Speaker
Marie Hein
(RWTH Aachen University)
Description
Weakly supervised methods have emerged as a powerful tool for model agnostic anomaly detection at the LHC. While remarkable performance has been achieved for specific sets of high-level input features, a further exploration of different input feature sets of various types will lead to more model agnostic and better performing setups. In this talk, we explore low-level features as well as some high-level features, including subjettiness based feature sets and energy flow polynomials.
Authors
Alexander Mück
Joep Geuskens
Lukas Lang
Marie Hein
(RWTH Aachen University)
Michael Krämer
(RWTH Aachen University)
Radha Mastandrea
