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 these methods have shown remarkable performance on specific signatures such as di-jet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. We show that neural networks struggle with noisy input features and that this issue can be solved by using boosted decision trees. Overall, boosted decision trees have a superior and more predictable performance in the weakly supervised setting than neural networks. Additionally, we significantly improve the performance by using an extended set of features.
Authors
Alexander Mueck
David Shih
Gregor Kasieczka
(Hamburg University (DE))
Manuel Sommerhalder
(Hamburg University (DE))
Marie Hein
(RWTH Aachen University)
Michael Kramer
(Rheinisch Westfaelische Tech. Hoch. (DE))
Parada Prangchaikul
(Universität Hamburg (DE))
Thorben Finke
Tobias Quadfasel
