Lars Stietz

Universität Hamburg
Luruper Chaussee 149 (Building 68)
22761 Hamburg
Room: 108

Normalizing Flows and Their Applications in Precision Physics and Applied Mathematics
Supervisors: Prof. Peter Schleper (UHH), Prof. Daniel Ruprecht (TUHH)

Lars obtained his Bachelor's degree in technomathematics at Hamburg University of Technology and the consecutive Master's degree at the Universität Hamburg. He wrote his Master’s thesis at the Institute of Atmospheric Physics of the German Aerospace Center (DLR) on "Artificial Neural Networks for Individual Tracking and Characterization of Wake Vortices in LiDAR Measurements" (https://elib.dlr.de/189820/).
The aim of his project is the development of deep normalizing flow networks (NFs) to obtain probability densities of stochastic data and demonstrate their usage in precision measurements and searches for new interactions in particle collisions at highest energies, as well as for inversion problems. While deep neural networks are by now routinely used in most classification problems in particle physics, their usage is still limited since the understanding of the network response in terms of likelihoods is most of the time unknown. Likelihood-based estimation however is the standard working horse for both precision measurements of fundamental parameters (e.g. top quark mass) and for searches of rare processes (e.g. triple-Higgs interactions), where only a few measured events dominate the sensitivity. The NF approach can be a game changer towards interpretable neural networks for many applications. This however requires research for multiclass applications, uncertainty estimation, and inversion properties.