Improving Monte Carlo simulations in high energy physics using machine learning techniques

Monte Carlo event generators are nowadays indispensable tools for predictions based on first principles in high energy physics, and they represent one of the mainstays of particle physics research at the Large Hadron Collider. In the dawn of the high luminosity upgrade of the Large Hadron Collider, there is a push to more complex signatures and higher accuracy, rendering the generation of simulated events more expensive. At the same time, there are strict limitations on the computational budget. In this situation, the efficiency of event generators can be identified as a key issue. The recent rapid advancement of machine learning tools, first and foremost deep neural networks, and their successes in diverse applications make them a promising choice in addressing this challenge. In this thesis, I consider two central building blocks of event generation that represent bottlenecks in typical applications. The first is the sampling of phase space configurations such that their distribution closely approximates a given target. For this I present two new approaches, one based on normalizing flows and the other on nested sampling. The second is the unweighting of event samples, that is the generation of unit weight events that contribute equally to the total scattering cross-section. To accelerate the unweighting process, I present an unbiased unweighting method based on fast and accurate neural network surrogates for the event weights. Furthermore, I show how a surrogate optimized for the factorization properties of the corresponding matrix elements can significantly improve the performance for suitable processes. All methods are evaluated by means of examples, which are oriented towards realistic applications. It is also discussed how the different approaches could be combined and what opportunities there are for further developments.