TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.8.3.043
TI  - Multi-scale mining of kinematic distributions with wavelets
PY  - 2020/03/17
UR  - https://www.scipost.org/SciPostPhys.8.3.043
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 8
IS  - 3
SP  - 043
A1  - Lillard, Benjamin
AU  - Plehn, Tilman
AU  - Romero, Alexis
AU  - Tait, Tim M. P.
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.8.3.043,
	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={SciPost Phys.},
	volume={8},
	pages={043},
	year={2020},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.8.3.043},
	url={https://scipost.org/10.21468/SciPostPhys.8.3.043},
}