Ph.D. Theses

Silicon strip sensors developed for the ATLAS Inner Tracker Upgrade have shown a strong dependence of the breakdown voltage on varying levels of relative humidity (RH). The solution to this humidity sensitivity has so far been to store and test the sensors in a dry environment (RH < 10 %). [...]

Kolloidale Nanokristalle haben faszinierende größen- und formabhängige Eigenschaften. Ein großes Oberfläche-zu-Volumen-Verhältnis, starke Quanteneinschlusseffekte und plasmonische Effekte machen sie für Anwendungen in der Energieumwandlung und der Katalyse interessant. [...]

Monte Carlo simulation of particle interactions underpins nearly every aspect of modern collider experiments, yet its computational cost will exceed available resources as luminosities and detector complexities increase.Calorimeter shower simulation accounts for the dominant share of this cost, particularly for the highly granular designs planned for future facilities.Deep generative Machine Learning models offer a promising path toward addressing this bottleneck by learning from relatively small amounts of Monte Carlo data and then sampling new showers more efficiently.This work introduces CaloClouds, a generative model for fast electromagnetic shower simulation in highly granular sampling calorimeters, developed and evaluated using the silicon-tungsten electromagnetic calorimeter of the International Large Detector (ILD) as a case study.In contrast to previous approaches built on fixed voxel grids, CaloClouds represents showers as point clouds -- sets of energy-weighted coordinates with continuous spatial positions.This formulation minimizes the projection artefacts that arise when mapping grid-based outputs onto irregular detector readout geometries and enables the use of a single model across different detector positions with the same longitudinal structure.The model combines two complementary components: ShowerFlow, a normalising flow that captures global properties such as per-layer energies and number of energy depositions, and a diffusion model that generates individual energy depositions.A dedicated preprocessing pipeline converts Geant4 simulation steps into high-resolution point clouds, providing training data at finer granularity than the physical readout cells.To disentangle the contributions of data representation from model performance, this work introduces optimal shower generators constructed by projecting Geant4 steps onto virtual grids of varying resolution without generative modelling.Benchmarks against these references show that CaloClouds achieves fidelity close to the finest-grained optimal generator, indicating that the model reaches its maximum potential given the data representation.Simultaneously, comparisons with the coarse-grained optimal shower generators reveal fundamental limitations of fixed-grid representations independent of the generative model architecture.Full integration into the ILD software chain via the DDML library enables evaluation with standard reconstruction tools, including Pandora particle flow.Physics benchmarks span single-photon observables, di-photon separation, and $\pi^0$ reconstruction in $e^+e^-\rightarrow~\tau^+\tau^-$ events, with the majority demonstrating state-of-the-art agreement with Geant4.On CPU hardware, CaloClouds achieves more than 120-fold acceleration relative to full Geant4 simulation, while on GPU hardware, speedups of several thousand-fold are achievable.Taken together, these results establish CaloClouds as an effective solution that balances speed and accuracy for fast calorimeter simulation, providing a foundation for meeting the simulation demands of next-generation collider experiments..

Vector-like quarks are predicted in various scenarios in beyond the Standard Model (SM) theories. This work presents a search for a single vector-like top quark (T) decaying into a top quark and a SM Higgs boson (T!tH), in the T mass range from 600 GeV to 1200 GeV. [...]

The high global warming potential of CH4 makes the catalytic conversion of residual CH4 in exhaust gases vital for applications such as CH4 combus tion engines and turbines, power-to-gas or biomass plants. The most active heterogeneous catalyst system for complete CH4 oxidation at low temper atures (< 650K) in lean reaction gas mixtures (overstoichiometric oxygen content) is the class of Pd-based catalysts supported by (mixed) metal oxide supports. [...]

The past decades saw the development of X-ray induced Coulomb ex-plosion imaging as one of several methods to perform single-particlemolecular imaging on a femtosecond timescale. It has been enabledby the advent of x-ray free electron lasers. [...]

Recent advances in laser technologies such as x-ray free-electron lasers and high harmonic generation have led to ever-shorter light pulses that enable us to probe ultrafastnuclear and electronic dynamics in atoms and molecules. Theoretical quantum dynamics simulations are indispensable in gaining deeper insights into these ultrafastprocesses. [...]

Highest resolution X-ray microscopy requires high numerical aperture (NA) optics ofexcellent quality which allow to focus X-ray beams to small focal points. MultilayerLaue lenses (MLLs) are a new type of diffractive optic with the capability to focushard X-rays with high efficiency to nanometer spots. [...]

Particle accelerators are sophisticated scientific facilities that require precise but time-consuming optimisation to achieve optimal performance. Considering benchmark tasks at the ARES and LCLS facilities, this dissertation proposes methods to deploy simulation-trained reinforcement learning (RL) policies for accelerator tuning zero-shot to the real world and novel tuning tasks, while comparing their performance to traditional methods. [...]