Speaker
Lukas Lang
(RWTH Aachen University)
Description
Weakly supervised anomaly detection has been shown to be a sensitive and robust tool for Large Hadron Collider (LHC) analysis. The effectiveness of these methods relies heavily on the input features of the classifier, influencing both model coverage and the detection of low signal cross sections. In this talk, we demonstrate that improvements in both areas can be achieved by using energy flow polynomials. To further highlight this, we introduce new benchmark signals for the LHCO RnD dataset, which is a widely used benchmark dataset in this field.
Authors
Alexander Mück
(RWTH Aachen)
David Shih
Gregor Kasieczka
Lukas Lang
(RWTH Aachen University)
Marie Hein
(RWTH Aachen University)
Michael Krämer
(RWTH Aachen University)
Radha Mastandrea
(University of California, Berkeley)
Ranit Das
(Rutgers University)
