Bachelor Thesis Topics

Detection of anomalies in the SuperKEKB operation data

At the electron positron collider SuperKEKB luminosities should be reached that are more than an order of magnitude higher than at its predecessor. This is a very challenging task and often instabilities are observed that lead to the loss of beams. For a stable operation it is important to understand under which conditions this happens so that counter measures can be taken. The task of this project is to analyze the data from a large set of beam monitoring devices with machine learning methods to identify anomalous conditions.

Neural network for the classification of Y(4S) events

B factory experiments such as Belle II are well suited to measure branching fractions of B mesons because the number of Y(4S) -> B Bbar events can be determined well. However, the fractions of Y(4S) decays to pairs of neutral and charged B mesons are needed as well and their ratio may depend on the center of mass energy. For a measurement of this energy dependency a multivariate classifier should be developed that can distinguish between pairs of neutral and charged B mesons.

Exploration of AI methods for software optimization

In recent years new developments in artificial intelligence had a large impact on many areas. In this project, the potential of AI methods for software development should be evaluated. On the one hand there are tools like GitHub Copilot that assist developers. On the other hand tools like AlphaDev may help to discover new algorithms. Knowledge of C++ is required for this project.

Further topics may be available.