Anomaly Detection for Resonant New Physics with Machine Learning.
2018
Despite extensive theoretical motivation for
physics beyondthe
Standard Model(BSM) of
particle physics, searches at the
Large Hadron Collider(LHC) 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 detectiontechnique 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 spacethat are
uncorrelatedwith 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 2 sigma excess into a 7 sigma excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.
Keywords:
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Correction
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