Stellenangebote - Bachelorarbeiten

Our group is developing new pixel detectors based on the High-Voltage MAPS (monolithic active pixel detector) technology. Monolithic detectors are in many respects superior compared to standard hybrid silicon detectors. They are used for the new Mu3e experiment and considered for LHC-High Luminosity Upgrades. We offer several bachelor and master theses in this area. This project also qualifies as Projektpraktikum.

Das in Heidelberg entwickelte ³He-Atomstrahl-Spinecho-Spektrometer ermöglicht das Vermessen kleinster Energieänderungen (~100 peV) in der Wechselwirkung zwischen Atomen und Felder oder Materie. Das Experiment ist in seiner Form weltweit einzigartig und erforscht derzeit:

• Casimir Kräfte und Quantenreibung
• Nicht-Newtonische Gravitation
• Axion-Suche (Dark Matter Kandidat)
• Geometrische Phasen

Für diese spannenden und grundlegenden physikalischen Fragen können wir Hilfe gebrauchen, sowohl am Experiment, als auch bei der Theorie. Interesse mit zu machen? Dann meldet euch bei:

• P.D. Maarten DeKieviet (maarten.dekieviet@physik.uni-heidelberg.de)

The Paul Scherrer Institute (PSI, Switzerland) operates the highest intensity proton accelerator (HIPA) in the world. This facility also provides high intensity muons beams where the muons are produced in pion decays. An upgrade of the accelerator is planned for 2026 with the goal to increase the muon beam intensity by almost two orders of magnitude, thus providing 10^10 muons per second. This upgrade will dramatically increase the discvery potential for rare physics processes like mu->eee (Mu3e Phase II) and mu-> e gamma which are also called the "golden" muon decay channels. Simulation and design studies need to be performed to reduce background and optimise the sensitivity.  

Instead of having special mathematics for all the different fields of physics, Geometric Algebra (GA) supplies a unified and unifying mathematical language for the whole of physics. It not only allows for a geometric interpretation of the constituent elements, it uncovers hidden connections between the otherwise seemingly unrelated mathematical descriptions. „Why hasn‘t anyone told me that before?“ is a regularly heard, awing reaction of students being exposed to this language for the first time. We are currently searching for discrepancies with and extensions of the regular mathematical approach, both theoretically and experimentally. These are exciting times, come and join us!

Our group explores the feasibility of applying modern machine learning algorithms such as Graph Neural
Networks (GNN) deployed on FPGAs for online track reconstruction within the ATLAS online server farm
for the High-Luminosity LHC (HL-HLC) upgrade. GNNs are a powerful class of geometric deep learning
methods for modelling spatial dependencies via message passing over graphs. They are well-suited for
track reconstruction tasks by learning on an expressive structured graph representation of hit data. A
considerable speed-up over CPU-based execution is possible on FPGAs.
We can offer several topics for theses:

1. focusing on performance simulation aspects and model optimizations
2. limiting the input data to e.g. the pixel detector only and studying the performance if the GNN only delivers track seeds or even only hit triplets
3. focusing on model translation and hardware deployment
4. studying the robustness of the models with respect to detector deformations
    

Track reconstruction in high particle multiplicity environments is one of the most challenging tasks in modern particle physics experiments. Typically, a combination of complex algorithms and track fits is used to tackle this challenge. For tracking fitting, usually the Kalman filter is used. The Kalman filter, however, is an iterative algorithm and can not be parallised, which makes it very difficult to profit from modern computing architectures which are based on parallelisation.

Our group has developed a new track fit, the General Triplet Track Fit (GTTF), which can be parallelised too a large degree and be implemented on parallel architectures like Graphics Processor Units (GPUs) and Field Programmable Gate Arrays (FPGAs). We plan to use the GTTF for several applications and are looking for students to study the performance gain.  

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