  Model-independent search strategies have been increasingly proposed in recent
years because on the one hand there has been no clear signal for new physics
and on the other hand there is a lack of a highly probable and parameter-free
extension of the standard model. For these reasons, there is no simple search
target so far. In this work, we try to take a new direction and ask the
question: bearing in mind that we have a large number of new physics theories
that go beyond the Standard Model and may contain a grain of truth, can we
improve our search strategy for unknown signals by using them "in combination"?
In particular, we show that a signal hypothesis based on a large, intermingled
set of many different theoretical signal models can be a superior approach to
find an unknown BSM signal. Applied to a recent data challenge, we show that
"mixture-of-theories training" outperforms strategies that optimize signal
regions with a single BSM model as well as most unsupervised strategies.
Applications of this work include anomaly detection and the definition of
signal regions in the search for signals of new physics.
