Multi-scale mining of kinematic distributions with wavelets

Ben G. Lillard, Tilman Plehn, Alexis Romero, Tim M. P. Tait

SciPost Phys. 8, 043 (2020) · published 17 March 2020

Abstract

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. Our kinematic wavelet analysis kit KWAK provides a publicly available framework to analyze and visualize general distributions.

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