Speaker
Description
Modern high-energy physics experiments generate massive, high-dimensional datasets that demand advanced strategies for anomaly detection. This talk presents EagleEye, a novel density-based method designed to compare two multivariate distributions on a point-by-point basis using a distribution-free approach rooted in Bernoulli and binomial statistics. EagleEye’s deterministic framework, which analyzes local neighborhoods to pinpoint deviations, can be shown to outperform established techniques on challenging domain searches such as the LHC Olympics R&D dataset in a completely unsupervised manner, without the need to specify signal regions and/or control regions, or any other weakly-supervised prescription. In this talk I will discuss the statistical properties of EagleEye, detailing how the algorithm remains computationally efficient and parallelizable, whilst explaining how the method can locate anomolous regions of over/underdensity in feature space, and even estimate the total and local signal-to-background ratio. I will also show how EagleEye can be readily adapted to a diverse range of science tasks—from new particle searches to climate data.
| AI keywords | Anomaly detection; unsupervised learning; theory |
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