Novelty detection meets collider physics

Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows us to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to the clustering of unknown-pattern testing data or new-physics signal events, for the design of detection algorithms. We also explore the influence of the known-pattern data fluctuations, arising from nonsignal regions, on detection sensitivity. Strategies to address it are proposed. The algorithms are applied to detecting fermionic ditop partner and resonant ditop productions at LHC, and exotic Higgs decays of two specific modes at a future e^+e^- collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency.