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Custom orthogonal weight functions (COWs) for event classification
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Dembinski, Hans, Kenzie, Matthew, Langenbruch, Christoph and Schmelling, Michael (2022) Custom orthogonal weight functions (COWs) for event classification. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1040 . 167270. doi:10.1016/j.nima.2022.167270 ISSN 0168-9002.
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Official URL: https://doi.org/10.1016/j.nima.2022.167270
Abstract
A common problem in data analysis is the separation of signal and background. We revisit and generalise the so-called sWeights method, which allows one to calculate an empirical estimate of the signal density of a control variable using a fit of a mixed signal and background model to a discriminating variable. We show that sWeights are a special case of a larger class of Custom Orthogonal Weight functions (COWs), which can be applied to a more general class of problems in which the discriminating and control variables are not necessarily independent and still achieve close to optimal performance. We also investigate the properties of parameters estimated from fits of statistical models to sWeighted data and provide closed formulas for the asymptotic covariance matrix of the fitted parameters. To illustrate our findings, we discuss several practical applications of these techniques.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 0168-9002 | ||||||||
Official Date: | 1 October 2022 | ||||||||
Dates: |
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Volume: | 1040 | ||||||||
Article Number: | 167270 | ||||||||
DOI: | 10.1016/j.nima.2022.167270 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 23 August 2022 | ||||||||
Date of first compliant Open Access: | 23 August 2022 | ||||||||
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