Anomaly Detection in Presence of Irrelevant Features

Understand

Experiments at particle colliders are the primary source of insight into physics at microscopic scales.

• Searches at these facilities often rely on optimization of analyses targeting specific models of new physics.
• Increasingly, however, data-driven model-agnostic approaches based on machine learning are also being explored.
• A major challenge is that such methods can be highly sensitive to the presence of many irrelevant features in the data.