MSc thesis topics
| Topic: | Track reconstructing using Transformers at Belle II |
| Summary: | Traditional methods for track reconstruction in High Energy Physics experiments often face scalability issues with detector occupancy. Inspired by the success of Transformer models in natural language processing, we're exploring the feasibility of training a Transformer to translate detector signals into track parameters. You will develop and train a Transformer to find tracks in the Belle II experiment using simulated and real data taking at the SuperKEKB collider in Japan. You will evaluate the feasibility and benefits of deploying the Transformer-based track reconstruction in terms of processing speed, track finding efficiency, and track parameter resolution. |
| You learn: | python-programming, machine Learning |
| Prerequisites: | good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", first experience in own software projects required), basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Lea Reuter |
| Last update: | 21.04.2024 |
| Topic: | Search for dark photons and axionlike particles in single-photon events at Belle II |
| Summary: | Belle II is a world-leading experiment in the search for dark sector candidates in the GeV range, such as dark photons or axion-like particles (ALPs). Invisibly decaying or long-lived dark photons and ALPs give rise to events with a single, monochromatic photon in the event, a unique and very challenging experimental signature. You will join our team our team to work on this search, which is currently focused on on-shell dark photons, with the aim of extending it to so-called off-shell (heavy) dark photons and ALPs. You will learn how to use tools for generating simulated datasets with Monte Carlo techniques and for statistical analysis. |
| You learn: | Python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 13.03.2024 |
| Topic: | Search for long-lived dark photons decaying into visible final states at Belle II |
| Summary: | Among all the dark sector searches, the Belle II experiment has a unique sensitivity to a weakly coupled dark photon whose decays have a displaced vertex. You will start a new search for long-lived dark photons decaying into a pair of muons, pions, kaons and, more challenging, electrons, with the aim of exploring previously unexplored parameters’ space. You will learn how to characterise the detector performance for the reconstruction of displaced vertices and use tools for statistical analysis. |
| You learn: | Python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 13.03.2024 |
| Topic: | Search for axionlike particles (ALPs) decaying into two photons at Belle II |
| Summary: | The Belle II experiment is world-leading in searches for axionlike particles (ALPs) with masses in the GeV-range. Using the growing Belle II dataset, you will prepare a new search for ALPs produced in a process called photon fusion, trying to find physics beyond the Standard Model. You will learn about statistical methods for limit setting and about methods to reduce the very large Standard Model backgrounds. If interested and with some prior experience, the use of machine learning is a possible direction to produce world-leading results in a potential following PhD thesis. |
| You learn: | Python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Prof. Dr. Torben Ferber |
| Last update: | 20.04.2024 |
| Topic: | Neuromorphic Computing with Spiking Neural Networks for Energy Efficient Waveform Analysis |
| Summary: | Waveform analysis is a technique to extract information from signals, such as amplitude and shape. It is widely used in particle physics experiments, for example in the readout of calorimeters. However, waveform analysis is computationally intensive and energy consuming, which limits its scalability and applicability. Spiking neural networks (SNNs) are a type of artificial neural networks that mimic the behavior of biological neurons, which communicate through spikes or pulses. SNNs have the potential to perform waveform analysis with high accuracy and low energy consumption, as they can exploit the temporal dynamics and sparsity of signals. The objective of this project is to investigate the feasibility and performance of using SNNs for waveform analysis in particle physics. You will evaluate and compare the accuracy and energy efficiency of SNN models with conventional methods on simulated and real data sets for CsI(Tl)-crystals similar to those used, for example, by the Belle II experiment. The results of the model evaluation will be analyzed to identify the strengths and weaknesses of SNN models for waveform analysis in particle physics. You will discuss factors that affect the accuracy and energy efficiency of SNN models will suggest possible improvements and future directions. |
| You learn: | python-programming, machine learning, sustainable computing |
| Prerequisites: | good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", first experience in own projects preferred), basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Jan Kieseler |
| Last update: | 20.04.2024 |
| Topic: | Ultrafast machine learning with quantized graph neural networks |
| Summary: | The electromagnetic calorimeter (ECL) is a subdetector of Belle II that measures the energy and time of photons and neutral hadrons, and is used to identify electrons. The ECL reconstruction is computationally intensive and time-consuming and needs specialized algorithms to be used for trigger decisions. One possible way to achieve this goal is to use QKeras, a quantization extension to Keras that provides drop-in replacement for some of the Keras layers. QKeras allows the creation of deep quantized neural networks that can be deployed on field-programmable gate arrays (FPGAs). The proposed research project aims to use QKeras to optimize the existing ECL reconstruction algorithm for Belle II and prepare the implementation on FPGAs. You will design and train deep quantized graph neural network using QKeras that can perform ECL reconstruction with high accuracy and efficiency. You will then compare the performance of the QKeras-based ECL reconstruction with the current one in terms of energy and time resolution, particle identification, and physics observables. As last step, you will evaluate the feasibility and benefits of deploying the QKeras-based ECL reconstruction on FPGAs in terms of processing speed, power consumption, and resource utilization. |
| You learn: | python-programming, machine Learning, QKeras, FPGA |
| Prerequisites: | good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", first experience in own projects preferred), basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Isabel Haide |
| Last update: | 20.04.2024 |
| Topic: | Detector studies for future beam dump experiments |
| Summary: | Light dark sector particles can be searched for at high intensity beam dump experiments like LUXE (Laser Und XFEL Experiment) at DESY, or SHiP at CERN. Axionlike particles would be detected by reconstructing its decay products: Two photons with a distinct angular and energy correlation. During the MSc thesis you will design and optimize possible detector options to measure energy, time, and direction of the decay photons using state-of-the-art simulation software. |
| You learn: | Particle physics (ALPs), GEANT4, data analysis, Python-programming, C++-programming |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung") |
| Supervisor: | Prof. Dr. Torben Ferber, Prof. Dr. Markus Klute |
| Contact: | Prof. Dr. Torben Ferber |
| Last update: | 13.03.2024 |
| Topic: | Search for B+/0 → π+/π0/ρ+/ρ0 νν decays at Belle II experiment |
| Summary: | Recently the Belle II experiment set a stringent limit on the branching fraction for the rare decay B+ → K+νν, which is b → s transition, using a novel tagging approach which relies heavily on a machine learning-based selection. The measurement of the branching fraction for this decay is of great interest as it is strongly sensitive to many NP scenarios, such as leptoquarks, axions or other dark matter candidates. In this project, you would perform the first search with Belle II data for very similar processes also using machine learning-based selection, but instead of b → s transitions, you would search for b → d transitions B+/0 → π+/π0/ρ+/ρ0 νν, where the branching fractions in the SM are further suppressed by |Vtd/Vts|^2. |
| You learn: | machine learning, python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Slavomira Stefkova |
| Last update: | 20.04.2024 |
| Topic: | Search for B+ → (ccbar → ν̄ν) K+ with Belle II |
| Summary: | In this project, search for a process of B+ → (ccbar → ν̄ν) K+ where ccbar is one of the resonant states such as J/psi which decays invisibly. In the SM this decay is mediated by Z Bosons and the branching fraction for this decay is expected to be tiny. However, beyond SM physics, such as new Z’ Bosons, could enhance the branching fraction significantly. For this search you will work on an analysis which will use so called "exclusive hadronic tagging" for the other B in the event before looking for a signal decay of interest within the rest of the event. Development of the rest of the selection will include an implementation of machine learning algorithms that will be trained in order to suppress backgrounds. |
| You learn: | machine learning, python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Slavomira Stefkova |
| Last update: | 20.04.2024 |
| Topic: | Search for B+→l nu gamma at Belle II |
| Summary: | The radiative leptonic decay B+ -> l+nu gamma yields important information for the theoretical predictions of non-leptonic B meson decays into light-meson pairs. The emission of the photon probes the first inverse moment λ_B of the light-cone distribution amplitude (LCDA) of the B meson. This parameter is a vital input to QCD factorisation schemes for the non-perturbative calculation of non-leptonic B meson decays. This project aims to observe the B+→l+nu gamma decay for the first time and set an improved limit on λ_B using modern machine learning analysis techniques. |
| You learn: | python programming, data analysis |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Pablo Goldenzweig |
| Last update: | 20.04.2024 |
| Topic: | Search for Bs→φπ0 decays at Belle |
| Summary: | The Belle experiment, which concluded in 2011, collected a unique sample of Υ(5S) decays to Bs meson pairs in the clean e+e- collision environment. This dataset has yet to be fully exploited. The rare decay Bs→φπ0 is strongly suppressed in the Standard Model and has yet to be observed. However, in a theoretical analysis motivated by the Kπ CP-puzzle, models with modified or additional Z bosons allow for an increase of the branching fraction by an order of magnitude without inconsistencies with other measurements. The Kπ CP-puzzle consists of an unexpectedly large direct CP asymmetry in the decays B± → K±π0 and B0 → K±π∓. These decays are dominated by isospin-conserving processes, but have a small contribution from isospin-violating penguin processes as well. In the isospin-violating decay Bs→φπ0 the penguin processes dominate, which means that potential NP contributions can have a much larger relative effect. If these contributions exist, an observation of the Bs→φπ0 decay may be possible with the Belle Υ(5S) dataset. |
| You learn: | python programming, data analysis |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Pablo Goldenzweig |
| Last update: | 20.04.2024 |
| Topic: | Bs→K+ l- nu with Y(5S) using machine learning-based Full Event Interpretation |
| Summary: | There are two conceptually different methods to determine the CKM matrix element Vub, a factor in the amplitude of transitions from a b-quark to a u-quark and a W boson. As quarks cannot be detected directly, one has to handle the hadronisation to infer Vub, even in the simplest case, where the W boson decays into a charged lepton and a neutrino. One method, which is easier to calculate but is experimentally more challenging, takes into account all possible final states that typically occur during the hadronisation process (inclusive measurement). The other involves calculating the hadronisation effects and searching for only a single final state, e.g., B0->pi-l+nu (exclusive measurement). Both approaches have been pursued and the result is a tantalizing 3sigma discrepancy between the measurements of the same quantity Vub. By measuring Bs->K-l+nu we can shed light on the possible reasons or sources for this, as we replace the spectator d quark in B0->pi-l+nu with an s quark. Since the calculation of the hadronisation in the exclusive measurement relies on Lattice QCD, the kaon in the final state simplifies the calculation significantly. A result close to the existing exclusive measurement would strengthen the trust in that measurement and could hint to new particles beyond the Standard Model. A result closer to the inclusive measurement would more likely be interpreted as a hint that the calculations for the exclusive measurement are unreliable and that the inclusive measurement gives the real value of Vub. This will constitute a first measurement of the rare Bs→K-l+nu decay by employing a modern machine learning-based algorithm to reconstruct the full Y(5S) event for the first time. |
| You learn: | machine learning, python programming, data analysis |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Pablo Goldenzweig |
| Last update: | 20.04.2024 |
| Topic: | GPU support for the Grid |
| Summary: | GPUs play an increasingly important role in High Energy Physics (HEP). Therefore, KIT provides GPUs accessible to various HEP collaborations via the distributed computing infrastructure, the so-called Grid. Currently, there are only initial concepts. You would contribute to implementing the GPU support in the Grid on a collaboration-wide scale. It can be complementary to the PhD or MSc thesis. |
| You learn: | Batch System HTCondor, GPU usage, Grid infrastructure |
| Prerequisites: | interest in large scale computing, |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 20.04.2024 |
| Topic: | Reinforcement Learning for Dynamic Resource Management |
| Summary: | KIT develops concepts and software to integrate opportunistic computing resources transparently. The decision of how many resources of which type has to be provided for efficient usage is complex. Reinforcement learning, where an artificial neural network learns itself, can increase resource efficiency and reduce resource specific parameter tuning. The implementation and study of reinforcement learning can complement the PhD or MSc thesis. |
| You learn: | Batch System HTCondor, bash, python, machine learning, Grid infrastructure |
| Prerequisites: | interest in large scale computing, basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 20.04.2024 |