Abstract
Science concerns itself with modelling the world. These models provide a lens trough which to interpret the world and with which predictions can be made for new, unseen scenarios. That does however not mean that these models are fully determined: often models have free parameters that must be set before the model can be used at all. The number of free parameters is commonly referred to as the model’s dimensionality.
The existence of free parameters in a model is not a problem in and of itself, but handling a model becomes increasingly more complicated when model’s dimensionality increases. In particle physics phenomenology this problem is so prominent, that most analyses are performed on simplified models with a lower dimensionality, sacrificing predictive power in the process.
However, modern algorithms might be able to alleviate the problems encountered when working in high-dimensional parameter spaces. Machine learning algorithms, for example, are especially well-suited to deal with high dimensionalities, making them of great interest to particle physics specifically.
To this end, this thesis covers work on the application of modern (machine learning) algorithms in particle physics. Specifically, the following four problem cases are explored: optimisation, generalisation or interpolation, exploration, and generating (or sampling) new data.
This thesis contains a pedagogical introduction into machine learning (with a focus on the applied methods and techniques), and a basic introduction into particle physics (in so far it is necessary to understand the works covered).
