  Anomaly detection with convolutional autoencoders is a popular method to
search for new physics in a model-agnostic manner. These techniques are
powerful, but they are still a "black box," since we do not know what
high-level physical observables determine how anomalous an event is. To address
this, we adapt a recently proposed technique by Faucett et al., which maps out
the physical observables learned by a neural network classifier, to the case of
anomaly detection. We propose two different strategies that use a small number
of high-level observables to mimic the decisions made by the autoencoder on
background events, one designed to directly learn the output of the
autoencoder, and the other designed to learn the difference between the
autoencoder's outputs on a pair of events. Despite the underlying differences
in their approach, we find that both strategies have similar ordering
performance as the autoencoder and independently use the same six high-level
observables. From there, we compare the performance of these networks as
anomaly detectors. We find that both strategies perform similarly to the
autoencoder across a variety of signals, giving a nontrivial demonstration that
learning to order background events transfers to ordering a variety of signal
events.
