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Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC

Genovese, Donatella et al.
arXiv (All) · Papers
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artificial-intelligencedata-analysishigh-energy-physics-experimenthigh-energy-physics-phenomenologystatistics-and-probability
Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic strategies for new-physics searches at the Large Hadron Collider. Searching for rare events in learned low-dimensional representations removes the need
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