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During RUN3 (2021-2023) of the Large Hadron Collider, the Time Projection Chamber (TPC) of ALICE will be operated with quadruple stacks of Gas Electron Multipliers (GEMs). This technology will allow to overcome the rate limitation due to the gated operation of the Multi-Wire Proportional Chambers (MWPCs) used in RUN1 (2009-2013) and RUN2 (2015-2018).
As part of the Upgrade project, long-term irradiation tests, so called "ageing tests", have been carried out. A test setup with a detector using a quadruple stack of 10x10cm2 GEMs was built and operated in Ar-CO2 and Ne-CO2-N2 gas mixtures. The detector performance such as gas gain and energy resolution were monitored continuously. In addition, outgassing tests of materials used for the assembly process of the upgraded TPC were performed. To reach the expected dose of the GEM-based TPC, the detector was operated at much higher gains than the TPC. It was found, that the GEMs could keep their performance within the projected lifetime of the TPC. Most of the tested materials showed no negative impact on the detector. For the tested epoxy adhesive no certain conclusion could be drawn.
At much higher doses than expected for the upgraded TPC, a new phenomenon was observed, which changed the hole geometry of the GEMs and led to a degradation of the energy resolution. Even though its occurrence is not expected during the lifetime of the GEM-based TPC, simulations were carried out to study this effect more systematically. The simulations confirmed, that a change of the hole geometries of the GEMs, lead to an increase of the local gain variation, which results in a decrease of the energy resolution.
Furthermore the effect of methane as quench gas on GEMs was studied, even though this gas is not foreseen to be used in the TPC. From ageing tests with single-wire proportional counters it is well known that hydrocarbons are produced in the plasma of the avalanches, which cover the electrodes and lead to a degradation of the detector performance. Even though GEMs have a quite different geometry, the ageing tests showed, that also this technology tends to methane-induced ageing. A loss of gas gain as well as a degradation of the energy resolution due to deposits on the electrodes was monitored. A qualitative and quantitative comparison between ageing in GEMs and proportional counters was performed.
The internet has often been considered a 'technology of freedom' – a nearly revolutionary tool believed to flatten social hierarchies and democratize access to media by 'giving voice' to everybody equally. Contradictory to this point of view, research has shown the existence of a 'digital divide,' the phenomenon that access to and use of the internet, as well as the outcomes derived from this use, correlate with pre-existing inequalities.
Based on ethnographic fieldwork among activists in Dakar, Senegal, this thesis analyzes how inequalities shape and are shaped by the relationships between activists and smartphones. Do smartphones indeed flatten social hierarchies, or are inequalities rather reproduced – or even reinforced – through them?
When performing transfer learning in Computer Vision, normally a pretrained model (source model) that is trained on a specific task and a large dataset like ImageNet is used. The learned representation of that source model is then used to perform a transfer to a target task. Performing transfer learning in this way had a great impact on Computer Vision, because it worked seamlessly, especially on tasks that are related to each other. Current research topics have investigated the relationship between different tasks and their impact on transfer learning by developing similarity methods. These similarity methods have in common, to do transfer learning without actually doing transfer learning in the first place but rather by predicting transfer learning rankings so that the best possible source model can be selected from a range of different source models. However, these methods have focused only on singlesource transfers and have not paid attention to multi-source transfers. Multi-source transfers promise even better results than single-source transfers as they combine information from multiple source tasks, all of which are useful to the target task. We fill this gap and propose a many-to-one task similarity method called MOTS that predicts both, single-source transfers and multi-source transfers to a specific target task. We do that by using linear regression and the source representations of the source models to predict the target representation. We show that we achieve at least results on par with related state-of-the-art methods when only focusing on singlesource transfers using the Pascal VOC and Taskonomy benchmark. We show that we even outperform all of them when using single and multi-source transfers together (0.9 vs. 0.8) on the Taskonomy benchmark. We additionally investigate the performance of MOTS in conjunction with a multi-task learning architecture. The task-decoder heads of a multi-task learning architecture are used in different variations to do multi-source transfers since it promises efficiency over multiple singletask architectures and incurs less computational cost. Results show that our proposed method accurately predicts transfer learning rankings on the NYUD dataset and even shows the best transfer learning results always being achieved when using more than one source task. Additionally, it is further examined that even just using one task-decoder head from the multi-task learning architecture promises better transfer learning results, than using a single-task architecture for the same task, which is due to the shared information from different tasks in the multi-task learning architecture in previous layers. Since the MOTS rankings for selecting the MTI-Net task-decoder head with the highest transfer learning performance were very accurate for the NYUD but not satisfying for the Pascal VOC dataset, further experiments need to varify the generalizability of MOTS rankings for the selection of the optimal task-decoder head from a multi-task architecture.
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.