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

Variational Autoencoders for New Physics Mining at the Large Hadron Collider