Novelty Detection Meets Collider Physics
This work addresses the challenge of model-independent data analysis in high-energy physics, though it is incremental as it applies existing novelty detection methods to new collider physics scenarios.
The paper tackled the problem of detecting unknown patterns in collider physics data by developing autoencoder-based novelty detection algorithms, achieving high efficiency in recognizing new-physics benchmarks like fermionic di-top partners and exotic Higgs decays.
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.