SciPost Submission Page
Multi-scale Mining of Kinematic Distributions with Wavelets
by Ben G. Lillard, Tilman Plehn, Alexis Romero, Tim M. P. Tait
Submission summary
| Authors (as registered SciPost users): | Benjamin Lillard · Tilman Plehn · Tim M. P. Tait |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/1906.10890v3 (pdf) |
| Date accepted: | Feb. 14, 2020 |
| Date submitted: | Feb. 5, 2020, 1 a.m. |
| Submitted by: | Benjamin Lillard |
| 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
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In Fig.1, Fig.4 and Fig.5 we have added the original injected signal in the second panel of each plot.
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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.
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We have added a paragraph in Sec. 2.3. to introduce the fixed resolution global significance (FRGS) in the body of the paper.
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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)