  Despite extensive theoretical motivation for physics beyond the 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 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 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.
