| Talk | |||||||||||
| Title | Novelty Detection Meets Collider Physics | ||||||||||
| Video |
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| Author(s) | Li, Ying-Ying (speaker) (HKUST) | ||||||||||
| Corporate author(s) | CERN. Geneva | ||||||||||
| Imprint | 2019-04-16. - 0:22:45. | ||||||||||
| Series | (LPCC Workshops) (3rd IML Machine Learning Workshop) | ||||||||||
| Lecture note | on 2019-04-16T14:00:00 | ||||||||||
| Subject category | LPCC Workshops | ||||||||||
| Abstract | 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. | ||||||||||
| Copyright/License | © 2019-2024 CERN | ||||||||||
| Submitted by | paul.seyfert@cern.ch | ||||||||||
Record created 2019-04-18, last modified 2022-11-02
Talk details