  Unsupervised anomaly detection could be crucial in future analyses searching
for rare phenomena in large datasets, as for example collected at the LHC. To
this end, we introduce a physics inspired variational autoencoder (VAE)
architecture which performs competitively and robustly on the LHC Olympics
Machine Learning Challenge datasets. We demonstrate how embedding some physical
observables directly into the VAE latent space, while at the same time keeping
the classifier manifestly agnostic to them, can help to identify and
characterise features in measured spectra as caused by the presence of
anomalies in a dataset.
