  This paper discusses model-agnostic searches for new physics at the Large
Hadron Collider (LHC) using anomaly-detection techniques for the identification
of event signatures that deviate from the Standard Model (SM). We investigate
anomaly detection in the context of machine-learning approaches using
autoencoders, and illustrate expected shapes of invariant masses in the outlier
region using Monte Carlo simulations. Challenges and conceptual limitations of
this approach are discussed.
