Bump Hunting in Latent Space
This work addresses anomaly detection for physics experiments like the LHC, but it is incremental as it builds on existing VAE methods with domain-specific modifications.
The paper tackles the problem of unsupervised anomaly detection in large datasets, such as those from the LHC, by introducing a physics-inspired VAE architecture that performs competitively and robustly on benchmark datasets, helping to identify and characterize anomalies in measured spectra.
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.