Multi-scale Mining of Kinematic Distributions with Wavelets

Understand

Typical LHC analyses search for local features in kinematic distributions.

• Assumptions about anomalous patterns limit them to a relatively narrow subset of possible signals.
• Wavelets extract information from an entire distribution and decompose it at all scales, simultaneously searching for features over a wide range of scales.
• We propose a systematic wavelet analysis and show how bumps, bump-dip combinations, and oscillatory patterns are extracted.