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- W2901002004 abstract "Despite extensive theoretical motivation for physics beyond the standard model (BSM) of particle physics, searches at the Large Hadron Collider have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest $2ensuremath{sigma}$ excess into a $7ensuremath{sigma}$ excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search." @default.
- W2901002004 created "2018-11-29" @default.
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- W2901002004 date "2018-12-12" @default.
- W2901002004 modified "2023-10-15" @default.
- W2901002004 title "Anomaly Detection for Resonant New Physics with Machine Learning" @default.
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- W2901002004 doi "https://doi.org/10.1103/physrevlett.121.241803" @default.
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