  Machine learning--based anomaly detection (AD) methods are promising tools
for extending the coverage of searches for physics beyond the Standard Model
(BSM). One class of AD methods that has received significant attention is
resonant anomaly detection, where the BSM is assumed to be localized in at
least one known variable. While there have been many methods proposed to
identify such a BSM signal that make use of simulated or detected data in
different ways, there has not yet been a study of the methods' complementarity.
To this end, we address two questions. First, in the absence of any signal, do
different methods pick the same events as signal-like? If not, then we can
significantly reduce the false-positive rate by comparing different methods on
the same dataset. Second, if there is a signal, are different methods fully
correlated? Even if their maximum performance is the same, since we do not know
how much signal is present, it may be beneficial to combine approaches. Using
the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative
answers to these questions. We find that there are significant gains possible
by combining multiple methods, which will strengthen the search program at the
LHC and beyond.
