  Novelty detection is the machine learning task to recognize data, which
belong to an unknown pattern. Complementary to supervised learning, it allows
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
non-signal regions, on detection sensitivity. Strategies to address it are
proposed. The algorithms are applied to detecting fermionic di-top partner and
resonant di-top productions at LHC, and exotic Higgs decays of two specific
modes at a $e^+e^-$ future collider. With parton-level analysis, we conclude
that potentially the new-physics benchmarks can be recognized with high
efficiency.
