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Multi-scale Mining of Kinematic Distributions with Wavelets
by Ben G. Lillard, Tilman Plehn, Alexis Romero, Tim M. P. Tait
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Benjamin Lillard · Tilman Plehn · Tim Tait |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/1906.10890v3 (pdf) |
| Date accepted: | 2020-02-14 |
| Date submitted: | 2020-02-05 01:00 |
| Submitted by: | Lillard, Benjamin |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Experimental |
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.
Author comments upon resubmission
List of changes
1. In Fig.1, Fig.4 and Fig.5 we have added the original injected signal in the second panel of each plot.
2. In Section 2.1 we have added text to clarify that the discrete signal "f_j" and the function "f(x)" represent the same distribution.
3. We have added a paragraph in Sec. 2.3. to introduce the fixed resolution global significance (FRGS) in the body of the paper.
4. In Section 3.1 on page 10 we add a paragraph describing how the fraction of wavelet coefficients to use in the signal reconstruction in Fig.4 provides primarily a qualitative description of the excess signal, and that the choice to use 3%, 5%, 10% or some other fraction does not affect the statistical analysis.
Published as SciPost Phys. 8, 043 (2020)