# Multi-scale Mining of Kinematic Distributions with Wavelets

## TL;DR

This paper introduces KWAK, a wavelet-based framework for analyzing kinematic distributions at multiple scales, enabling the detection of various features like bumps and oscillations that traditional methods might miss.

## Contribution

It presents a systematic wavelet analysis method for particle physics data, expanding the ability to identify diverse features in kinematic distributions.

## Key findings

- Successfully extracts bumps, bump-dip, and oscillatory patterns
- Provides a publicly available analysis and visualization toolkit
- Enhances detection of features across a wide range of scales

## 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.