Anomaly Detection under Coordinate Transformations

Understand

There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC) and elsewhere.

• The first step of any anomaly detection approach is to specify observables and then use them to decide on a set of anomalous events.
• One common choice is to select events that have low probability density.
• It is a well-known fact that probability densities are not invariant under coordinate transformations, so the sensitivity can depend on the initial choice of coordinates.