Apr 15 – 18, 2019
Europe/Zurich timezone
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Novelty Detection Meets Collider Physics

Apr 16, 2019, 2:00 PM
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium


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Ms Ying-Ying Li (HKUST)


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 future e+e− collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency.

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