Autoencoders with latent spaces shaped like S^2, S^2×S^2, or RP^2, matched to the phase-space topology of the background, reduce spurious reconstruction errors and give a small but consistent anomaly-detection gain over flat latent spaces on simulated top-quark decays.
Novelty Detection Meets Collider Physics
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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 $e^+e^-$ future collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency.
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Enhancing anomaly detection with topology-aware autoencoders
Autoencoders with latent spaces shaped like S^2, S^2×S^2, or RP^2, matched to the phase-space topology of the background, reduce spurious reconstruction errors and give a small but consistent anomaly-detection gain over flat latent spaces on simulated top-quark decays.