Impacts of immigration on low-status groups

What are the social impacts of immigration on low-status groups? While existing research on the effects of immigration has mostly focused on the majority population, this dissertation shifts the focus to economically disadvantaged individuals and ethnic minorities, as these groups are often more affected by migration than others. Furthermore, previous migration studies have predominantly examined opinions rather than behaviors. In contrast, this dissertation also considers behavioral responses, particularly voting behavior and discriminatory actions. Another challenge in prior research is the tendency of migrants to settle in specific regions, making causal analysis of migration’s effects difficult. Unlike such self-selective migration patterns, the 2015/2016 refugee crisis in Germany, due to the quasi-random distribution of refugees, provides a unique opportunity to examine the effects of immigration. Back then, social tensions, revolutions, and civil wars in North Africa and the Middle East triggered an unexpected increase in refugee migration to Germany. This dissertation analyzes the impact of refugee immigration on ethnic minorities as a socially disadvantaged group. Additionally, it explores whether individuals with low socioeconomic status are more likely to vote for right-wing populists. Thus, this dissertation contributes to a better understanding of right-wing populism, discrimination, interethnic relations, and ethnic boundaries. Previous studies show that the right-wing populist party Alternative for Germany (AfD) exploited the massive influx of refugees into Germany to gain voters through anti-immigration propaganda. An alternative line of research offers an economic explanation, suggesting that those disadvantaged by modernization are more likely to support populist parties. However, research on this modernization losers' theory (MLT) in relation to AfD voters is inconclusive and largely based on pre-election surveys and problematic causal assumptions. The first study of this dissertation analyzes post-election survey data from 2017, part of the German Longitudinal Election Study (N ≈ 1,200), which allows for the examination of actual voting behavior. Through valid causal reasoning and improved model selection, the study finds that, contrary to the MLT, a low socioeconomic status had no significant impact on the likelihood of voting for the AfD. Instead, the subjective perception of disadvantage proved to be a decisive factor in favor of voting for the AfD. Moreover, the study demonstrates that greater dissatisfaction with politics acts as a mediating factor between perceived disadvantage and AfD voting. The immigration of refugees may have intensified xenophobic attitudes and thereby exacerbated ethnic discrimination. Increasing hostility could also be directed against earlier immigrant groups. The second study in this dissertation examines whether the refugee crisis influenced the ethnic discrimination of Turkish applicants in the German rental housing market. Previous studies often relied on cross-sectional comparisons of neighborhoods with varying migrant populations, which is problematic due to unobserved confounders and the self-selection of migrants. To draw causal conclusions about how immigration affects ethnic discrimination, my co-authors and I used data from a field experiment – the gold standard for analyzing discrimination. Email correspondence tests were conducted on a large online housing platform just before the onset of the refugee crisis (1st wave), and the experiment was repeated at the peak of the crisis (2nd wave). Our longitudinal approach, combined with the quasi-random geographic distribution of refugees, allows for causal inferences with minimal bias. Based on approximately 10,000 email applications for 5,000 rental apartments, we found that the influx of refugees did not significantly alter the level of ethnic discrimination against Turks in the German rental housing market. While the first two studies of this dissertation primarily focus on the behavior of the ethnic majority in Germany, the third study examines the impact of refugee immigration on the perspectives of the two largest ethnic minorities in Germany: individuals with Polish and Turkish migration backgrounds. This study is one of the first in a European country to investigate the social impacts of the immigration of a new group on established immigrants. To do so, I combine macro-level data on refugees with individual longitudinal data from a large German panel survey (SOEP) from 2012 to 2018, based on a random sample. Using fixed-effects estimations, this study shows that an increasing proportion of refugees in a county raised concerns about immigration and reduced perceived discrimination among Turkish (N ≈ 700 respondents, n ≈ 2,900 person-years) and Polish (N ≈ 500 respondents, n ≈ 2,100 person-years) respondents. Furthermore, Turkish immigrants tended to feel more German in response to the refugee crisis, while simultaneously feeling a stronger connection to Turkey. Polish immigrants also felt more German, though not more connected to Poland. These findings support the assumption that minority groups distance themselves from new immigrants while also using the situation to improve their social position by strengthening their identification with the majority group and/or their own ethnic group. In summary, this dissertation highlights the multi-dimensionality of social status by demonstrating that it encompasses more than just objective socioeconomic status. It shows that objective status can differ from subjective perception and status evaluation. While an objectively low socioeconomic status had no influence on the probability of voting for right-wing populists, the subjective assessment of one’s status had a significant effect. A discrepancy was also found between the objective and subjective status of migrants. Although objective discrimination was not influenced by the influx of refugees in the short term, at least not in the housing market, subjective experiences of discrimination decreased. This can be interpreted as an improvement in relations with the German majority from the perspective of Turkish and Polish minorities, though this was not confirmed by the German majority. These findings suggest that researchers should place greater emphasis on subjective perspectives.Welche sozialen Auswirkungen hat Immigration auf Gruppen mit niedrigem Status? Während sich die bestehende Forschung zu Effekten von Zuwanderung größtenteils auf die Mehrheitsbevölkerung beschränkte, richtet diese Dissertation den Fokus auf wirtschaftlich benachteiligte Personen und ethnische Minderheiten, weil diese von Migration oft stärker betroffen sind als andere soziale Gruppen. Darüber hinaus haben frühere Migrationsstudien überwiegend Meinungen statt Verhaltensweisen untersucht. Im Gegensatz dazu werden in dieser Dissertation auch Verhaltensreaktionen betrachtet, insbesondere Wahlverhalten und diskriminierende Handlungen. Eine weitere Herausforderung der bisherigen Forschung ist die Tendenz von Migranten, sich in bestimmten Regionen niederzulassen, was eine kausale Analyse der Auswirkungen von Migration erschwert. Im Gegensatz zu einer solchen selbstselektiven Form der Zuwanderung bietet die Flüchtlingskrise 2015/2016 in Deutschland aufgrund der quasi-zufälligen Verteilung der Flüchtlinge eine einzigartige Gelegenheit, die Effekte von Zuwanderung zu untersuchen. Damals lösten soziale Spannungen, Revolutionen und Bürgerkriege in Nordafrika und dem Nahen Osten einen unerwarteten Anstieg der Flüchtlingszuwanderung nach Deutschland aus. Diese Dissertation analysiert die Auswirkungen der Flüchtlingszuwanderung auf ethnische Minderheiten als sozial benachteiligte Gruppe. Zudem wird untersucht, ob Menschen mit niedrigem sozioökonomischen Status eher Rechtspopulisten wählen. Damit trägt diese Arbeit zu einem besseren Verständnis von Rechtspopulismus, Diskriminierung, interethnischen Beziehungen und ethnischen Grenzen bei. Frühere Untersuchungen zeigen, dass die rechtspopulistische Partei Alternative für Deutschland (AfD) den massiven Zustrom von Flüchtlingen nach Deutschland genutzt hat, um durch einwanderungsfeindliche Propaganda Wähler zu gewinnen. Eine alternative Forschungsrichtung bietet eine ökonomische Erklärung an, die besagt, dass diejenigen, die durch die Modernisierung benachteiligt werden, eher populistische Parteien unterstützen. Die Forschung zu dieser Modernisierungsverlierertheorie (MLT) ist jedoch in Bezug auf AfD-Wähler uneindeutig und basiert überwiegend auf Vorwahlbefragungen und problematischen Kausalannahmen. Die erste Studie dieser Dissertation analysiert Nachwahl-Umfragedaten aus dem Jahr 2017 der German Longitudinal Election Study (N ≈ 1.200), wodurch das tatsächliche Wahlverhalten untersucht wird. Durch eine valide kausale Argumentation und eine verbesserte Modellwahl kommt die Studie zu dem Ergebnis, dass im Widerspruch zur MLT ein niedriger sozioökonomischer Status keinen signifikanten Einfluss auf die Wahrscheinlichkeit hatte, die AfD zu wählen. Stattdessen erwies sich die subjektive Wahrnehmung von Benachteiligung als ein entscheidender Faktor zugunsten der AfD-Wahl. Darüber hinaus demonstriert die Studie, dass eine stärkere Unzufriedenheit mit der Politik als mediierender Faktor zwischen subjektiver Benachteiligung und AfD-Wahl wirkt. Die Zuwanderung von Flüchtlingen könnte fremdenfeindliche Einstellungen verstärkt und damit ethnische Diskriminierung verschärft haben. Die zunehmende Fremdenfeindlichkeit könnte sich zudem auch gegen frühere Einwanderergruppen richten. In der zweiten Studie dieser Dissertation wird untersucht, ob die Flüchtlingskrise die ethnische Diskriminierung türkischer Bewerber auf dem deutschen Mietwohnungsmarkt beeinflusste. Frühere Studien stützten sich häufig auf Querschnittsvergleiche von Stadtteilen mit unterschiedlichen Migrantenanteilen, was aufgrund unbeobachteter Confounder und Selbstselektion von Zuwanderern problematisch ist. Um kausale Rückschlüsse darüber ziehen zu können, wie sich Zuwanderung auf ethnische Diskriminierung auswirkt, nutzten meine Mitautoren und ich Daten eines Feldexperiments – der Goldstandard für die Analyse von Diskriminierung. Es wurden E-Mail-Korrespondenztests auf einer großen Online-Wohnungsplattform kurz vor Ausbruch der Flüchtlingskrise durchgeführt (1. Welle) und das Experiment wurde auf dem Höhepunkt der Krise wiederholt (2. Welle). Unser longitudinaler Ansatz, kombiniert mit der quasi-zufälligen räumlichen Verteilung der Flüchtlinge, ermöglicht kausale Rückschlüsse mit geringer Verzerrung. Basierend auf etwa 10.000 E-Mail-Bewerbungen für 5.000 Mietwohnungen stellten wir fest, dass der Zustrom von Flüchtlingen das Ausmaß der ethnischen Diskriminierung von Türken auf dem deutschen Mietwohnungsmarkt nicht signifikant veränderte. Während sich die ersten beiden Artikel dieser Dissertation vor allem auf das Verhalten der ethnischen Mehrheit in Deutschland konzentrieren, untersucht der dritte Artikel die Auswirkungen der Flüchtlingszuwanderung auf die Sichtweise der beiden größten ethnischen Minderheiten in Deutschland: Personen mit polnischem und türkischem Migrationshintergrund. Diese Studie ist eine der ersten, die in einem europäischen Land untersucht, welche sozialen Auswirkungen die Zuwanderung einer neuen Gruppe auf bereits bestehende Zuwanderergruppen hat. Hierfür kombiniere ich Makrodaten über Flüchtlinge mit individuellen Längsschnittdaten einer groß angelegten deutschen Panelbefragung (SOEP) von 2012 bis 2018, basierend auf einer Zufallsstichprobe. Mithilfe von Fixed-Effects-Schätzungen zeigt diese Studie, dass ein steigender Anteil von Flüchtlingen in einem Landkreis die Sorgen über Zuwanderung erhöhte und die selbst wahrgenommene Diskriminierung unter türkischen (N ≈ 700 Befragte, n ≈ 2.900 Personenjahre) und polnischen (N ≈ 500 Befragte, n ≈ 2.100 Personenjahre) Befragten verringerte. Darüber hinaus fühlten sich türkische Einwanderer tendenziell deutscher als Reaktion auf die Flüchtlingskrise, während sie gleichzeitig eine stärkere Verbundenheit mit der Türkei empfanden. Polnische Einwanderer fühlten sich ebenfalls deutscher, jedoch nicht stärker mit Polen verbunden. Diese Ergebnisse unterstützen die Annahme, dass sich Minderheitengruppen von neuen Zuwanderern distanzieren. Zugleich nutzen sie die Situation, um ihre soziale Position zu verbessern, indem sie ihre Identifikation mit der Mehrheitsgruppe und/oder ihrer eigenen ethnischen Gruppe stärken. Zusammenfassend verdeutlicht diese Dissertation die Multidimensionalität des sozialen Status, indem sie zeigt, dass dieser mehr umfasst als nur den objektiven sozioökonomischen Status. Es wird aufgezeigt, dass der objektive Status von der subjektiven Wahrnehmung sowie von der Bewertung des Status abweichen kann. Während ein objektiv niedriger sozioökonomischer Status keinen Einfluss auf die Wahrscheinlichkeit hatte, Rechtspopulisten zu wählen, hatte die subjektive Bewertung des eigenen Status einen erheblichen Effekt. Eine Abweichung gab es auch zwischen objektivem und subjektivem Status von Migranten. Obwohl die objektive Diskriminierung durch die Zuwanderung von Flüchtlingen kurzfristig nicht beeinflusst wurde, zumindest nicht auf dem Mietwohnungsmarkt, nahmen subjektive Diskriminierungserfahrungen ab. Dies lässt sich dahingehend interpretieren, dass sich die Beziehungen zur deutschen Mehrheitsgesellschaft aus der Perspektive der türkischen und polnischen Minderheiten verbesserten; dies aber nicht von der deutschen Mehrheit bestätigt wurde. Diese Ergebnisse zeigen, dass Forscher subjektive Perspektiven stärker berücksichtigen sollten

Identification of thunderstorm occurrence in convection-permitting ensemble forecasts using deep neural networks

Thunderstorms have potentially hazardous impacts on society and the economy due to accompanying phenomena, such as lightning, strong winds, and intense precipitation, creating a demand for accurate and timely thunderstorm forecasts. Thunderstorm forecasts several hours in advance are based on simulations of the future atmosphere via numerical weather prediction (NWP). However, as none of the NWP state variables, such as temperature, pressure, or specific humidity, directly indicates thunderstorm occurrence, surrogate variables like convective available potential energy or synthetic radar reflectivity are used as proxies instead. Surrogate variables of thunderstorm occurrence are typically derived from NWP state variables through the consideration of physical principles and empirical knowledge. In this thesis, however, we present a machine learning (ML) model based on deep learning which bypasses the use of such surrogate variables; instead, the model directly processes the vertical variation of the NWP state variables with height to infer the corresponding probability of thunderstorm occurrence. In addition, this thesis makes use of a convection-permitting ensemble NWP model, i.e., an NWP model which (1) allows for resolving atmospheric convection without parameterizations, and (2) generates multiple possible forecasts consistent with forecast uncertainty. While these two properties have individually shown promise for improving thunderstorm forecasts, their combined potential for this task has so far been less explored. Specifically, we train our model on forecasts of ICON-D2-EPS, a limited-area model for Central Europe run operationally by the German Meteorological Service (DWD), with observations from the lightning detection network LINET serving as the ground truth. With regard to model architecture, we employ considerations based on physics and symmetries to keep model size and inference times computationally efficient. For instance, a sparse layer encourages interactions at similar height levels, whereas a shuffling mechanism forces the model to learn pressure coordinates instead of non-physical patterns tied to the vertical NWP grid. Evaluating our model for lead times up to 11 hours, we find that it outperforms a baseline model relying on traditional thunderstorm surrogate variables, which shows the capability of deep learning methods to discover—on their own—skillful representations of thunderstorm occurrence in NWP data. A linear sensitivity analysis (saliency map) suggests that these patterns found in the data are to a considerable extent physically interpretable: our model has learned the climatological propagation direction of thunderstorms in the study region and relies on fine-grained structures, such as ice-particle content near the tropopause and cloud cover, as well as mesoscale structures related to atmospheric instability and moisture. As additional results, we quantitatively explain skill gains resulting from our use of ensemble data. Finally, we demonstrate how neural network models like ours help keeping thunderstorm occurrence predictable for longer lead times compared to models which do not rely on ML. This thesis primarily contributes to improving the skill of thunderstorm forecasts by combining high-resolution NWP and ensemble systems with deep learning. On the other hand, many concepts and methods derived here apply to general binary classification problems, especially when high class imbalance is involved. More generally, our results exemplify the usefulness of incorporating physical considerations and symmetry principles into ML architectures to achieve lightweight models

Single-molecule spectroscopy & super-resolution microscopy at the biochemistry bench

Single-molecule spectroscopy and super-resolution microscopy offer valuable insights into molecular dynamics but have been limited by high costs and technical complexity. These tools are mostly accessible to specialized labs with custom-built systems. This work aims to make them more affordable and accessible to a wider range of researchers, including those in smaller or resource-limited labs. A major challenge in single-molecule experiments is the variability in experimental setups, often due to the use of home-built systems, a limitation common across all single-molecule techniques. In the context of smFRET, which this study focused on, applying established data correction routines enabled reliable and comparable results across different setups. The most critical parameter influencing data accuracy was the gamma factor, which accounts for differences in the quantum yields of the donor and acceptor fluorophores, as well as the wavelength-dependent detection efficiencies of the point detectors. However, its overall impact was minimal given the typical FRET efficiency differences observed in biomolecules, underscoring the importance of thoughtful protein and fluorophore design to minimize variability. Comparisons with other techniques, like Pulsed Electron-Electron Double Resonance (PELDOR) and anomalous X-ray scattering interferometry (AXSI), confirmed that smFRET provides consistent distance measurements. Discrepancies arose due to fluorophore-protein interactions but could be mitigated through careful experimental design. A key development of this work is Brick-MIC, an affordable, open-source platform for single-molecule experiments. Built with 3D printing and open-source software, Brick-MIC allows researchers to customize setups at a fraction of traditional costs. It supports techniques like smFRET, fluorescence correlation spectroscopy (FCS), and super-resolution imaging, making these tools more accessible to the scientific community. In a simplified iteration, a blue-green FRET system was created using a 488 nm laser, making it cost-effective while still providing valuable insights into biomolecular conformational changes. This system, while lacking stoichiometric information, enables the observation of biomolecule movements, catering to application-driven studies. Additionally, Brick-MIC was applied to nanoparticle detection, specifically identifying SARS-CoV-2 virus particles. By combining microfluidics, fluorescence correlation spectroscopy, and dual-layer detection strategies, this work enabled rapid and specific virus detection, demonstrating the practical applications of this affordable platform in diagnostics and public health

Towards a mechanistic understanding of sensorimotor control and symptom perception in persistent physical symptoms

Distressing physical symptoms that persist for months are frequent, occur across all areas of medicine and strongly impact quality of life. The association with measurable and reproducible pathophysiological processes is often loose or even absent and for most persistent physical symptoms (PPS), positive diagnostic markers are lacking, which challenges diagnosis and treatment. This thesis aims to contribute towards a better mechanistic understanding of PPS that can inform treatment and diagnosis by investigating symptom perception and sensorimotor processing in two examples of PPS, i.e., functional dizziness and post COVID-19 condition. We adopt a Bayesian brain perspective that proposes that the brain infers the most likely causes of sensory inputs by inverting an internal model that constitutes a probabilistic mapping between different states and sensory input as well as prior knowledge about these states. Recent theories have proposed that erroneous internal models can lead to the emergence of symptoms and dysfunctional motor processing, also in the absence of pathophysiological processes. Here, we provide further evidence in support of this hypothesis for functional dizziness and post COVID-19 condition. Using two different experimental paradigms, we were able to show that sensorimotor deficits (in functional dizziness) and increased breathlessness perception (in post COVID-19 condition) do not reflect altered and potentially pathological body states but rather are due to involvement of incorrect internal models. We highlight that different mechanisms could underlie these results and discuss the role of incorrect but highly precise priors in functional dizziness and maladaptive cost-functions in patients with post COVID-19 condition. In addition, we bridge the gap between experimental data and theories by developing a mathematical model that proposes a potential mechanism of how processing of respiratory data can lead to the emergence of breathlessness perception. In summary, this thesis provides an explanatory framework, a measurable marker of incorrect internal model use and an improved mechanistic understanding for functional dizziness and post COVID-19 condition. These findings can contribute towards development and refinement of existing treatments and reduce stigmatization of PPS

DNA origami meets silica: enhanced methods and functional customization for nanotechnological innovation

DNA, the molecule of life, has become a versatile tool in nanotechnology due to its programmability, precise base-pairing, and ability to self-assemble into complex nanostructures. DNA origami, a method that folds long single-stranded DNA into predefined shapes using short complementary staples, has revolutionized nanoscale architecture. These structures hold great potential for materials science and biomedicine, including molecular diagnostics, drug delivery, and the creation of hybrid nanomaterials. However, DNA's fragility and susceptibility to denaturation under physiological conditions pose challenges that limit its utility. This thesis focuses on stabilizing and functionalizing DNA origami through innovative silicification techniques. The work develops an accelerated silicification process that reduces processing time from days to hours while maintaining structural integrity. A rotation-based method ensures uniform coating without aggregation, enabling scalable production of silica-coated DNA origami. Additionally, the thesis investigates whether DNA origami retains functional addressability post-silicification. My studies confirm that site-specific modifications remain feasible, preserving adaptability—a critical factor for integration into biosensing systems. To further extend the utility of DNA origami, customizable silica coatings were developed. Fluorescent silica enables real-time imaging, while dissolvable silica introduces controlled degradation in response to environmental stimuli. These innovations provide tools for dynamic and responsive nanostructures. By addressing challenges in stability, functionality, and adaptability, this work lays the foundation for the development of multifunctional hybrid materials and positions DNA origami as a cornerstone of future nanotechnological advancements.DNA, das Molekül des Lebens, hat sich durch seine Programmierbarkeit, präzise Basenpaarung und Fähigkeit zur Selbstorganisation in komplexe Nanostrukturen zu einem vielseitigen Werkzeug der Nanotechnologie entwickelt. DNA-Origami, eine Methode, bei der ein langes einzelsträngiges DNA-Molekül mithilfe kurzer, komplementärer „Staples“ in vorgegebene Formen gefaltet wird, hat die Konstruktion nanoskaliger Architekturen revolutioniert. Diese Strukturen bieten großes Potenzial für Anwendungen in Materialwissenschaften und Biomedizin, etwa in der Molekulardiagnostik, Arzneimittelabgabe und beim Aufbau hybrider Nanomaterialien. Dennoch stellen die Fragilität und Anfälligkeit für Denaturierung von DNA unter physiologischen Bedingungen Herausforderungen dar, die ihre Nutzung einschränken. Diese Arbeit konzentriert sich auf die Stabilisierung und Funktionalisierung von DNA-Origami durch innovative Silifizierungsverfahren. Diese Arbeit entwickelt einen beschleunigten Silifizierungsprozess, der die Verarbeitungszeit von Tagen auf Stunden verkürzt, ohne die Integrität der Struktur zu beeinträchtigen. Eine rotationsbasierte Methode ermöglicht eine gleichmäßige Beschichtung ohne Aggregation. Diese Fortschritte eröffnen neue Möglichkeiten für eine skalierbare Produktion von DNA-Origami mit Silikabeschichtung. Zusätzlich wird untersucht, ob die funktionelle Adressierbarkeit von DNA-Origami nach der Silifizierung erhalten bleibt. Meine Studien zeigen, dass ortsspezifische Modifikationen möglich bleiben, wodurch die Anpassungsfähigkeit beibehalten wird. Dies ist entscheidend für die Integration in Biosensorsysteme. Um die Anwendbarkeit von DNA-Origami zu erweitern, wurden anpassbare Silikabeschichtungen entwickelt. Fluoreszierendes Silica ermöglicht Echtzeit-Bildgebung, während lösliches Silica eine kontrollierte Degradation einführt. Diese Innovationen schaffen Werkzeuge für reaktionsfähige Nanostrukturen. Durch die Bewältigung dieser Herausforderungen legt die Arbeit die Grundlage für die Entwicklung multifunktionaler Hybridmaterialien und positioniert DNA-Origami als Schlüsseltechnologie zukünftiger nanotechnologischer Entwicklungen

Investigations on multilateration of ionoacoustic signals for localisation of the bragg peak in pre-clinical research

Radiation therapy is one of the most typically used treatments in cancer care, with around 60% of patients undergoing this form of treatment. While X-rays and gamma rays (photon therapy) are the standard approach, proton therapy has emerged as a valuable alternative. Proton therapy is renowned for its ability to provide a more conformal dose delivery. Proton therapy’s superiority over photon therapy is due to protons depositing their maximum energy directly within the tumour while sparing surrounding healthy tissues. However, proton therapy is highly sensitive to range uncertainties. Range uncertainties in proton therapy arise primarily because we cannot precisely determine where the proton beam will stop, leading to the risk of overshooting or undershooting the target. Thus, there is a need for in vivo range verification methods to reduce range uncertainties. The two methods nearing routine clinical use are positron emission tomography (PET) and prompt gamma imaging (PGI). Range verification relies on monitoring nuclear reaction products along proton beams for these methods. However, PET and PGI methods do not directly correlate the measurable signal, beam range, or Bragg peak (BP) position. Additionally, their equipment is bulky and not cost-effective. Therefore, the research conducted during this work proposes a range verification method that is both cost-effective and establishes a direct correlation between the proton beam and ionoacoustic (IA) signals. At present, only two commercial platforms support small animal photon radiotherapy, though their imaging systems can be adapted for research beamlines. Proton therapy offers distinct advantages over photon therapy, which led to the development of the Small Animal Proton Irradiator for Research in Molecular Image-guided Radiation-Oncology (SIRMIO) project. It was led by Prof. Dr. Katia Parodi at Ludwig Maximilians-Universit¨at (LMU) Munich and funded by the European Research Council (ERC) under grant agreement 725539. SIRMIO aimed to create the first portable, imageguided research platform for small animal proton therapy. As part of this effort, different range verification methods are investigated. One of these methods is the one studied in this thesis, which is based on localising the BP using IA signals. The research presented here investigates BP localisation using IA signals, aiming to determine the BP position in both two-dimensional (2D) and three-dimensional (3D) space. The localisation was performed in homogenous and heterogenous media via time-of-flight (ToF) estimation from different sensor spatial locations. The localisation of the BP was assessed using a technique called multilateration. The initial studies were performed in-silico, using ideal point sources that emulated the BP position and evaluated the robustness of two numerical optimisation algorithms: Nelder-Mead Simplex and Levenberg Marquardt. Secondly, the robustness of the multilateration technique was assessed for two localisation methods: time-of-arrival (TOA) and time-difference-of-arrival (TDOA). By modelling random and systematic uncertainties in the geometrical ToF, the robustness of both TOA and TDOA was evaluated. Random uncertainties aimed to model the speed of sound variations, inaccurate knowledge of the sensor spatial location and errors on the ToF. On the other hand, the objective of modelling systematic uncertainties was to simulate the inaccurate knowledge of the measurement starting time from a proton beam accelerator. After fully understanding the numerical optimisation methods and the impact of uncertainties on TOA and TDOA, the localisation focus was addressed to a realistic simulation case using a pre-clinical beam with an energy of 20 MeV. The multilateration of the BP position was performed with a sensor network of 843 ideal point sensors arranged in a semi-circular configuration with a diameter of 60 mm. Similarly, the impact of different ToF extraction methods on BP localisation was evaluated. Moreover, the studies were further expanded to investigate the impact of the number of sensors on the ToF estimation and, consequently, their impact on the accuracy of the BP localisation. Experimental campaigns were conducted to benchmark the localisation of the BP using pre-knowledge gained from the simulation studies. These experimental studies retrieved the BP position in the Tandem accelerator with two different beam energies (20 and 22 MeV). The first experimental campaign aimed to localise the BP using 3 transducers. Furthermore, two different techniques were implemented to localise the spatial location of the transducers. The second experimental campaign aimed to localise the BP using 5 transducers. Moreover, the spatial locations of the transducers were estimated experimental using a single approach based on the measurement performed with an optical tracking system. For the SIRMIO case, a dedicated localisation setup with a 50 MeV beam energy was considered. This setup aimed to localise the BP under various conditions, including different proton beam time profiles, beam spatial locations, and numbers of sensors. The first step involved studying the error in ToF as a function of the proton time profiles and then assessing multilateration accuracy based on thesame proton time profiles. After identifying the optimal proton time profile, the BP was localised by keeping the proton time profile constant while varying the number of sensors. For the numerical methods, the Levenberg-Marquardt method demonstrated greater robustness compared to the Nelder-Mead Simplex method, with failure rates (FR) of 0.22% and 0% when localising the emulated BP positions with TOA and 1.12% and 4.85% when localising the source with TDOA, respectively. Considering ideal point sources, both localisation methods were equivalent in 2D. A mean error in localisation of 7.4×10^−4 mm and 7.8×10^−4 mm for TOA and TDOA was obtained. In 3D, the localisation error varied from 7.8×10^−4 mm and 1.0×10^−3 mm for TOA and TDOA. The speed of sound varies in vivo depending on the tissue type, which is expected to reduce the BP localisation accuracy. With a conservative assumption of a 5% error in the average speed of sound along the acoustic path (modelled by random uncertainties), it was observed that the localisation error after multilateration increased by around 2 mm for the examined geometry. The lowest error on the ToF estimation is obtained for the maximum-envelope extraction method when considering IA signals. Therefore, through optimal sensor positioning to minimise ToF errors, the BP could be localised in-silico with an accuracy exceeding 90 μm (equivalent to a 2% error). The BP was localised for the first experimental setup with errors ranging from 0.43 mm to 0.48 mm, depending on the sensor arrangement. The localisation was performed with a total dose of 1.69 Gy with a single shot. In the second experimental setup, the localisation was performed with 50 IA signals and a total dose of 29 Gy, achieving a localisation error of 1 mm. For both setups, the primary sources of localisation errors were inaccuracies in sensor positioning and low signal-to-noise ratio (SNR) due to the weak and directional nature of the IA emissions. The studies conducted for the SIRMIO beamline demonstrated that the proton time profile significantly impacts the ToF estimation, influencing the accuracy of BP localisation. The optimal localisation accuracy was achieved with proton time profiles ranging from 1 μs to 4 μs. In this setup, the BP was localised for different beam offsets along the x,y, and z axes. When applying offsets along the beam axis (x-axis), the maximum error was found to be 0.48 mm. Conversely, a maximum error of 1.23 mm was obtained for a transverse beam offset (z-axis). In conclusion, this work introduces a range verification method using IA signals within the framework of the SIRMIO project. Additionally, further discussions explore the potential for transitioning the studies presented in this thesis toward real-time range verification applications