Nov 14 – 16, 2018
America/Chicago timezone

Novelty Detection Meets Collider Physics (20'+5')

Nov 16, 2018, 2:30 PM
One West (WH1W) (Fermilab)

One West (WH1W)



Tao Liu (The Hong Kong University of Science and Technology (HK))


Novelty detection is the machine learning task to recognize data belonging to an unknown pattern. Complementary to supervised learning, it allows to analyze data without a priori knowledge on signal or model-independently. In this talk, we would 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 data/signal events, for optimizing detection algorithms. We also study the generic influence of the known-pattern data fluctuations on detection sensitivity which arise from non-signal regions in the feature space (Look Elsewhere Effect). Strategies to address it are proposed. For proof of concept, the algorithms are applied to detecting signal/novel events which are defined by fermionic di-top partner and resonant di-top productions at LHC, and by exotic Higgs decays of two specific modes at future e+e- collider, respectively. With parton-level analysis, we show that the signal of new-physics benchmarks could be recognized with high efficiency.​

Primary author

Tao Liu (The Hong Kong University of Science and Technology (HK))

Presentation materials