Speaker
Gregor Kasieczka
(Hamburg University (DE))
Description
As the search for new fundamental phenomena at modern particle colliders is a complex and multifaceted task dealing with high-dimensional data, it is not surprising that machine learning based techniques are quickly becoming a widely used tool for many aspects of searches. On the one hand, classical strategies are being supercharged by ever more sophisticated tagging algorithms; on the other hand, new paradigms — such as searching for anomalies in a data-driven way — are being proposed. This talk will review some key developments and consider which steps might be needed to maximise the discovery potential of particle physics experiments.
