Normalizing Flows for LHC Theory

Abstract

Over the next years, measurements at the LHC and the HL-LHC will provide us with a wealth of new data. The best hope to answer fundamental questions, like the nature of dark matter, is to adopt big data techniques in simulations and analyses to extract all relevant information. On the theory side, LHC physics crucially relies on our ability to simulate events efficiently from first principles. These simulations will face unprecedented precision requirements to match the experimental accuracy. Innovative ML techniques like generative networks can help us overcome limitations from the high dimensionality of the phase space. Such networks can be employed within established simulation tools or as part of a new framework. Since neural networks can be inverted, they open new avenues in LHC analyses.