  Using variational autoencoders trained on known physics processes, we develop
a one-sided threshold test to isolate previously unseen processes as outlier
events. Since the autoencoder training does not depend on any specific new
physics signature, the proposed procedure doesn't make specific assumptions on
the nature of new physics. An event selection based on this algorithm would be
complementary to classic LHC searches, typically based on model-dependent
hypothesis testing. Such an algorithm would deliver a list of anomalous events,
that the experimental collaborations could further scrutinize and even release
as a catalog, similarly to what is typically done in other scientific domains.
Event topologies repeating in this dataset could inspire new-physics model
building and new experimental searches. Running in the trigger system of the
LHC experiments, such an application could identify anomalous events that would
be otherwise lost, extending the scientific reach of the LHC.
