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arXiv · 1906.10890

Multi-scale Mining of Kinematic Distributions with Wavelets

Ben G. Lillard·Tilman Plehn·Alexis Romero·Tim M. P. Tait
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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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Ben G. Lillard, Tilman Plehn, Alexis Romero, Tim M. P. Tait. 2019-06-26. Multi-scale Mining of Kinematic Distributions with Wavelets. https://doi.org/10.21468/scipostphys.8.3.043

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