  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. As a further proof of principle, we subsequently
apply this scheme to the detection of two significantly different signals at
the LHC featuring a $t\bar{t}\gamma\gamma$ final state: $t\bar t h$, giving a
narrow resonance in the diphoton mass spectrum, and gravity-mediated
supersymmetry, which results in broad distributions at high transverse
momentum. Compared to existing dedicated searches at the LHC, the sensitivities
for both signals are found to be encouraging.
