relation between weather extremes and economic repercussions.
First, extreme weather events cause direct impacts on local economic performance. I compute daily local direct output loss time series of heat stress, river floods, tropical cyclones, and their consecutive occurrence using (near-future) climate projection ensembles. These regional impacts are estimated based on physical drivers and local productivity distribution. Direct effects of the aforementioned disaster categories are widely heterogeneous concerning regional and temporal distribution. As well, their intensity changes differently under future warming. Focusing on the hurricane-impacted capital, I find that long-term growth losses increase with higher heterogeneity of a shock ensemble.
Second, repercussions are sectorally and regionally distributed via economic ripples within the trading network, causing higher-order effects. I use Acclimate to identify three phases of those economic ripples. Furthermore, I compute indirect impacts and analyze overall regional and global production and consumption changes. Regarding heat stress, global consumer losses double while direct output losses increase by a factor 1.5 between 2000 – 2039. In my research I identify the effect of economic ripple resonance and introduce it to climate impact research. This effect occurs if economic ripples of consecutive disasters overlap, which increases economic responses such as an enhancement of consumption losses. These loss enhancements can even be more amplified with increasing direct output losses, e.g. caused by climate crises.
Transport disruptions can cause economic repercussions as well. For this, I extend the model Acclimate with a geographical transportation route and expand the decision horizon of economic agents. Using this, I show that policy-induced sudden trade restrictions (e.g. a no-deal Brexit) can significantly reduce the longer-term economic prosperity of affected regions. Analyses of transportation disruptions in typhoon seasons indicate that severely affected regions must reduce production as demand falls during a storm. Substituting suppliers may compensate for fluctuations at the beginning of the storm, which fails for prolonged disruptions.
Third, possible coping mechanisms and adaptation strategies arise from direct and indirect economic responses to weather extremes. Analyzing annual trade changes due to typhoon-induced transport disruptions depict that overall exports rise. This trade resilience increases with higher network node diversification. Further, my research shows that a basic insurance scheme may diminish hurricane-induced long-term growth losses due to faster reconstruction in disasters aftermaths. I find that insurance coverage could be an economically reasonable coping scheme towards higher losses caused by the climate crisis. Indirect effects within the global economic network from weather extremes indicate further adaptation possibilities. For one, diversifying linkages reduce the hazard of sharp price increases. Next to this, close economic interconnections with regions that do not share the same extreme weather season can be economically beneficial in the medium run. Furthermore, economic ripple resonance effects should be considered while computing costs. Overall, an increase in local adaptation measures reduces economic ripples within the trade network and possible losses elsewhere. In conclusion, adaptation measures are necessary and potential present, but it seems rather not possible to avoid all direct or indirect losses.
As I show in this thesis, dynamical modeling gives valuable insights into how direct and indirect economic impacts arise from different categories of weather extremes. Further, it highlights the importance of resolving individual extremes and reflecting amplifying effects caused by incomplete recovery or consecutive disasters.
In the present thesis I investigate the lattice dynamics of thin film hetero structures of magnetically ordered materials upon femtosecond laser excitation as a probing and manipulation scheme for the spin system. The quantitative assessment of laser induced thermal dynamics as well as generated picosecond acoustic pulses and their respective impact on the magnetization dynamics of thin films is a challenging endeavor. All the more, the development and implementation of effective experimental tools and comprehensive models are paramount to propel future academic and technological progress.
In all experiments in the scope of this cumulative dissertation, I examine the crystal lattice of nanoscale thin films upon the excitation with femtosecond laser pulses. The relative change of the lattice constant due to thermal expansion or picosecond strain pulses is directly monitored by an ultrafast X-ray diffraction (UXRD) setup with a femtosecond laser-driven plasma X-ray source (PXS). Phonons and spins alike exert stress on the lattice, which responds according to the elastic properties of the material, rendering the lattice a versatile sensor for all sorts of ultrafast interactions. On the one hand, I investigate materials with strong magneto-elastic properties; The highly magnetostrictive rare-earth compound TbFe2, elemental Dysprosium or the technological relevant Invar material FePt. On the other hand I conduct a comprehensive study on the lattice dynamics of Bi1Y2Fe5O12 (Bi:YIG), which exhibits high-frequency coherent spin dynamics upon femtosecond laser excitation according to the literature. Higher order standing spinwaves (SSWs) are triggered by coherent and incoherent motion of atoms, in other words phonons, which I quantified with UXRD. We are able to unite the experimental observations of the lattice and magnetization dynamics qualitatively and quantitatively. This is done with a combination of multi-temperature, elastic, magneto-elastic, anisotropy and micro-magnetic modeling.
The collective data from UXRD, to probe the lattice, and time-resolved magneto-optical Kerr effect (tr-MOKE) measurements, to monitor the magnetization, were previously collected at different experimental setups. To improve the precision of the quantitative assessment of lattice and magnetization dynamics alike, our group implemented a combination of UXRD and tr-MOKE in a singular experimental setup, which is to my knowledge, the first of its kind. I helped with the conception and commissioning of this novel experimental station, which allows the simultaneous observation of lattice and magnetization dynamics on an ultrafast timescale under identical excitation conditions. Furthermore, I developed a new X-ray diffraction measurement routine which significantly reduces the measurement time of UXRD experiments by up to an order of magnitude. It is called reciprocal space slicing (RSS) and utilizes an area detector to monitor the angular motion of X-ray diffraction peaks, which is associated with lattice constant changes, without a time-consuming scan of the diffraction angles with the goniometer. RSS is particularly useful for ultrafast diffraction experiments, since measurement time at large scale facilities like synchrotrons and free electron lasers is a scarce and expensive resource. However, RSS is not limited to ultrafast experiments and can even be extended to other diffraction techniques with neutrons or electrons.
The Greenland Ice Sheet is the second-largest mass of ice on Earth. Being almost 2000 km long, more than 700 km wide, and more than 3 km thick at the summit, it holds enough ice to raise global sea levels by 7m if melted completely. Despite its massive size, it is particularly vulnerable to anthropogenic climate change: temperatures over the Greenland Ice Sheet have increased by more than 2.7◦C in the past 30 years, twice as much as the global mean temperature. Consequently, the ice sheet has been significantly losing mass since the 1980s and the rate of loss has increased sixfold since then. Moreover, it is one of the potential tipping elements of the Earth System, which might undergo irreversible change once a warming threshold is exceeded. This thesis aims at extending the understanding of the resilience of the Greenland Ice Sheet against global warming by analyzing processes and feedbacks relevant to its centennial to multi-millennial stability using ice sheet modeling.
One of these feedbacks, the melt-elevation-feedback is driven by the temperature rise with decreasing altitudes: As the ice sheet melts, its thickness and surface elevation decrease, exposing the ice surface to warmer air and thus increasing the melt rates even further. The glacial isostatic adjustment (GIA) can partly mitigate this melt-elevation feedback as the bedrock lifts in response to an ice load decrease, forming the negative GIA feedback. In my thesis, I show that the interaction between these two competing feedbacks can lead to qualitatively different dynamical responses of the Greenland Ice Sheet to warming – from permanent loss to incomplete recovery, depending on the feedback parameters. My research shows that the interaction of those feedbacks can initiate self-sustained oscillations of the ice volume while the climate forcing remains constant.
Furthermore, the increased surface melt changes the optical properties of the snow or ice surface, e.g. by lowering their albedo, which in turn enhances melt rates – a process known as the melt-albedo feedback. Process-based ice sheet models often neglect this melt-albedo feedback. To close this gap, I implemented a simplified version of the diurnal Energy Balance Model, a computationally efficient approach that can capture the first-order effects of the melt-albedo feedback, into the Parallel Ice Sheet Model (PISM). Using the coupled model, I show in warming experiments that the melt-albedo feedback almost doubles the ice loss until the year 2300 under the low greenhouse gas emission scenario RCP2.6, compared to simulations where the melt-albedo feedback is neglected,
and adds up to 58% additional ice loss under the high emission scenario RCP8.5. Moreover, I find that the melt-albedo feedback dominates the ice loss until 2300, compared to the melt-elevation feedback.
Another process that could influence the resilience of the Greenland Ice Sheet is the warming induced softening of the ice and the resulting increase in flow. In my thesis, I show with PISM how the uncertainty in Glen’s flow law impacts the simulated response to warming. In a flow line setup at fixed climatic mass balance, the uncertainty in flow parameters leads to a range of ice loss comparable to the range caused by different warming levels.
While I focus on fundamental processes, feedbacks, and their interactions in the first three projects of my thesis, I also explore the impact of specific climate scenarios on the sea level rise contribution of the Greenland Ice Sheet. To increase the carbon budget flexibility, some warming scenarios – while still staying within the limits of the Paris Agreement – include a temporal overshoot of global warming. I show that an overshoot by 0.4◦C increases the short-term and long-term ice loss from Greenland by several centimeters. The long-term increase is driven by the warming at high latitudes, which persists even when global warming is reversed. This leads to a substantial long-term commitment of the sea level rise contribution from the Greenland Ice Sheet.
Overall, in my thesis I show that the melt-albedo feedback is most relevant for the ice loss of the Greenland Ice Sheet on centennial timescales. In contrast, the melt-elevation feedback and its interplay with the GIA feedback become increasingly relevant on millennial timescales. All of these influence the resilience of the Greenland Ice Sheet against global warming, in the near future and on the long term.
The current generation of ground-based instruments has rapidly extended the limits of the range accessible to us with very-high-energy (VHE) gamma-rays, and more than a hundred sources have now been detected in the Milky Way. These sources represent only the tip of the iceberg, but their number has reached a level that allows population studies. In this work, a model of the global population of VHE gamma-ray sources based on the most comprehensive census of Galactic sources in this energy regime, the H.E.S.S. Galactic plane survey (HGPS), will be presented. A population synthesis approach was followed in the construction of the model. Particular attention was paid to correcting for the strong observational bias inherent in the sample of detected sources. The methods developed for estimating the model parameters have been validated with extensive Monte Carlo simulations and will be shown to provide unbiased estimates of the model parameters. With these methods, five models for different spatial distributions of sources have been constructed. To test the validity of these models, their predictions for the composition of sources within the sensitivity range of the HGPS are compared with the observed sample. With one exception, similar results are obtained for all spatial distributions, showing that the observed longitude profile and the source distribution over photon flux are in fair agreement with observation. Regarding the latitude profile and the source distribution over angular extent, it becomes apparent that the model needs to be further adjusted to bring its predictions in agreement with observation. Based on the model, predictions of the global properties of the Galactic population of VHE gamma-ray sources and the prospects of the Cherenkov Telescope Array (CTA) will be presented.
CTA will significantly increase our knowledge of VHE gamma-ray sources by lowering the threshold for source detection, primarily through a larger detection area compared to current-generation instruments. In ground-based gamma-ray astronomy, the sensitivity of an instrument depends strongly, in addition to the detection area, on the ability to distinguish images of air showers produced by gamma-rays from those produced by cosmic rays, which are a strong background. This means that the number of detectable sources depends on the background rejection algorithm used and therefore may also be increased by improving the performance of such algorithms. In this context, in addition to the population model, this work presents a study on the application of deep-learning techniques to the task of gamma-hadron separation in the analysis of data from ground-based gamma-ray instruments. Based on a systematic survey of different neural-network architectures, it is shown that robust classifiers can be constructed with competitive performance compared to the best existing algorithms. Despite the broad coverage of neural-network architectures discussed, only part of the potential offered by the
application of deep-learning techniques to the analysis of gamma-ray data is exploited in the context of this study. Nevertheless, it provides an important basis for further research on this topic.