Multi-scale Mining of Kinematic Distributions with Wavelets
High Energy Physics - Phenomenology
2020-03-18 v3
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.
Cite
@article{arxiv.1906.10890,
title = {Multi-scale Mining of Kinematic Distributions with Wavelets},
author = {Ben G. Lillard and Tilman Plehn and Alexis Romero and Tim M. P. Tait},
journal= {arXiv preprint arXiv:1906.10890},
year = {2020}
}
Comments
21 pages, 8 figures. KWAK package available at https://github.com/alexxromero/kwak_wavelets