Papers › Weakly supervised anomaly detection with event-level variables
Weakly supervised anomaly detection with event-level variables
Liam Brennan, Tamas Almos Vami, Oz Amram, Sanjana Sekhar, Yuta Takahashi, Louis Moureaux, Manuel Sommerhalder, Petar Maksimovic, Tianji Cai, Nathaniel Craig
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We introduce a new topology for weakly supervised anomaly detection searches, di-object plus~X. In this topology, one looks for a resonance decaying to two standard model particles produced in association with other anomalous event activity (X). This additional activity is used for classification. We demonstrate how anomaly detection techniques which have been developed for di-jet searches focusing on jet substructure anomalies can be applied to event-level anomaly detection in this topology. To robustly capture event-level features of multi-particle kinematics, we employ new physically motivated variables derived from the geometric structure of a collision's phase space manifold. As a proof of concept, we explore the application of this approach to several benchmark signals in the di-τ and di-μ plus~X final states. We demonstrate that our anomaly detection approach can reach discovery-level significances for signals that would be missed in a conventional bump-hunt approach.
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