  To maximize the discovery potential of high-energy colliders, experimental
searches should be sensitive to unforeseen new physics scenarios. This goal has
motivated the use of machine learning for unsupervised anomaly detection. In
this paper, we introduce a new anomaly detection strategy called FORCE:
factorized observables for regressing conditional expectations. Our approach is
based on the inductive bias of factorization, which is the idea that the
physics governing different energy scales can be treated as approximately
independent. Assuming factorization holds separately for signal and background
processes, the appearance of non-trivial correlations between low- and
high-energy observables is a robust indicator of new physics. Under the most
restrictive form of factorization, a machine-learned model trained to identify
such correlations will in fact converge to the optimal new physics classifier.
We test FORCE on a benchmark anomaly detection task for the Large Hadron
Collider involving collimated sprays of particles called jets. By teasing out
correlations between the kinematics and substructure of jets, our method can
reliably extract percent-level signal fractions. This strategy for uncovering
new physics adds to the growing toolbox of anomaly detection methods for
collider physics with a complementary set of assumptions.
