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- Datenassimilation (2)
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- Institut für Physik und Astronomie (26) (entfernen)
In der vorliegenden Arbeit wird untersucht, in wie weit physikalische Experimente ein flow-Erleben bei Lernenden hervorrufen. Flow-Erleben wird als Motivationsursache gesehen und soll den Weg zu Freude und Glück darstellen. Insbesondere wegen dem oft zitierten Fachkräftemangel in naturwissenschaftlichen und technischen Berufen ist eine Motivationssteigerung in naturwissenschaftlichen Unterrichtsfächern wichtig. Denn trotz Leistungssteigerungen in internationalen Vergleichstests möchten in Deutschland deutlich weniger Schüler*innen einen solchen Beruf ergreifen als in anderen Industriestaaten. Daher gilt es, möglichst früh Schüler*innen für naturwissenschaftlich-technische Fächer zu begeistern und insbesondere im regelrecht verhassten Physikunterricht flow-Erleben zu erzeugen.
Im Rahmen dieser Arbeit wird das flow-Erleben von Studierenden in klassischen Laborexperimenten und FELS (Forschend-Entdeckendes Lernen mit dem Smartphone) als Lernumgebung untersucht. FELS ist eine an die Lebenswelt der Schüler*innen angepasste Lernumgebung, in der sie mit Smartphones ihre eigene Lebenswelt experimentell untersuchen.
Es zeigt sich, dass sowohl klassische Laborexperimente als auch in der Lebenswelt durchgeführte, smartphonebasierte Experimente flow-Erleben erzeugen. Allerdings verursachen die smartphonebasierten Experimente kaum Stressgefühle.
Die in dieser Arbeit herausgefundenen Ergebnisse liefern einen ersten Ansatz, der durch Folgestudien erweitert werden sollte.
The plasmasphere is a dynamic region of cold, dense plasma surrounding the Earth. Its shape and size are highly susceptible to variations in solar and geomagnetic conditions. Having an accurate model of plasma density in the plasmasphere is important for GNSS navigation and for predicting hazardous effects of radiation in space on spacecraft. The distribution of cold plasma and its dynamic dependence on solar wind and geomagnetic conditions remain, however, poorly quantified. Existing empirical models of plasma density tend to be oversimplified as they are based on statistical averages over static parameters. Understanding the global dynamics of the plasmasphere using observations from space remains a challenge, as existing density measurements are sparse and limited to locations where satellites can provide in-situ observations. In this dissertation, we demonstrate how such sparse electron density measurements can be used to reconstruct the global electron density distribution in the plasmasphere and capture its dynamic dependence on solar wind and geomagnetic conditions.
First, we develop an automated algorithm to determine the electron density from in-situ measurements of the electric field on the Van Allen Probes spacecraft. In particular, we design a neural network to infer the upper hybrid resonance frequency from the dynamic spectrograms obtained with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite, which is then used to calculate the electron number density. The developed Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm is applied to more than four years of EMFISIS measurements to produce the publicly available electron density data set.
We utilize the obtained electron density data set to develop a new global model of plasma density by employing a neural network-based modeling approach. In addition to the location, the model takes the time history of geomagnetic indices and location as inputs, and produces electron density in the equatorial plane as an output. It is extensively validated using in-situ density measurements from the Van Allen Probes mission, and also by comparing the predicted global evolution of the plasmasphere with the global IMAGE EUV images of He+ distribution. The model successfully reproduces erosion of the plasmasphere on the night side as well as plume formation and evolution, and agrees well with data.
The performance of neural networks strongly depends on the availability of training data, which is limited during intervals of high geomagnetic activity. In order to provide reliable density predictions during such intervals, we can employ physics-based modeling. We develop a new approach for optimally combining the neural network- and physics-based models of the plasmasphere by means of data assimilation. The developed approach utilizes advantages of both neural network- and physics-based modeling and produces reliable global plasma density reconstructions for quiet, disturbed, and extreme geomagnetic conditions.
Finally, we extend the developed machine learning-based tools and apply them to another important problem in the field of space weather, the prediction of the geomagnetic index Kp. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere, the radiation belts and the plasmasphere. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast and make short-term predictions of Kp, basing their inferences on the recent history of Kp and solar wind measurements at L1. We analyze how the performance of neural networks compares to other machine learning algorithms for nowcasting and forecasting Kp for up to 12 hours ahead. Additionally, we investigate several machine learning and information theory methods for selecting the optimal inputs to a predictive model of Kp. The developed tools for feature selection can also be applied to other problems in space physics in order to reduce the input dimensionality and identify the most important drivers.
Research outlined in this dissertation clearly demonstrates that machine learning tools can be used to develop empirical models from sparse data and also can be used to understand the underlying physical processes. Combining machine learning, physics-based modeling and data assimilation allows us to develop novel methods benefiting from these different approaches.
In the present study, we employ the angle-resolved photoemission spectroscopy (ARPES) technique to study the electronic structure of topological states of matter. In particular, the so-called topological crystalline insulators (TCIs) Pb1-xSnxSe and Pb1-xSnxTe, and the Mn-doped Z2 topological insulators (TIs) Bi2Te3 and Bi2Se3. The Z2 class of strong topological insulators is protected by time-reversal symmetry and is characterized by an odd number of metallic Dirac type surface states in the surface Brillouin zone. The topological crystalline insulators on the other hand are protected by the individual crystal symmetries and exhibit an even number of Dirac cones.
The topological properties of the lead tin chalcogenides topological crystalline insulators can be tuned by temperature and composition. Here, we demonstrate that Bi-doping of the Pb1-xSnxSe(111) epilayers induces a quantum phase transition from a topological crystalline insulator to a Z2 topological insulator. This occurs because Bi-doping lifts the fourfold valley degeneracy in the bulk. As a consequence a gap appears at ⌈¯, while the three Dirac cones at the M̅ points of the surface Brillouin zone remain intact. We interpret this new phase transition is caused by lattice distortion. Our findings extend the topological phase diagram enormously and make strong topological insulators switchable by distortions or electric field. In contrast, the bulk Bi doping of epitaxial Pb1-xSnxTe(111) films induces a giant Rashba splitting at the surface that can be tuned by the doping level. Tight binding calculations identify their origin as Fermi level pinning by trap states at the surface.
Magnetically doped topological insulators enable the quantum anomalous Hall effect (QAHE) which provide quantized edge states for lossless charge transport applications. The edge states are hosted by a magnetic energy gap at the Dirac point which has not been experimentally observed to date. Our low temperature ARPES studies unambiguously reveal the magnetic gap of Mn-doped Bi2Te3. Our analysis shows a five times larger gap size below the Tc than theoretically predicted. We assign this enhancement to a remarkable structure modification induced by Mn doping. Instead of a disordered impurity system, a self-organized alternating sequence of MnBi2Te4 septuple and Bi2Te3quintuple layers is formed. This enhances the wave-function overlap and gives rise to a large magnetic gap. Mn-doped Bi2Se3 forms similar heterostructure, but only a nonmagnetic gap is observed in this system. This correlates with the difference in magnetic anisotropy due to the much larger spin-orbit interaction in Bi2Te3 compared to Bi2Se3. These findings provide crucial insights for pushing lossless transport in topological insulators towards room-temperature applications.
The Earth's inner magnetosphere is a very dynamic system, mostly driven by the external solar wind forcing exerted upon the magnetic field of our planet. Disturbances in the solar wind, such as coronal mass ejections and co-rotating interaction regions, cause geomagnetic storms, which lead to prominent changes in charged particle populations of the inner magnetosphere - the plasmasphere, ring current, and radiation belts. Satellites operating in the regions of elevated energetic and relativistic electron fluxes can be damaged by deep dielectric or surface charging during severe space weather events. Predicting the dynamics of the charged particles and mitigating their effects on the infrastructure is of particular importance, due to our increasing reliance on space technologies.
The dynamics of particles in the plasmasphere, ring current, and radiation belts are strongly coupled by means of collisions and collisionless interactions with electromagnetic fields induced by the motion of charged particles. Multidimensional numerical models simplify the treatment of transport, acceleration, and loss processes of these particles, and allow us to predict how the near-Earth space environment responds to solar storms. The models inevitably rely on a number of simplifications and assumptions that affect model accuracy and complicate the interpretation of the results. In this dissertation, we quantify the processes that control electron dynamics in the inner magnetosphere, paying particular attention to the uncertainties of the employed numerical codes and tools.
We use a set of convenient analytical solutions for advection and diffusion equations to test the accuracy and stability of the four-dimensional Versatile Electron Radiation Belt (VERB-4D) code. We show that numerical schemes implemented in the code converge to the analytical solutions and that the VERB-4D code demonstrates stable behavior independent of the assumed time step. The order of the numerical scheme for the convection equation is demonstrated to affect results of ring current and radiation belt simulations, and it is crucially important to use high-order numerical schemes to decrease numerical errors in the model.
Using the thoroughly tested VERB-4D code, we model the dynamics of the ring current electrons during the 17 March 2013 storm. The discrepancies between the model and observations above 4.5 Earth's radii can be explained by uncertainties in the outer boundary conditions. Simulation results indicate that the electrons were transported from the geostationary orbit towards the Earth by the global-scale electric and magnetic fields.
We investigate how simulation results depend on the input models and parameters. The model is shown to be particularly sensitive to the global electric field and electron lifetimes below 4.5 Earth's radii. The effects of radial diffusion and subauroral polarization streams are also quantified.
We developed a data-assimilative code that blends together a convection model of energetic electron transport and loss and Van Allen Probes satellite data by means of the Kalman filter. We show that the Kalman filter can correct model uncertainties in the convection electric field, electron lifetimes, and boundary conditions. It is also demonstrated how the innovation vector - the difference between observations and model prediction - can be used to identify physical processes missing in the model of energetic electron dynamics.
We computed radial profiles of phase space density of ultrarelativistic electrons, using Van Allen Probes measurements. We analyze the shape of the profiles during geomagnetically quiet and disturbed times and show that the formation of new local minimums in the radial profiles coincides with the ground observations of electromagnetic ion-cyclotron (EMIC) waves. This correlation indicates that EMIC waves are responsible for the loss of ultrarelativistic electrons from the heart of the outer radiation belt into the Earth's atmosphere.
In the last decade the photovoltaic research has been preponderantly overturned by the arrival of metal halide perovskites. The introduction of this class of materials in the academic research for renewable energy literally shifted the focus of a large number of research groups and institutions. The attractiveness of halide perovskites lays particularly on their skyrocketing efficiencies and relatively simple and cheap fabrication methods. Specifically, the latter allowed for a quick development of this research in many universities and institutes around the world at the same time. The outcome has been a fast and beneficial increase in knowledge with a consequent terrific improvement of this new technology. On the other side, the enormous amount of research promoted an immense outgrowth of scientific literature, perpetually published. Halide perovskite solar cells are now effectively competing with other established photovoltaic technologies in terms of power conversion efficiencies and production costs. Despite the tremendous improvement, a thorough understanding of the energy losses in these systems is of imperative importance to unlock the full thermodynamic potential of this material. This thesis focuses on the understanding of the non-radiative recombination processes in the neat perovskite and in complete devices. Specifically, photoluminescence quantum yield (PLQY) measurements were applied to multilayer stacks and cells under different illumination conditions to accurately determine the quasi-Fermi levels splitting (QFLS) in the absorber, and compare it with the external open-circuit voltage of the device (V_OC). Combined with drift-diffusion simulations, this approach allowed us to pinpoint the sites of predominant recombination, but also to investigate the dynamics of the underlying processes. As such, the internal and external ideality factors, associated to the QFLS and V_OC respectively, are studied with the aim of understanding the type of recombination processes taking place in the multilayered architecture of the device. Our findings highlight the failure of the equality between QFLS and V_OC in the case of strong interface recombination, as well as the detrimental effect of all commonly used transport layers in terms of V_OC losses. In these regards, we show how, in most perovskite solar cells, different recombination processes can affect the internal QFLS and the external V_OC and that interface recombination dictates the V_OC losses. This line of arguments allowed to rationalize that, in our devices, the external ideality factor is completely dominated by interface recombination, and that this process can alone be responsible for values of the ideality factor between 1 and 2, typically observed in perovskite solar cells. Importantly, our studies demonstrated how strong interface recombination can lower the ideality factor towards values of 1, often misinterpreted as pure radiative second order recombination. As such, a comprehensive understanding of the recombination loss mechanisms currently limiting the device performance was achieved. In order to reach the full thermodynamic potential of the perovskite absorber, the interfaces of both the electron and hole transport layers (ETL/HTL) must be properly addressed and improved. From here, the second part of the research work is devoted on reducing the interfacial non-radiative energy losses by optimizing the structure and energetics of the relevant interface in our solar cell devices, with the aim of bringing their quasi-Fermi level splitting closer to its radiative limit. As such, the interfaces have been carefully addressed and optimized with different methodologies. First, a small amount of Sr is added into the perovskite precursor solution with the effect of effectively reducing surface and interface recombination. In this case, devices with V_OC up to 1.23 V were achieved and the energy losses were minimized to as low as 100 meV from the radiative limit of the material. Through a combination of different methods, we showed that these improvements are related to a strong n-type surface doping, which repels the holes in the perovskite from the surface and the interface with the ETL. Second, a more general device improvement was achieved by depositing a defect-passivating poly(ionic-liquid) layer on top of the perovskite absorber. The resulting devices featured a concomitant improvement of the V_OC and fill factor, up to 1.17 V and 83% respectively, reaching efficiency as high as 21.4%. Moreover, the protecting polymer layer helped to enhance the stability of the devices under prolonged maximum power point tracking measurements. Lastly, PLQY measurements are used to investigate the recombination mechanisms in halide-segregated large bandgap perovskite materials. Here, our findings showed how few iodide-rich low-energy domains act as highly efficient radiative recombination centers, capable of generating PLQY values up to 25%. Coupling these results with a detailed microscopic cathodoluminescence analysis and absorption profiles allowed to demonstrate how the emission from these low energy domains is due to the trapping of the carriers photogenerated in the Br-rich high-energy domains. Thereby, the strong implications of this phenomenon are discussed in relation to the failure of the optical reciprocity between absorption and emission and on the consequent applicability of the Shockley-Queisser theory for studying the energy losses such systems. In conclusion, the identification and quantification of the non-radiative QFLS and V_OC losses provided a base knowledge of the fundamental limitation of perovskite solar cells and served as guidance for future optimization and development of this technology. Furthermore, by providing practical examples of solar cell improvements, we corroborated the correctness of our fundamental understanding and proposed new methodologies to be further explored by new generations of scientists.
During a dark night, it is possible to observe thousands of stars by eye. All these stars are located within the Milky Way, our home. Not all stars are the same, they can have different sizes, masses, temperatures and ages. Heavy stars do not live long (in astronomical terms), only a few million years, but stars less massive than the Sun can get more than ten billion years old. Such small stars that formed in the beginning of the Universe still shine today. These ancient stars are very helpful to learn more about the early Universe, the First Stars and the history of the Milky Way. But how do you recognise an ancient star? Using their chemical fingerprints! In the beginning of the Universe, there were only two chemical elements: hydrogen and helium (and a tiny bit of lithium). All the heavier elements like carbon, calcium and iron were only made later within stars and their explosions. The amount of chemical elements in the Universe increases with the number of stars that are born, evolve and explode. Stars that form later are born with more heavy elements, or a greater metallicity. In the field of astronomy that is called “Galactic Archaeology”, stars of various metallicities are used to study the history of the Milky Way. In this doctoral thesis, the focus is on metal-poor stars because these are expected to be the oldest and can therefore tell us a lot about the early history of our Galaxy.
Until today, we still have not discovered a metal-free star. The most metal-poor stars, however, give us important insights in the lives and deaths of the First Stars. Many of the oldest, most metal-poor stars have an unexpectedly large amount of carbon, compared to for example iron. These carbon-enhanced metal-poor (CEMP) stars tell us something about the very first stars in the Universe: they somehow produced a lot of carbon. If we look at the precise chemical fingerprints of the CEMP stars, we can learn a lot more. But our interpretation depends on the assumption that the chemical fingerprint of a star does not change during its life. In this thesis, new data is presented that shows that this assumption may be too simple: many extremely metal-poor CEMP stars are members of binary systems. Interactions between two stars in a binary system can pollute the surface of the stars. Likely not all of the CEMP stars in binary systems were actually polluted, but we should be very careful in our interpretations of the fingerprints of these stars.
The CEMP stars and other metal-poor stars are also important for our understanding of the early history of the Milky Way. Most researchers who study metal-poor stars look for these stars in the halo of the Milky Way: a huge diffuse Galactic component containing about 1% of the stars in our Galaxy. However, models predict that the oldest metal-poor stars are located in the center of the Milky Way, in the bulge. The metal-poor inner Galaxy is unfortunately difficult to study due to large amounts of dust between us and the center and an overwhelming majority of metal-rich stars. This thesis presents results from the successful Pristine Inner Galaxy Survey (PIGS), a new survey looking for (and finding) the oldest stars in the bulge of the Milky Way. PIGS is using images with a specific color that is sensitive to the metallicity of stars, and can therefore efficiently select the metal-poor stars among millions of other, more metal-rich stars. The interesting candidates are followed up with spectroscopy, which is then analysed using two independent methods. With this strategy, PIGS has discovered the largest sample of metal-poor stars in the inner Galaxy to date. A new result from the PIGS data is that the metal-poor stars rotate more slowly around the Galactic center compared to the more metal-rich stars, and they show larger randomness in their motions as well. Another important contribution from PIGS is the discovery of tens of CEMP stars in the inner Galaxy, where previously only two such stars were known.
The new results from this thesis help us to understand the First Stars and the early history of the Milky Way. Ongoing and future large surveys will provide us with a lot of additional data in the coming years. It is an exciting time for the field of Galactic Archaeology.
Living cells rely on transport and interaction of biomolecules to perform their diverse functions. A powerful toolbox to study these highly dynamic processes in the native environment is provided by fluorescence fluctuation spectroscopy (FFS) techniques. In more detail, FFS takes advantage of the inherent dynamics present in biological systems, such as diffusion, to infer molecular parameters from fluctuations of the signal emitted by an ensemble of fluorescently tagged molecules. In particular, two parameters are accessible: the concentration of molecules and their transit times through the observation volume. In addition, molecular interactions can be measured by analyzing the average signal emitted per molecule - the molecular brightness - and the cross-correlation of signals detected from differently tagged species.
In the present work, several FFS techniques were implemented and applied in different biological contexts. In particular, scanning fluorescence correlation spectroscopy (sFCS) was performed to measure protein dynamics and interactions at the plasma membrane (PM) of cells, and number and brightness (N&B) analysis to spatially map molecular aggregation. To account for technical limitations and sample related artifacts, e.g. detector noise, photobleaching, or background signal, several correction schemes were explored. In addition, sFCS was combined with spectral detection and higher moment analysis of the photon count distribution to resolve multiple species at the PM.
Using scanning fluorescence cross-correlation spectroscopy and cross-correlation N&B, the interactions of amyloid precursor-like protein 1 (APLP1), a synaptic membrane protein, were investigated. It is shown for the first time directly in living cells, that APLP1 undergoes specific interactions at cell-cell contacts. It is further demonstrated that zinc ions induce formation of large APLP1 clusters that enrich at contact sites and bind to clusters on the opposing cell. Altogether, these results provide direct evidence that APLP1 is a zinc ion dependent neuronal adhesion protein.
In the context of APLP1, discrepancies of oligomeric state estimates were observed, which were attributed to non-fluorescent states of the chosen red fluorescent protein (FP) tag mCardinal (mCard). Therefore, multiple FPs and their performance in FFS based measurements of protein interactions were systematically evaluated. The study revealed superior properties of monomeric enhanced green fluorescent protein (mEGFP) and mCherry2. Furthermore, a simple correction scheme allowed unbiased in situ measurements of protein oligomerization by quantifying non-fluorescent state fractions of FP tags. The procedure was experimentally confirmed for biologically relevant protein complexes consisting of up to 12 monomers.
In the last part of this work, fluorescence correlation spectroscopy (FCS) and single particle tracking (SPT) were used to characterize diffusive transport dynamics in a bacterial biofilm model. Biofilms are surface adherent bacterial communities, whose structural organization is provided by extracellular polymeric substances (EPS) that form a viscous polymer hydrogel. The presented study revealed a probe size and polymer concentration dependent (anomalous) diffusion hindrance in a reconstituted EPS matrix system caused by polymer chain entanglement at physiological concentrations. This result indicates a meshwork-like organization of the biofilm matrix that allows free diffusion of small particles, but strongly hinders diffusion of larger particles such as bacteriophages. Finally, it is shown that depolymerization of the matrix by phage derived enzymes rapidly facilitated free diffusion. In the context of phage infections, such enzymes may provide a key to evade trapping in the biofilm matrix and promote efficient infection of bacteria. In combination with phage application, matrix depolymerizing enzymes may open up novel antimicrobial strategies against multiresistant bacterial strains, as a promising, more specific alternative to conventional antibiotics.
The field of gamma-ray astronomy opened a new window into the non-thermal universe that allows studying the acceleration sites of cosmic rays and the role of cosmic rays on evolutionary processes in galaxies. The detection of almost one hundred Galactic very-high-energy (VHE: 0.1−100TeV) gamma-ray sources in the Milky Way demonstrates that particle acceleration up to tens of TeV energies is a common phenomenon. Furthermore, the detection of VHE gamma rays from other galaxies has confirmed that cosmic rays are not exclusively accelerated in the Milky Way. The rapid development of gamma-ray astronomy in the past two decades has led to a transition from the detection and study of individual sources to source population studies. To answer the question, whether the VHE gamma-ray source population of the Milky Way is unique, observations of galaxies, for which individual sources can be resolved, are required. Such galaxies are the Magellanic Clouds, two satellite galaxies of the Milky Way, which have been surveyed by the H.E.S.S. experiment in the last decade. In this thesis, data from a total of 450 hours of H.E.S.S. observations towards the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) are presented. During the analysis of the data sets, special emphasis is put on the evaluation of systematic uncertainties of the experiment in order to assure an unbiased flux estimation of the potential VHE gamma-ray sources of the Magellanic Clouds. A detailed analysis of the survey data revealed the detection of the gamma-ray binary LMCP3, the most powerful gamma-ray binary known so far, that is located in the LMC, and thus, increases the number of known VHE gamma-ray sources in the LMC to four. No other VHE gamma-ray source is detected in the Magellanic Clouds and integral flux upper limits are estimated. These flux upper limits are used to perform a source population study based on known VHE source classes and existing multi-wavelength catalogues. A comparison of the source populations of the Magellanic Clouds and the Milky Way revealed that no other source in the Magellanic Clouds is as bright as the most luminous VHE gamma-ray source in the LMC: the pulsar wind nebula N 157B, and that one-third of the source population of the Magellanic Clouds is less luminous than the other known VHE gamma-ray sources in the LMC. For only a couple of sources luminosity levels of Galactic VHE sources, that are more than one order of magnitude fainter than the detected sources in the LMC, are constrained. Based on the flux upper limits, differences on the TeV source populations in the Magellanic Clouds and the Milky Way as well as the importance of the source environments will be discussed.
The Milky Way is a spiral galaxy consisting of a disc of gas, dust and stars embedded in a halo of dark matter. Within this dark matter halo there is also a diffuse population of stars called the stellar halo, that has been accreting stars for billions of years from smaller galaxies that get pulled in and disrupted by the large gravitational potential of the Milky Way. As they are disrupted, these galaxies leave behind long streams of stars that can take billions of years to mix with the rest of the stars in the halo. Furthermore, the amount of heavy elements (metallicity) of the stars in these galaxies reflects the rate of chemical enrichment that occurred in them, since the Universe has been slowly enriched in heavy elements (e.g. iron) through successive generations of stars which produce them in their cores and supernovae explosions. Therefore, stars that contain small amounts of heavy elements (metal-poor stars) either formed at early times before the Universe was significantly enriched, or in isolated environments. The aim of this thesis is to develop a better understanding of the substructure content and chemistry of the Galactic stellar halo, in order to gain further insight into the formation and evolution of the Milky Way.
The Pristine survey uses a narrow-band filter which specifically targets the Ca II H & K spectral absorption lines to provide photometric metallicities for a large number of stars down to the extremely metal-poor (EMP) regime, making it a very powerful data set for Galactic archaeology studies. In Chapter 2, we quantify the efficiency of the survey using a preliminary spectroscopic follow-up sample of ~ 200 stars. We also use this sample to establish a set of selection criteria to improve the success rate of selecting EMP candidates for follow-up spectroscopy. In Chapter 3, we extend this work and present the full catalogue of ~ 1000 stars from a three year long medium resolution spectroscopic follow-up effort conducted as part of the Pristine survey. From this sample, we compute success rates of 56% and 23% for recovering stars with [Fe/H] < -2.5 and [Fe/H] < -3.0, respectively. This demonstrates a high efficiency for finding EMP stars as compared to previous searches with success rates of 3-4%.
In Chapter 4, we select a sample of ~ 80000 halo stars using colour and magnitude cuts to select a main sequence turnoff population in the distance range 6 < dʘ < 20 kpc. We then use the spectroscopic follow-up sample presented in Chapter 3 to statistically rescale the Pristine photometric metallicities of this sample, and present the resulting corrected metallicity distribution function (MDF) of the halo. The slope at the metal-poor end is significantly shallower than previous spectroscopic efforts have shown, suggesting that there may be more metal-poor stars with [Fe/H] < -2.5 in the halo than previously thought. This sample also shows evidence that the MDF of the halo may not be bimodal as was proposed by previous works, and that the lack of globular clusters in the Milky Way may be the result of a physical truncation of the MDF rather than just statistical under-sampling.
Chapter 5 showcases the unexpected capability of the Pristine filter for separating blue horizontal branch (BHB) stars from Blue Straggler (BS) stars. We demonstrate a purity of 93% and completeness of 91% for identifying BHB stars, a substantial improvement over previous works. We then use this highly pure and complete sample of BHB stars to trace the halo density profile out to d > 100 kpc, and the Sagittarius stream substructure out to ~ 130 kpc.
In Chapter 6 we use the photometric metallicities from the Pristine survey to perform a clustering analysis of the halo as a function of metallicity. Separating the Pristine sample into four metallicity bins of [Fe/H] < -2, -2 < [Fe/H] < -1.5, -1.5 < [Fe/H] < -1 and -0.9 < [Fe/H] < -0.8, we compute the two-point correlation function to measure the amount of clustering on scales of < 5 deg. For a smooth comparison sample we make a mock Pristine data set generated using the Galaxia code based on the Besançon model of the Galaxy. We find enhanced clustering on small scales (< 0.5 deg) for some regions of the Galaxy for the most metal-poor bin ([Fe/H] < -2), while in others we see large scale signals that correspond to known substructures in those directions. This confirms that the substructure content of the halo is highly anisotropic and diverse in different Galactic environments. We discuss the difficulties of removing systematic clustering signals from the data and the limitations of disentangling weak clustering signals from real substructures and residual systematic structure in the data.
Taken together, the work presented in this thesis approaches the problem of better understanding the halo of our Galaxy from multiple angles. Firstly, presenting a sizeable sample of EMP stars and improving the selection efficiency of EMP stars for the Pristine survey, paving the way for the further discovery of metal-poor stars to be used as probes to early chemical evolution. Secondly, improving the selection of BHB distance tracers to map out the halo to large distances, and finally, using the large samples of metal-poor stars to derive the MDF of the inner halo and analyse the substructure content at different metallicities. The results of this thesis therefore expand our understanding of the physical and chemical properties of the Milky Way stellar halo, and provide insight into the processes involved in its formation and evolution.
Percolation process, which is intrinsically a phase transition process near the critical point, is ubiquitous in nature. Many of its applications embrace a wide spectrum of natural phenomena ranging from the forest fires, spread of contagious diseases, social behaviour dynamics to mathematical finance, formation of bedrocks and biological systems. The topology generated by the percolation process near the critical point is a random (stochastic) fractal. It is fundamental to the percolation theory that near the critical point, a unique infinite fractal structure, namely the infinite cluster, would emerge. As de Gennes suggested, the properties of the infinite cluster could be deduced by studying the dynamical behaviour of the random walk process taking place on it. He coined the term the ant in the labyrinth. The random walk process on such an infinite fractal cluster exhibits a subdiffusive dynamics in the sense that the mean squared displacement grows as ~t2/dw, where dw, called the fractal dimension of the random walk path, is greater than 2. Thus, the random walk process on the infinite cluster is classified as a process exhibiting the properties of anomalous diffusions. Yet near the critical point, the infinite cluster is not the sole emergent topology, but it coexists with other clusters whose size is finite. Though finite, on specific length scales these finite clusters exhibit fractal properties as well. In this work, it is assumed that the random walk process could take place on these finite size objects as well. Bearing this assumption in mind requires one address the non-equilibrium initial condition. Due to the lack of knowledge on the propagator of the random walk process in stochastic random environments, a phenomenological correspondence between the renowned Ornstein-Uhlenbeck process and the random walk process on finite size clusters is established. It is elucidated that when an ensemble of these finite size clusters and the infinite cluster is considered, the anisotropy and size of these finite clusters effects the mean squared displacement and its time averaged counterpart to grow in time as ~t(d+df (t-2))/dw, where d is the embedding Euclidean dimension, df is the fractal dimension of the infinite cluster, and , called the Fisher exponent, is a critical exponent governing the power-law distribution of the finite size clusters. Moreover, it is demonstrated that, even though the random walk process on a specific finite size cluster is ergodic, it exhibits a persistent non-ergodic behaviour when an ensemble of finite size and the infinite clusters is considered.