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.
Accurate and robust methods for direct background estimation in resonant anomaly detection
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abstract
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R&D dataset, we demonstrate that this background template can also be repurposed to directly estimate the background expectation in a simple cut and count setup. In contrast to a traditional bump hunt, no fit to the invariant mass distribution is needed, thereby avoiding the potential problem of background sculpting. Furthermore, direct background estimation allows working with large background rejection rates, where resonant anomaly detection methods typically show their greatest improvement in significance.
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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.