Novelty Detection Meets Collider Physics
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Novelty detection is the machine learning task to recognize data, which belongs to a previously unknown pattern. Complementary to supervised learning, it allows the data to be analyzed in a model-independent way. We demonstrate the potential role of novelty detection in collider analyses using an artificial neural network. Particularly, we introduce a set of density-based novelty evaluators, which can measure the clustering effect of new physics events in the feature space, and hence separate themselves from the traditional density-based ones, which measure isolation. This design enables recognizing new physics events, if any, at a reasonably efficient level. For illustrating its sensitivity performance, we apply novelty detection to the searches for fermionic di-top partner and resonant di-top productions at LHC and for exotic Higgs decays of two specific modes at future e^+e^- collider.
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