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The main focus of research in the field of high-energy heavy-ion physics is the study of the quark-gluon plasma (QGP). Topic of the present work is the measurement of electron-positron pairs (dielectrons), which grant direct access to some of the key properties of this state of matter, since after their formation they leave the hot and dense medium without significant interaction. In particular, the measurement of the initial QGP temperature is considered a "holy grail" of heavy-ion physics. Therefore, in addition to the analysis of existing data, a feasibility study has been conducted to determine to which extent this goal would be achievable by upgrading the ALICE experiment at CERN.
Dielectrons are produced during all stages of a heavy-ion collision, with their invariant mass reflecting the amount of energy available at the time of their formation. Dielectrons of highest mass are thus produced in the initial scatterings of the colliding nuclei by quark-antiquark annihilation. Correlated electron-positron pairs can also emerge from the decay chains of early-produced pairs of heavy-flavour (HF) particles. During the QGP stage and at the beginning of the hadronic phase, the system emits thermal radiation in the form of photons and dielectrons, which carry information about the medium temperature to the observer. In the final stage of the collision, decays of light-flavour (LF) hadrons produce additional contributions to the dielectron spectrum.
The present work is based on early data from the ALICE experiment recorded from lead-lead collisions at a center-of-mass energy of 2.76 TeV. Due to the limited amount of data, a focus is placed on achieving high efficiencies throughout the analysis. To this end, a special electron identification strategy is developed and a custom track selection applied, together resulting in a tenfold increase in pair efficiency. The dielectron spectrum is evaluated on a statistical basis, using a pair prefilter, which is optimized based on two signal quality criteria, to reduce the fraction of electrons and positrons from unwanted sources at minimum signal loss. In addition, an artifact of the track reconstruction is exploited to suppress pairs from photon conversions and to correct the dielectron yield for a contribution from different-conversion pairs. The main signal uncertainty is extracted from the deviation between results of 20 analysis settings and amounts to 20% in most of the studied kinematic range.
For comparison with the analysis results, a hadronic cocktail consisting of the LF and HF contributions is simulated, which can reasonably well describe the measured dielectron production, with a hint of an enhancement at low invariant mass. Two approaches to model the in-medium modification of the heavy-flavour are followed, resulting in up to 50% suppression, which creates some additional space for a thermal contribution at intermediate mass.
For a complete comparison between experimental data and theoretical expectation, two model calculations are consulted. The Thermal Fireball Model provides predictions for thermal dielectron radiation from the QGP and hadron gas. The data tends to be better described with these additional thermal contributions. For a comparison with a prediction by the UrQMD model, the HF component of the cocktail is subtracted from the data. This results in better agreement if the HF suppression by in-medium effects is taken into account.
The feasibility study in this work has served as a physical motivation for the ALICE upgrade for LHC Run 3. The precision with which the early temperature of the QGP can be determined via dielectrons is chosen as key observable. A multitude of individual contributions are merged into a fully modeled dielectron analysis. The resulting signal-to-background ratio represents some of the expected systematic uncertainties, while from the significance combined with the planned number of lead-lead collisions a realistic "measurement" with statistical fluctuations around the expected dielectron signal is generated using a Poisson sampling technique. Since the HF yield exceeds the QGP thermal radiation by about an order of magnitude, an additional analysis step exploiting the enhanced track reconstruction is introduced to reduce its contribution by up to a factor of five. The resulting reduction in pair efficiency is overcompensated by an up to hundred times higher collision rate. The entire cocktail is then subtracted from the sampled data to isolate the thermal excess yield. The final analysis of this spectrum shows that the inverse slope of the model prediction, which depends directly on the QGP temperature, can be reproduced within statistical and systematic uncertainties of about 10%.
The promising results of this study have contributed on the one hand to the realization of the ALICE upgrade and to a design decision for the new Inner Tracking System, and at the same time represent exciting predictions for upcoming measurements.
Die vorliegende Dissertation stellt die Strahldynamikdesigns zweier Hochfrequenzquadrupol-Linearbeschleuniger bzw. Radio Frequency Quadrupoles (RFQs) vor: das fur den RFQ des Protonen-Linearbeschleunigers (p-Linac) des FAIR2-Projekts an der GSI3 Darmstadt sowie einen ersten Designentwurf für einen kompakten RFQ, der u.a. zur Erzeugung von Radioisotopen für medizinische Zwecke genutzt werden könnte. Der Schwerpunkt liegt auf dem ersten Design.
Model frameworks, based on Floquet theory, have been shown to produce effective tools for accurately predicting phase-noise response of single (free-running) oscillator systems. This method of approach, referred to herein as macro-modeling, has been discussed in several highly influential papers and now constitutes an established branch of modern circuit theory. The increased application of, for example, injection-locked oscillators and oscillator arrays in modern communication systems has subsequently exposed the demand for similar rigorous analysis tools aimed at coupled oscillating systems. This paper presents a novel solution in terms of a macro-model characterizing the phase-response of synchronized coupled oscillator circuits and systems perturbed by weak noise sources. The framework is generalized and hence applicable to all circuit configurations and coupling topologies generating a synchronized steady-state. It advances and replaces the phenomenological descriptions currently found in the published literature pertaining to this topic and, as such, represents a significant breakthrough w.r.t. coupled oscillator noise modeling. The proposed model is readily implemented numerically using standard routines.
For finite baryon chemical potential, conventional lattice descriptions of quantum chromodynamics (QCD) have a sign problem which prevents straightforward simulations based on importance sampling.
In this thesis we investigate heavy dense QCD by representing lattice QCD with Wilson fermions at finite temperature and density in terms of Polyakov loops.
We discuss the derivation of $3$-dimensional effective Polyakov loop theories from lattice QCD based on a combined strong coupling and hopping parameter expansion, which is valid for heavy quarks.
The finite density sign problem is milder in these theories and they are also amenable to analytic evaluations.
The analytic evaluation of Polyakov loop theories via series expansion techniques is illustrated by using them to evaluate the $\SU{3}$ spin model.
We compute the free energy density to $14$th order in the nearest neighbor coupling and find that predictions for the equation of state agree with simulations to $\mathcal{O}(1\%)$ in the phase were the (approximate) $Z(3)$ center symmetry is intact.
The critical end point is also determined but with less accuracy and our results agree with numerical results to $\mathcal{O}(10\%)$.
While the accuracy for the endpoint is limited for the current length of the series, analytic tools provide valuable insight and are more flexible.
Furthermore they can be generalized to Polyakov-loop-theories with $n$-point interactions.
We also take a detailed look at the hopping expansion for the derivation of the effective theory.
The exponentiation of the action is discussed by using a polymer expansion and we also explain how to obtain logarithmic resummations for all contributions, which will be achieved by employing the finite cluster method know from condensed matter physics.
The finite cluster method can also be used to evaluate the effective theory and comparisons of the evaluation of the effective action and a direction evaluation of the partition function are made.
We observe that terms in the evaluation of the effective theory correspond to partial contractions in the application of Wick's theorem for the evaluation of Grassmann-valued integrals.
Potential problems arising from this fact are explored.
Next to next to leading order results from the hopping expansion are used to analyze and compare the onset transition both for baryon and isospin chemical potential.
Lattice QCD with an isospin chemical potential does not have a sign problem and can serve as a valuable cross-check.
Since we are restricted by the relatively short length of our series, we content ourselves with observing some qualitative phenomenological properties arising in the effective theory which are relevant for the onset transition.
Finally, we generalize our results to arbitrary number of colors $N_c$.
We investigate the transition from a hadron gas to baryon condensation and find that for any finite lattice spacing the transition becomes stronger when $N_c$ is increased and to be first order in the limit of infinite $N_c$.
Beyond the onset, the pressure is shown to scale as $p \sim N_c$ through all available orders in the hopping expansion, which is characteristic for a phase termed quarkyonic matter in the literature.
Some care has to be taken when approaching the continuum, as we find that the continuum limit has to be taken before the large $N_c$ limit.
Although we currently are unable to take the limits in this order, our results are stable in the controlled range of lattice spacings when the limits are approached in this order.
Computational workflow optimization for magnetic fluctuation measurements of 3D nano-tetrapods
(2021)
The detailed understanding of micro–and nanoscale structures, in particular their magnetization dynamics, dominates contemporary solid–state physics studies. Most investigations already identified an abundance of phenomena in one–and two–dimensional nanostructures. The following thesis focuses on the magnetic fingerprint of three–dimensional CoFe nano–magnets, specifically the temporal development of their hysteresis loop. These nano–magnets were grown in a tetrahedral pattern on top of a highly susceptible home–build GaAs/AlGaAs micro–Hall sensor using focused electron beam induced deposition (FEBID).
During the measurements, utmost efforts were employed to exemplify current best research practices. The data life cycle of the present thesis is based upon open–source data science tools and packages. Data acquisition and analysis required self–written automated algorithms to handle the extensive quantity of data. Existing instrumental-controlling software was improved, and new Python packages were devised to analyze and visualize the gathered data. The open–source Python data analysis framework (ana) was developed to facilitate computational reproducibility. This framework transparently analyses and visualizes the gathered data automatically using Continuous Analysis tools based on GitLab and Continuous Integration. This automatization uses bespoke scripts combined with virtualization tools like Docker to facilitate reproducible and device–independent results.
The hysteresis loops reveal distinct differences in subsequently measured loops with identical initial experimental parameters, originating from the nano–magnet’s magnetic noise. This noise amplifies in regions where switching processes occur. In such noise–prone regions, the time–dependent scrutinization reveals presumably thermally induced metastable magnetization states. The frequency–dependent power spectral density uncovers a characteristic 1/f² behavior at noise–prone regions with metastable magnetization states.
In this roadmap article, we have focused on the most recent advances in terahertz (THz) imaging with particular attention paid to the optimization and miniaturization of the THz imaging systems. Such systems entail enhanced functionality, reduced power consumption, and increased convenience, thus being geared toward the implementation of THz imaging systems in real operational conditions. The article will touch upon the advanced solid-state-based THz imaging systems, including room temperature THz sensors and arrays, as well as their on-chip integration with diffractive THz optical components. We will cover the current-state of compact room temperature THz emission sources, both optolectronic and electrically driven; particular emphasis is attributed to the beam-forming role in THz imaging, THz holography and spatial filtering, THz nano-imaging, and computational imaging. A number of advanced THz techniques, such as light-field THz imaging, homodyne spectroscopy, and phase sensitive spectrometry, THz modulated continuous wave imaging, room temperature THz frequency combs, and passive THz imaging, as well as the use of artificial intelligence in THz data processing and optics development, will be reviewed. This roadmap presents a structured snapshot of current advances in THz imaging as of 2021 and provides an opinion on contemporary scientific and technological challenges in this field, as well as extrapolations of possible further evolution in THz imaging.
Presolar grains and their isotopic compositions provide valuable constraints to AGB star nucleosynthesis. However, there is a sample of O- and Al-rich dust, known as group 2 oxide grains, whose origin is difficult to address. On the one hand, the 17O/16O isotopic ratios shown by those grains are similar to the ones observed in low-mass red giant stars. On the other hand, their large 18O depletion and 26Al enrichment are challenging to account for. Two different classes of AGB stars have been proposed as progenitors of this kind of stellar dust: intermediate mass AGBs with hot bottom burning, or low mass AGBs where deep mixing is at play. Our models of low-mass AGB stars with a bottom-up deep mixing are shown to be likely progenitors of group 2 grains, reproducing together the 17O/16O, 18O/16O and 26Al/27Al values found in those grains and being less sensitive to nuclear physics inputs than our intermediate-mass models with hot bottom burning.
Neurons are cells with a highly complex morphology; their dendritic arbor spans up to thousands of micrometers. This extended arbor poses a challenge for the logistics of neuronal processes: mRNA, proteins, and organelles have to be transported to dendrites, hundreds of micrometers away from the soma. This thesis aims to calculate the minimum number of proteins needed to populate the dendritic trees for different scenarios.
In chapter 2, I analyzed the ability of different mechanisms to populate the dendritic arbor. I started from the solution of the diffusion equation in Sec. 2.1, then I included the contribution of active transport in Sec. 2.2 and showed how it could have either the effect of increasing the effective diffusion coefficient or of introducing a bias in the diffusion process. In Sec. 2.3 I studied the spatial distribution of locally synthesized protein, accordingly with actively and passively transported mRNA. In Sec. 2.5, I derived the boundary condition for branches showing a qualitatively different behavior of surface and cytoplasmic proteins induced by the medium’s dimensionality in which they diffuse.
In chapter 3, I introduced the concept of protein requirement, defined as the minimum number of proteins that the neuron needs to produce to provide at least one protein to each micrometer of the dendritic arbor. In Sec. 3.1, I derived the protein requirement for diffusive proteins for somatic translation and constant translation in the dendritic arbor. In Sec. 3.2, I analyzed numerically the protein requirement in the case of actively transported protein synthesized in the soma, and, in Sec. 3.3, in the case of actively transported proteins synthesized in the dendritic arbor. In Sec. 3.4, I analyzed the protein requirement of protein synthesized in the dendrite accordingly with the distribution of mRNA described in Sec. 3.3 and 3.2. In Sec. 3.5, I derived the protein requirement for a single branch and purely diffusive proteins.
In chapter 4, I analyzed the relation between the radii of the three afferent dendrites in a branch, their length, and the diffusion length of a protein. In Sec. 4.1 I derived the optimal ratio between the radii of the daughter dendrites that minimizes the protein requirement. In Sec. 4.3 I introduced the 3/2− Rall Rule and in Sec. 4.5 its generalization. Finally, I used those rules to estimate the fraction of proteins diffusing away from and toward the soma.
In chapter 5, I analyzed the radii distribution for three categories of neurons: cultured hippocampal neurons in Sec. 5.1, stomatogastric ganglia neuron in Sec. 5.2, and 3DEM reconstructed prefrontal pyramidal neurons in Sec. 5.3. For each of these three classes, I analyzed the distribution of radii, Rall exponents, and the probability ratio. For most of them, I found that the probability of a protein diffusing away from the soma is higher for surface proteins than for cytoplasmic ones. I quantified this with a parameter called surface bias.
In Chapter 6, I analyzed the fluorescent ratio imaged by our collaborators Anne-Sophie Hafner, for a surface protein, GFP::Nlg, and a soluble one, GFP, in cultured hippocampal neurons, and I compared the fluorescent ratio with the probability ratio obtained in 5.1, finding that they are in good agreement.
In chapter 7, I compared the real dendritic morphologies imaged by one of our collaborators Ali Karimi with the optimal branching rule obtained in Sec. 4.1 and I calculated the cost for not having optimal branching radii.
Finally, in Chapter 8, I used the knowledge of the branching statistics gathered in 5.3 to simulate the protein profile on three different classes of neurons: pyramidal neurons, granule neuron, and Purkinje neurons. I compared the protein profile for surface and cytoplasmic neurons for each morphology for two different values of the diffusion length: λ = 109µm and λ = 473µm, both for optimized radii and symmetrical radii. I showed how the radii optimization reduces the protein requirement of a factor 10 4 for pyramidal neurons.