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Abstract
The article discusses the automation of the determination for variables of interest (so-called observables), which allow to distinguish electroweak Zγ production from the inclusive Zγ production. The preliminary version of the original automation technique was developed. The output distributions obtained with this technique can be used as the discriminants for machine learning tools to separate rare high energy physics process. An overview of performance and rejection power for the technique is presented.
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Details
1 National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe highway 31, Moscow, 115409, Russia