Multi-scale Mining of Kinematic Distributions with Wavelets
arXiv:1906.10890 · doi:10.21468/SciPostPhys.8.3.043
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.
arXiv comment21 pages, 8 figures. KWAK package available at https://github.com/alexxromero/kwak_wavelets
References in corpus (17)
- A Large Mass Hierarchy from a Small Extra Dimension
- (De)Constructing Dimensions
- Z-prime Gauge Bosons at the Tevatron
- Gauge Invariant Effective Lagrangian for Kaluza-Klein Modes
- On the Unification Problem in Physics
- Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
- Search for resonances in diphoton events at =13 TeV with the ATLAS detector
- Search for new phenomena in high-mass diphoton final states using 37 fb of proton-proton collisions collected at TeV with the ATLAS detector
- Resonance-continuum interference in the di-photon Higgs signal at the LHC
- Clockwork / Linear Dilaton: Structure and Phenomenology
- Shift in the LHC Higgs diphoton mass peak from interference with background
- Interference in the On-Shell Rate and the Higgs Boson Total Width
- Analyzing the Gamma-ray Sky with Wavelets
- Wavelet-Based Techniques for the Gamma-Ray Sky
- Tagging Boosted Ws with Wavelets
- Exploring Peaks and Valleys in the Diphoton Spectrum
- Disorder and Mimesis at Hadron Colliders