Detecting New Physics as Novelty -- Complementarity Matters

Understand

Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge.

• In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders.
• In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in Ref.~\cite{Hajer:2018kqm}, between isolation-based and clustering-based novelty evaluators.
• This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events.