Masterarbeiten

Mögliche Themen für Masterarbeiten (Stand Mai 2024):

Analysis & Characterization of the Anomalous e-Burst Background in the XENONnT Dark Matter Detector
The XENONnT dark matter detector is the latest and largest dual-phase (liquid and gas) time projection chamber (TPC) in the XENON experiment series, operating at the INFN Laboratori Nazionali del Gran Sasso. XENONnT hosts 5.9 tons of liquid xenon in its target mass (8.5 tons in total) and features multiple upgrades with respect to its predecessors. The detector has been collecting data since 2021, facilitating a wide range of scientific searches, and producing world leading results.
Xenon-based particle detectors are known to observe exotic background signals in the form of significantly delayed localized bursts of electrons (e-bursts). These unique signals are thought to originate from electrons that got trapped and subsequently accumulated under the liquid xenon surface, yet, the mechanisms for their formation and release are still poorly understood. Understanding the origin of this background is of key importance for obtaining a better handle on backgrounds for low-energy scientific searches that look for particle interaction products down to the single electron (SE) and few electron level. Moreover, characterization of the processes that govern the formation of the e-burst background could shed light on the microphysics processes at the liquid-gas xenon interface, informing the operation and design of current and future generation of xenon-based dark matter detectors.
In the scope of this project you will analyze current and previously taken data from the XENONnT detector. You will devise algorithms to characterize the spatial and temporal distribution, as well as the morphology of any identified e-burst signals. Moreover, you will study the correlation of e-bursts with prior signals and detector parameters such as xenon purity and the strength of the extraction field in the TPC. You will also explore the hypothesis that this anomalous background is correlated with the spatially localized electron emission sites in the XENONnT detector, known as "hot spots". Finally, building upon the carried out analysis you would attempt to construct a model, describing the e-burst emission process.
Your work will be carried out as part of the analysis efforts of the XENON Collaboration.
Basic knowledge of particle physics, as well as solid programming skills in Python are required.
Project key topics: XENONnT data analysis, detector backgrounds phenomenology
Project timeline: June 2024 onward
Konzept der Kryodestillation für das DARWIN Experiment
Das DARWIN Experiment befindet sich in einer frühen Phase. Doch es ist bereits jetzt klar, dass bei einem Inventar von 50t Xenon und einer gewünschten Reduktion des radoninduzierten Untergrundes um einen Faktor zwei ca. 10t Xenon je Tag destilliert werden müssen. Ziel dieser Arbeit ist es, die genauen Anforderungen an die Radon- und Kryptonreinheit für das DARWIN Experiment zusammenzustellen. Basierend hierauf wird ein Konzept für eine kryogene Destillationsanlage erarbeitet. Hierbei ist es wichtig, gleichzeitig möglichst gute Trenneigenschaften bei einem hohen Durchsatz und einem möglichst geringen Inventar zu erreichen. Hierfür müssen empirische Designparameter aus Daten von Vorgängerexperimenten extrahiert werden. Am Ende soll ein konzeptionelles Design für die kryogene Destillationsanlage des DARWIN Experiments stehen, das den Zusammenhang zwischen Reduktion des Untergrundes, Xenoninventar, Durchsatz und Trennvermögen aufzeigt und damit die Entscheidungsgrundlage fürweiterführende Arbeiten bildet.
Sie sollten Spaß am Programmieren haben, Lust darauf, unbekannte Fragestellungen aktiv (mit) zu beantworten und über grundlegende Programmierkenntnisse verfügen. Themengebiete der Arbeit umfassen Astroteilchenphysik, Thermodynamik, Kryotechnik, zielorientierte Modellierung (C++, Python) und wissenschaftliche Arbeitsweise in einem Forschungsumfeld (Schreiben von Berichten, Halten von wissenschaftlichen Vorträgen und in guter wissenschaftlicher Praxis). Vorkenntnisse in diesen Gebieten sind hilfreich, aber keine Voraussetzung.
Untersuchung eines Flüssig-Xenon-Vetos für das DARWIN Experiment
Für den Erfolg des DARWIN Experiments ist die Identifikation und Unterdrückung aller möglichen Untergrundquellen essenziell. Deshalb werden mehrere Strategien der Untergrundunterdrückung angewandt: Identifikation von Elektron- bzw. Kernrückstößen über das S1/S2-Signalverhältnis, Verwendung hochreiner Materialien in der TPC-Umgebung und aktive und passive Abschirmung des Detektors (Vetozähler, Wassertank und Untergrundlabor). Aufgrund der Konstruktion der TPC innerhalb eines doppelwandigen Kryostaten ergibt sich eine dünne Außenschicht von Xenon-Flüssigkeit („liquid Xe skin“), die als ein zusätzliches Veto-System instrumentiert werden kann.
In dieser Arbeit untersuchen Sie in detaillierten Simulationen die Wirksamkeit eines solchen „liquid Xe skin veto“ mithilfe des Programmpakets GEANT4. Dabei implementieren Sie zunächst die komplexe Geometrie inklusive möglicher Lichtsensoren (PMTs) im äußeren Bereich des Kryostaten, modellieren Untergrundreaktionen in diesem Bereich wie auch in der TPC und bestimmen, inwieweit diese Untergrundreaktionen in den PMTs registriert und somit letztendlich unterdrückt werden können.
Wir erwarten die Bereitschaft zur intensiven Auseinandersetzung mit dem Thema und Freude, sich in ein modernes, spannendes und für Sie neues Wissenschaftsfeld einzuarbeiten. Grundkenntnisse der Kern- und Teilchenphysik sowie von Teilchendetektoren sind notwendig. Grundkenntnisse in der Programmiersprache C++ sind Voraussetzung, Grundkenntnisse in Python und ROOT sind hilfreich.
Simulation of Particle Induced Damage Tracks in Natural Crystals
Paleo-Detectors (PDs) represent a novel method for Dark Matter and Neutrino detection, which recently captured the interest of the Astroparticle Physics community. Dark Matter and Neutrino interactions can produce nanometer-sized damage tracks in certain natural crystals. While lying in the depths of the Earth over millions and billions of years, some crystals are expected to accumulate a large number of such tracks. These crystals can be excavated and imaged with cutting-edge nanometer-scale microscopy techniques, reading out these damage features, essentially using these minerals as PDs. With PDs we not only can probe a large range of Dark Matter particle masses, but also study Neutrinos from various astrophysical sources such as Supernovae and the Sun. Uniquely, these detectors will allow us to study the evolution of the fluxes of Dark Matter particles and astrophysical neutrinos over millions of years. Here at KIT we aim to conduct a series of key feasibility tests towards the realization of these detectors.
In the scope of this innovative project you will work at the intersection of astroparticle physics and cutting edge microscopy and imaging techniques: The simulation of the expected morphology of damage tracks produced by Dark Matter and Neutrinos in various natural crystals is of key importance for the selection of the best candidates for the role of PDs. In cooperation with geologists from Heidelberg University we have devised a list of candidate crystals for the role of PDs. In this project you would produce simulations of particle interactions in a select number of these crystals using a number of simulation software programs such as GEANT4 and SRIM. You will compare the obtained results, deriving expected sensitivity to low energy nuclear recoils for each crystal, comparing them to theoretical predictions and expectations for other minerals.
Basic knowledge of particle physics and solid programming skills are required in either Python and/or C++, as well as the willingness to explore other software frameworks.

Project key topics: Simulations, data analysis
Project timeline: from June 2024 onward
Development of Machine Learning Analysis Techniques for Imaging of Dark Matter Induced Nanometer-sized Tracks in Minerals
Paleo-Detectors (PDs) represent a novel method for Dark Matter and Neutrino detection, which recently captured the interest of the Astroparticle Physics community. Dark Matter and Neutrino interactions can produce nanometer-sized damage tracks in certain natural crystals. While lying in the depths of the Earth over millions and billions of years, some crystals are expected to accumulate a large number of such tracks. These crystals can be excavated and imaged with cutting-edge nanometer-scale microscopy techniques, reading out these damage features, essentially using these minerals as PDs. With PDs we not only can probe a large range of Dark Matter particle masses, but also study Neutrinos from various astrophysical sources such as Supernovae and the Sun. Uniquely, these detectors will allow us to study the evolution of the fluxes of Dark Matter particles and astrophysical neutrinos over millions of years. Here at KIT we aim to conduct a series of key feasibility tests towards the realization of these detectors. A key aspect of realizing the PDs concept revolves around the ability to image and analyze the nanometer-scale damage tracks produced by particle interactions in the minerals.
In this project you will work on developing machine learning-based image processing algorithms for identification and subsequent characterization of minute features in microscopy imagery. The work could be based on similar existing algorithms that were already developed in the Dark Matter group for other purposes or take an new approach to the task. The ultimate goal will be to devise robust algorithms capable of identifying tracks produced by Dark Matter and Neutrinos and discriminating them from natural damage features or tracks produced by background particles.
Strong programming skills are required in either Python and/or C++ as well as keen interest in machine learning and data processing techniques.

Project key topics: Machine learning, image processing, data analysis
Project timeline: from June 2024 onward

Für weitere Informationen wenden Sie sich gerne auch direkt an Prof. Dr. Kathrin Valerius oder Dr. Klaus Eitel.