  Recently, several normalizing flow-based deep generative models have been
proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as
an example, we show that these models can simultaneously perform unsupervised
anomaly detection with no additional training cost. As a demonstration, we
consider electromagnetic showers initiated by one (background) or multiple
(signal) photons. The CaloFlow model is designed to generate single photon
showers, but it also provides access to the shower likelihood. We use this
likelihood as an anomaly score and study the showers tagged as being unlikely.
As expected, the tagger struggles when the signal photons are nearly collinear,
but is otherwise effective. This approach is complementary to a supervised
classifier trained on only specific signal models using the same low-level
calorimeter inputs. While the supervised classifier is also highly effective at
unseen signal models, the unsupervised method is more sensitive in certain
regions and thus we expect that the ultimate performance will require a
combination of approaches.
